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Systematic review article, how common is belief in the learning styles neuromyth, and does it matter a pragmatic systematic review.

• Swansea University Medical School, Swansea University, Swansea, United Kingdom

A commonly cited use of Learning Styles theory is to use information from self-report questionnaires to assign learners into one or more of a handful of supposed styles (e.g., Visual, Auditory, Converger) and then design teaching materials that match the supposed styles of individual students. A number of reviews, going back to 2004, have concluded that there is currently no empirical evidence that this “matching instruction” improves learning, and it could potentially cause harm. Despite this lack of evidence, survey research and media coverage suggest that belief in this use of Learning Styles theory is high amongst educators. However, it is not clear whether this is a global pattern, or whether belief in Learning Styles is declining as a result of the publicity surrounding the lack of evidence to support it. It is also not clear whether this belief translates into action. Here we undertake a systematic review of research into belief in, and use of, Learning Styles amongst educators. We identified 37 studies representing 15,405 educators from 18 countries around the world, spanning 2009 to early 2020. Self-reported belief in matching instruction to Learning Styles was high, with a weighted percentage of 89.1%, ranging from 58 to 97.6%. There was no evidence that this belief has declined in recent years, for example 95.4% of trainee (pre-service) teachers agreed that matching instruction to Learning Styles is effective. Self-reported use, or planned use, of matching instruction to Learning Styles was similarly high. There was evidence of effectiveness for educational interventions aimed at helping educators understand the lack of evidence for matching in learning styles, with self-reported belief dropping by an average of 37% following such interventions. From a pragmatic perspective, the concerning implications of these results are moderated by a number of methodological aspects of the reported studies. Most used convenience sampling with small samples and did not report critical measures of study quality. It was unclear whether participants fully understood that they were specifically being asked about the matching of instruction to Learning Styles, or whether the questions asked could be interpreted as referring to a broader interpretation of the theory. These findings suggest that the concern expressed about belief in Learning Styles may not be fully supported by current evidence, and highlight the need to undertake further research on the objective use of matching instruction to specific Learning Styles.

Introduction

For decades, educators have been advised to match their teaching to the supposed Learning styles of students ( Hyman and Rosoff, 1984 ). There are now over 70 different Learning Styles classification systems ( Coffield et al., 2004 ). They are largely questionnaire-based; students are asked to self-report their preferences for different approaches to learning and other activities and are then assigned one or more Learning Styles. The VARK classification is perhaps the most well-known ( Newton, 2015 ; Papadatou-Pastou et al., 2020 ), which categorizes individuals as one or more of Visual, Auditory, Read-Write and Kinesthetic learners. Other common Learning Styles classifications in the literature include those by Kolb, Honey and Mumford, Felder, and Dunn and Dunn ( Coffield et al., 2004 ; Newton, 2015 ).

In the mid-2000s two substantial reviews of the literature concluded that there was currently no evidence to support the idea that the matching of instructional methods to the supposed Learning Styles of individual students improved their learning ( Coffield et al., 2004 ; Pashler et al., 2008 ). Subsequent reviews have reached the same conclusion ( Cuevas, 2015 ; Aslaksen and Lorås, 2018 ) and there have been numerous, carefully controlled attempts to test this “matching” hypothesis (e.g., ( Krätzig and Arbuthnott, 2006 ; Massa and Mayer, 2006 ; Rogowsky et al., 2015 , 2020 ; Aslaksen and Lorås, 2019 ). The identification of supposed student Learning Style does not appear to influence the way in which students choose to study ( Husmann and O'Loughlin, 2018 ), and does not correlate with their stated preferences for different teaching methods ( Lopa et al., 2015 ).

Despite this lack of evidence, a number of studies suggest that many educators believe that matching instruction to Learning Style(s) is effective. One of the first studies to test this belief was undertaken in 2009 and looked at various statements about the brain and nervous system which are widespread but which are not supported by research evidence, for example the idea what we only use 10% of our brain, or that we are born with all the brain cells that we will ever have. The study described such statements as “neuromyths” and showed that belief in them was high, including belief in matching of instruction to Learning Styles which was reported by 82% of a sample of trainee teachers in the United Kingdom ( Howard-Jones et al., 2009 ). A number of similar studies have been conducted since, and have reached the same conclusion, with belief in Learning Styles reaching as high as 97.6% in a study of preservice teachers in Turkey ( Dündar and Gündüz, 2016 ).

This apparent widespread belief in an ineffective teaching method has caused concern amongst the education community. Part of the concern arises from a perception that the use of Learning Styles is actually harmful ( Pashler et al., 2008 ; Riener and Willingham, 2010 ; Dekker et al., 2012 ; Rohrer and Pashler, 2012 ; Dandy and Bendersky, 2014 ; Willingham et al., 2015 ). The proposed harms include concerns that learners will be pigeonholed or demotivated by being allocated into a Learning Style. For example, a student who is categorized as an “auditory learner” may conclude that there is no point in pursing studies, or a career, in visual subjects such as art, or written subjects such as journalism and so be demotivated during those classes. They might also conclude that they will be more successful in auditory subjects such as music, and thus inappropriately motivated by unrealistic expectations of success and become demotivated if that success does not materalise. It is worth noting however that many advocates of Learning Styles propose that it may be motivating for individual learners to know their supposed style ( Coffield et al., 2004 ). Another concern is that to try and match instruction to Learning Styles risks wasting resources and effort on an ineffective method. Educators are motivated to try and do the best for their learners, and a logical extension of the matching hypothesis is that educators would need to try and generate 4 or more versions of their teaching materials and activities, to match the different styles identified in whatever classification they have used. Additional concerns are that the continued belief in Learning Styles undermines the credibility of educators and education research, and creates unwarranted and unrealistic expectations of educators ( Newton and Miah, 2017 ). These unrealistic expectations could also manifest when students do not achieve the academic grades that they expect, or do not enjoy, or engage with, their learning; if students are not taught in a way that matches their supposed Learning Style, then they may attribute these negative experiences to a lack of matching and be further demotivated for future study. These concerns, and controversy, have also generated publicity in the media, both the mainstream media and in publications focused on educators ( Pullmann, 2017 ; Strauss, 2017 ; Brueck, 2018 ).

The apparent widespread acceptance of a technique that is not supported by evidence is made more striking by the fact that there are many teaching methods which demonstrably promote learning. Many of these methods are simple and easy to learn, for example the use of practice tests, or the spacing of instruction ( Weinstein et al., 2018 ). These methods are based upon an abundance of research which demonstrates how we learn (and how we don't), in particular the limitations of human working memory for the processing of new information in real time, and the use of strategies to account for those limitation (e.g., Young et al., 2014 ). Unfortunately these evidence-based techniques do not appear to be reflected in teacher-training textbooks ( National Council on Teacher Quality, 2016 ).

The lack of evidence to support the matching hypothesis is now acknowledged by some proponents of Learning Styles theory. For example Richard Felder states in a 2020 opinion piece

“ As the critics of learning styles correctly claim, the meshing hypothesis (matching instruction to students' learning styles maximizes learning) has no rigorous research support, but the existence and utility of learning styles does not rest on that hypothesis and most proponents of learning styles reject it .” ( Felder, 2020 )

“ I now think of learning styles simply as common patterns of student preferences for different approaches to instruction, with certain attributes - behaviors, attitudes, strengths, and weaknesses - being associated with each preference ”. ( Felder, 2020 )

This specific distinction between the matching/meshing hypothesis, and the existence of individual preferences, is at the heart of many studies which have examined belief in the matching hypothesis. Many studies ask about both preferences and matching. These are very different concepts, but the wording of the questions asked about them is very similar. Here for example is the original wording of the questions used in Howard-Jones et al. (2009) , which has been used in many studies since. Participants are asked to rate their agreement with the statements that;

“ Individuals learn better when they receive information in their preferred learning style (e.g., auditory, visual, kinesthetic) ” ( Matching question) .

and, separately ,

“ Individual learners show preferences for the mode in which they receive information (e.g., visual, auditory, kinesthetic)” ( Preferences question) .

The similarities between these statements creates a risk that participants may not fully distinguish between them. This risk is heightened by the existence of similar-sounding but distinct concepts. For example there is evidence that individuals show fairly stable differences in certain cognitive tests, e.g., of visual or verbal ability, sometimes called a “cognitive style” (e.g., Mayer and Massa, 2003 ). There is also evidence that individuals express reasonably stable preferences for the way in which they receive information, although these preferences do not appear to be correlated with abilities ( Massa and Mayer, 2006 ). This literature, and the underlying science, is complex and multi-faceted, but the nomenclature bears a resemblance to the literature on Learning Styles and the science itself may be the genesis of many Learning Styles theories ( Pashler et al., 2008 ).

This potential overlap in concepts is reflected in studies which have examined what educators understand by the term Learning Styles. A 2020 qualitative study investigated this in detail and found a range of different interpretations of the term Learning Styles. Although the VAK/VARK classification system was the most commonly recognized classification, many educators incorrectly conflated it with other theories, such as Howard Gardeners theory of Multiple Intelligences, and learning theories such as cognitivism. There was also a large diversity in the ways in which educators attempted to account for the use of Learning Styles in their teaching practice. Many educators responded by including a diversity of approaches within their teaching, but not necessarily mapped onto specific Learning Styles instrument or with instruction specific to individuals. For example using a wide variety of audiovisual modalities, or a diversity of active approaches to learning ( Papadatou-Pastou et al., 2020 ). An earlier study reported that participants incorrectly used the term “Learning Styles” interchangeably with “Universal Design for Learning,” and other strategies that take into account individual differences (differentiation) ( Ruhaak and Cook, 2018 ). This complexity is reflected in teacher-training textbooks, which commonly refer to Learning Styles but in a variety of ways, including student motivation and preferences for learning ( Wininger et al., 2019 ). There is also a related misunderstanding about Learning Styles theory; the absence of evidence for a matching hypothesis does not mean that students should all be taught the same way, or that they do not have preferences for how they learn. Attempts to refute the matching hypothesis have been incorrectly interpreted in this way ( Newton and Miah, 2017 ).

Thus, one interpretation of the current literature and surrounding media is that, concern has arisen due to widespread belief in the efficacy of an ineffective and potentially harmful teaching technique, but the participants in studies which report on this widespread belief do not clearly understand what they are being asked, or what the intended consequences are if they disagree with what they are asked.

One set of questions to be addressed in this review then is whether the aforementioned concern is fully justified, and whether this potential confusion is reflected in the data. We examine this by using a systematic review approach to take a broader look at trends and patterns in a larger dataset. The evidence showing a lack of evidence for matching instruction to Learning Styles has been available since 2004. It would be reasonable then to expect that belief in this method would have declined since then, particularly if it is harmful. A related question is whether educators actually use Learning Styles; to generate multiple versions of teaching materials and activities would require considerable additional effort for no apparent benefit, which should also hasten the decline of Learning Styles.

With this is mind, we have conducted a Pragmatic Systematic Review. Pragmatism is an approach to research that attempts to identify results that are useful, relevant to practical issues in the real-world, rather than focusing solely on academic questions ( Duram, 2010 ; Feilzer, 2010 ). Pragmatic Evidence-based Education is an approach which combines the most useful education research evidence and relies on judgement to apply it in specific context ( Newton et al., accepted ). Thus, here we have designed research questions to help us develop and discuss findings which are, we hope, useful to the sector rather than solely of academic interest. In addition, we have included many of the usual measures of study quality associated with a systematic review. However, these are included as results as in themselves, rather than as reasons to include/exclude studies from the review. A detailed picture of the quality of studies should be useful for the sector to determine whether the findings justify the aforementioned concern, and whether it needs to be addressed.

Research Questions

1. What percentage of educators believe in the matching of instruction to Learning Styles?

2. What percentage of educators enact, or plan to enact the matching of instruction to Learning Styles?

3. Has belief in matching instruction to Learning Styles decreased over time?

4. Do evidence-based interventions reduce belief in matching instruction to Learning Styles?

5. Do studies present clear evidence that participants understand the difference between (a) matching instruction to Learning Styles and (b) preferences exhibited by learners for the ways in which they receive information?

The review followed the PRISMA guidelines for conducting and reporting a Systematic Review ( Moher et al., 2009 ), with a consideration of measures of quality and reporting for survey-based research, taken from ( Kelley et al., 2003 ; Bennett et al., 2011 ).

Eligibility Criteria, Information Sources, and Search Strategy

Education research is often published in journals that are outside the immediate field of education, but instead are linked to the subject being learned. Therefore, we used EBSCO to search the following databases: CINAHL Plus with Full Text; eBook Collection (EBSCOhost); Library, Information Science & Technology Abstracts; MEDLINE; APA PsycArticles; APA PsycINFO; Regional Business News; SPORTDiscus with Full Text; Teacher Reference Center; MathSciNet via EBSCOhost; MLA Directory of Periodicals; MLA International Bibliography. We also searched PubMed and the Education Research database ERIC.

The following search terms were used: “belief in learning styles”; “believe in learning styles”; “believed in learning styles”; “Individuals learn better when they receive information in their preferred learning style” (this is the survey question used in the original Howard-Jones paper ( Howard-Jones et al., 2009 ). Neuromyth * ; “learning styles” AND myth AND survey or questionnaire. We used advanced search settings for all sources to apply related words and to ensure that the searches looked for the terms within the full text of the articles. No date restriction was applied to the searches and so the results included items up to and including April 2020.

This returned 1,153 items. Exclusion of duplicates left 838 items. These were then screened according to the inclusion criteria (below). Screening articles on the basis of their titles identified 85 eligible items. The abstracts of these were then evaluated which resulted in 46 items for full-text screening. We also used Google Scholar to search for the same terms. Google Scholar provides better inclusion of non-journal research including of gray literature ( Haddaway et al., 2015 ) and unpublished theses that are hosted on servers outside the normal databases ( Jamali and Nabavi, 2015 ). For example, when searching for the specific survey item used in the original Howard-Jones paper ( Howard-Jones et al., 2009 ) and in many studies subsequently; “Individuals learn better when they receive information in their preferred learning style.” This search returned zero results on ERIC and four result on PsychINFO, but returned 107 results on Google Scholar, most of which were relevant. However, all Google Scholar results had to hand screened in real-time since Google Scholar does not have the same functionality as the databases described above; it includes multiple versions of the same papers, and the search interface is limited, making it difficult to accurately quantify and report search results ( Boeker et al., 2013 ).

Study Selection

To be included in the review a study had to meet the following criteria;

• Survey educators about their belief in the matching of instruction to one or more of the Learning Styles classifications identified in aforementioned reviews ( Coffield et al., 2004 ; Pashler et al., 2008 ) and/or educators use of that matching in their teaching. This included pre-service or trainee teachers (individuals studying toward a teaching qualification).

• Report sufficient data to allow calculation of the number and percentage of respondents stating a belief that individuals learn better when they receive information in their preferred learning style (or use/plan to use Learning Styles theory in this way).

Exclusion criteria included the following

• Surveys of participant groups that were not educators or trainee educators.

• Only survey belief in individual learning preferences (i.e., rather than matching instruction).

• Survey other opinions about Learning Styles, for example whether they explain differences in academic abilities (e.g., Bellert and Graham, 2013 ).

• Survey belief in personalizing learning to suit preferences or other characteristics not included in the Learning Styles literature (e.g., prior educational achievement, “deep, surface or strategic learners.”

Some studies were not explicitly clear that they surveyed belief in matching instruction, but used related non-specific concepts such as the “existence of Learning Styles.” These were excluded unless additional information was available to confirm that the studies specifically surveyed belief in matching instruction to Learning Styles. For example ( Grospietsch and Mayer, 2018 ) reported surveying belief in the existence of Learning Styles. However, the content of this paper discussed knowledge acquisition in the context of matching, and stated that the research instruments was derived from Dekker et al. (2012) , and had been used in an additional paper by the same authors ( Grospietsch and Mayer, 2019 ), while a follow-up paper from the same authors described both these earlier papers as surveying belief in matching instruction to Learning Styles ( Grospietsch and Mayer, 2020 ). These two survey studies were therefore included. Another study ( Canbulat and Kiriktas, 2017 ) was not clear and no additional information was available. Two emails were sent to the corresponding author with a request for clarity, but no response was received.

Application of the inclusion criteria resulted in 33 studies being included, containing a total of 37 samples. We then went back to Google Scholar to search within those articles which cited the 33 included studies. No further studies were identified which met the inclusion criteria.

Data Collection Process

Data were independently extracted from every paper by two authors working separately (PN + AS). Extracted data were then compared and any discrepancies resolved through discussion.

The following metrics were collected where available (all data are shown in Appendix 1 ):

• The year the study was published

• Year that data were collected (where stated, and if different from publication date. If a range was stated, then the year which occupied the majority of the range was taken (e.g., Aug 2014–April 2015 was recorded as 2014).

• Country where the research was undertaken

• Publication type (peer reviewed journal, thesis, gray literature)

• Population type (e.g., academics in HE, teachers, etc.)

• Whether or not funding was received and if so where from

• Whether or not a Conflict of Interest was reported/detected

• Target population size

• Sample size

• “N” (completed returns)

• Average teaching experience of participant group

• Percentage and number of participants who stated agreement with a question regarding belief in the matching of instruction to Learning Styles, and the text of the specific question asked

• Percentage and number of participants who stated agreement with a question regarding belief that learners express preferences for how they receive information, and the text of the specific question asked

• The percentage and number of participants who stated that they did, or would, use matching to instruction in their teaching, and the text of the specific question asked

• The percentage and number of participants who stated agreement with a question regarding belief in the matching of instruction to Learning Styles after any intervention aimed at helping participants understand the lack of evidence for matching instruction to Learning Styles

Summary Measures and Synthesis of Results

Most measures are simple percentages of participants who agreed, or not, with questionnaire statements. Summary measures are then the average of these. In order to account for unequal sample size, simple weighted percentages were calculated; percentages were converted to raw numbers using the stated “N” for an individual sample. The sum of these raw numbers from each study was then divided by the sum of “N” from each study and converted to a percentage. Percentages from individual studies were used as individual data points in groups for subsequent statistical analysis, for example to compare the percentage of participants who believed in matching instruction to the percentage who actually used Learning Styles in this way.

Risk of Bias Within and Across Studies

Bias is defined as anything which leads a review to “over-estimate or under-estimate the true intervention effect” ( Boutron et al., 2019 ). In this case an “intervention effect” would be belief in, or use of, Learning Styles either before or after any intervention, or belief in a preference for receiving information in different ways.

Many concerns regarding bias are unlikely to apply here. For example, publication bias, wherein results are less likely to be reported if they are not statistically significant. Most of the data reported in the studies under consideration here are not subject to tests of significance, so this is less of a concern.

However, a number of other factors affect can generate bias within a questionnaire-type study of the type analyzed here. These factors also affect the external validity of study findings, i.e., how likely is it that study findings can be generalized to other populations. We collected the following information from each study in order to assess the external validity of the studies. These metrics were derived from multiple sources ( Kelley et al., 2003 ; Bennett et al., 2011 ; Boutron et al., 2019 ). Some were calculated from the objective data described above, whereas others were subject to judgement by the authors. In the latter case, each author made an independent judgement and then any queries were resolved through discussion.

• Sampling Method . Each study was classified into one of the following categories. Categories are drawn from the literature ( Kelley et al., 2003 ) and the studies themselves.

◦ Convenience sampling. The survey was distributed to all individuals within a specified population, and data were analyzed from those individuals who voluntarily completed the survey.

◦ Snowball sampling. Participants from a convenience sample were asked to then invite further participants to complete the survey.

◦ Unclassifiable . Insufficient information was provided to allow determination of the sampling method

◦ (no other sampling approaches were used by the included studies)

• Validity Measures

◦ Neutral Invitation . Were participants invited to the study using neutral language. Neutrality in this case was defined as not demonstrating support for, or criticism of, Learning Styles in a way that could influence the response of a participant. An example of a neutral invitation is Dekker et al. (2012) “ The research was presented as a study of how teachers think about the brain and its influence on learning. The term neuromyth was not mentioned in the information for teachers.”

◦ Learning Styles vs. styles of learning . Was sufficient information made available to participants for them to be clear that they were being asked about Learning Styles rather than styles of learning, or preferences ( Papadatou-Pastou et al., 2020 ). For example, was it explained that, in order to identify a Learning Style, a questionnaire needs to be administered which then results in learners being allocated to one or more styles, with named examples (e.g., Newton and Miah, 2017 ).

◦ Matching Instruction . If yes to above, was it also made clear that, according to the matching hypothesis, educators are supposed to tailor instruction to individual learning styles.

Additional Analyses

The following additional analyses were pre-specified in line with our initial research questions.

Has Belief in Matching Instruction to Learning Styles Decreased Over Time?

The lack of evidence to support matching instruction to Learning Styles has been established since the mid-2000s and has been the subject of substantial publicity. We might therefore hypothesize that belief in matching instruction has decreased over time, for example due to the effects of the publicity, and/or from a revision of teacher-training programmes to reflect this evidence. Three different analyses were conducted to test for evidence of a decrease.

1. A Spearman Rank Correlation test was conducted to test for a correlation between the year that the study was undertaken and the percentage of participants who reported a belief in matching instruction to learning styles. A significant negative correlation would indicate a decrease over time.

2. Belief in matching instruction to Learning Styles was compared in trainee teachers vs. practicing teachers. If belief in Learning Styles was declining then we would expect to see lower rates of belief in trainee teachers. Two samples ( Tardif et al., 2015 ; van Dijk and Lane, 2018 ) contained a mix of trainee and qualified teachers and were excluded from this analysis. The samples of teachers in Dekker et al. (2012) and Macdonald et al. (2017) both contained 94% practicing teachers and 6% trainee teachers, and so the samples were counted as practicing teachers for the purpose of this analysis.

3. A Spearman Rank Correlation test was conducted to test for a correlation between the average teaching experience of study participants and the percentage of participants who reported a belief in matching instruction to Learning Styles. If belief in matching instruction to learning styles is decreasing then we might expect to see a negative correlation.

Is There a Difference Between Belief in Learning Styles and Use of Learning Styles

The weighted percentage for each of these was calculated, and the two groups of responses were also compared.

Question Validity Analysis

In many of the studies here, participants were asked about both “preferences for learning” and “matching instruction to Learning Styles.” As described in the introduction, the wording for both questions was similar. If there was confusion about the difference between these two statements, then we would expect the pattern of response to them to be broadly similar. To test for this, we calculated a difference score for each study by subtracting the percentage of participants who believed in matching instruction to Learning Styles from the percentage who agreed that individuals have preferences for how they learn. We then conducted a one-tailed t -test to determine whether the distribution of these scores was significantly different from zero. We also compared both groups of responses.

All datasets were checked for normal distribution before analysis using a Kolmogorov-Smirnov test. Non-parametric tests were used where datasets failed this test. Individual tests are described in the results section.

89.1% of Participants Believe in Matching Instruction to Learning Styles

34/37 samples reported the percentage of participants who stated agreement with an incorrect statement that individuals learn better when they receive information in their preferred learning style. The simple average of these 34 data points is 86.2%. To calculate a weighted percentage, these percentages were converted to raw numbers using the stated “N.” The sum of these raw numbers was then divided by the sum of “N” from the 34 samples to create a percentage. This calculation returned a figure of 89.1%. A distribution of the individual studies is shown in Figure 1 .

Figure 1 . The percentage of participants who stated agreement that individuals learn better when they receive information in their preferred Learning Style. Individual studies are shown with the name of the first author and the year the study was undertaken. Data are plotted as ±95% CI. Bubble size is proportional to the Log10 of the sample size.

No Evidence of a Decrease in Belief Over Time

As described in the methods we undertook three separate analyses to test for evidence that belief in Learning Styles has decreased over time. (1) A Spearman Rank correlation analysis was conducted to test for a relationship between the year a study was conducted and the percentage who reported that they believed in matching instruction to Learning Styles. No significant relationship was found ( r = −0.290, P = 0.102). (2) Belief in matching instruction to Learning Styles was compared in samples of qualified teachers ( N = 16) vs. pre-service teachers ( N = 12) using a Mann-Whitney U test. No significant difference was found ( Figure 2 ). A Mann Whitney U test returned a P value of 0.529 (U = 82). When calculating the weighted percentage from each group, belief in matching was 95.4% for pre-service teachers and 87.8% for qualified teachers. The weighted percentage for participants from Higher Education was 63.6%, although this was not analyzed statistically since these data were calculated from only three studies and these were different to the others in additional ways (see Discussion). (3) A Spearman Rank correlation analysis was conducted to test for a relationship between the mean years of experience reported by a participant group (qualified teachers) and the percentage who reported that the believed in matching instruction to Learning Styles. No significant relationship was found ( r = −0.158, P = 0.642).

Figure 2 . No difference between the percentage of Qualified Teachers vs. Pre-Service Teachers who believe in the efficacy of matching instruction to Learning Styles. The percentage of educators who agreed with each statement was compared by Mann-Whitney U test. P = 0.529.

Effect of Interventions

Four studies utilized some form of training for participants, to explain the lack of current evidence for matching instruction to Learning Styles. A pre-post test analysis was used in these studies to evaluate participants belief in the efficacy of matching instruction to Learning Styles both before and after the training. Calculating a weighted percentage revealed that, in these four studies, belief went from 78.4 to 37.1%. The effect size for this intervention effect was large (Cohens d = 3.6). Comparing these four studies using a paired t -test revealed that the difference between pre and post was significant ( P = 0.012). Results from the individual studies are shown in Figure 3 .

Figure 3 . Interventions which explain the lack of evidence to support the efficacy of matching instruction to Learning Styles are associated with a drop in the percentage of participants who report agreeing that matching is effective. Each of the four studies used a pre-post design to measure self-reported belief. The weighted percentage dropped from 78.4 to 37.1%.

Use of Learning Styles vs. Belief

Seven studies measured self-report of use, or planned use, of matching instruction to Learning Styles. Calculating the weighted average revealed that 79.7% of participants said they used, or intended to use, the matching of instruction to Learning Styles. This was compared to the percentage who reported that they believed in the efficacy of matching instruction. A Mann-Whitney U test was used since four of the seven studies did not measure belief in matching to instruction and so a paired test was not possible. No significant difference was found between the percentage of participants who reported believing that matching instruction to Learning Styles is effective (89.1%), and the percentage who used, or planned to use, it as a teaching method (79.7%) ( P = 0.146, U = 76.5). Data are shown in Figure 4 .

Figure 4 . No difference between the percentage of participants who report believing in the efficacy of matching instruction to Learning Styles, and the percentage who used, or intended to use, Learning Styles in this way. The pooled weighted percentage was 89.1 vs. 79.7%. P = 0.146 by Mann-Whitney U test.

No Difference in Belief in Preferences vs. Belief in Matching Instruction to Learning Styles

As described in the introduction, many studies compared belief in matching instruction to Learning Styles (a “neuromyth”) with a correct statement that individuals show preferences for the mode in which they receive information. Twenty-one studies questioned participants on both their belief in matching instruction to Learning Styles, and their belief that individual learners have preferences for the ways in which they receive information. A Wilcoxon matched-pairs test showed no significant difference between these two datasets ( P = 0.262, W = 57). A difference score was calculated by subtracting the percentage who believe in matching instruction from the percentage who believe that learners show preferences. The mean of these scores was 2.66, with a Standard Deviation of 8.97. A one sample t -test showed that the distribution of these scores was not significantly different from zero ( P = 0.189). The distribution of these scores is shown in Figure 5 and reveals many negative scores, i.e., where belief in matching instruction to Learning Styles is higher than a belief that individuals have preferences for how they receive information.

Figure 5 . No difference between belief in Learning Styles and Learning Preferences. (A) The percentage of participants who report believing that individuals have preferences for how the receive information, and the percentage who report believing that individuals learn better when receiving information in their preferred Learning Style. (B) The difference between these two measures, calculated for individual samples. A negative score means that fewer participants believed that students have preferences for how they received information compared to the percentage who believed that matching instruction to Learning Styles is effective.

Risk of Bias and Validity Measures

A summary table of the individual studies is shown in Table 1 . (The full dataset is available in Appendix 1 ).

Table 1 . Characteristics of included studies.

Of the 34 samples which measured belief in matching instruction to Learning Styles, 30 of them used the same question as used in Howard-Jones et al. (2009 ) (see Introduction). The four which used different questions were “Does Teaching to a Student's Learning Style Enhance Learning?” ( Dandy and Bendersky, 2014 ), “Students learn best when taught in a manner consistent with their learning styles” ( Kilpatrick, 2012 ), “How much do you agree with the thesis that there are different learning styles (e.g., auditory, visual or kinesthetic) that enable more effective learning?” ( Menz et al., 2020 ) and “A pedagogical approach based on such a distinction favors learning” (participants had been previously been asked to rate their agreement with the statement “Some individuals are visual, others are auditory”) ( Tardif et al., 2015 ).

Thirty of the 37 samples included used convenience sampling. Three of the studies used snowballing from convenience sampling, while the remaining 4 were unclassifiable; these were all from one study whose participants were recruited “ at various events related to education (e.g., book fair, pedagogy training sessions, etc.), by word of mouth, and via email invitations to databases of people who had previously enquired about information/courses on neuroscience and education” ( Gleichgerrcht et al., 2015 ). Thus, no studies used a rigorous, representative, random sample and so no further analysis was undertaken on the basis of sampling method. Some studies considered representativeness in their methodology, for example Dekker et al. (2012) reported that the local schools they approached “ could be considered a random selection of schools in the UK and NL” but the participants were then “ Teachers who were interested in this topic and chose to participate.” No information is given about the size of the population or the number of individuals to whom the survey was sent, and no demographic characteristics are given regarding the population.

Response Rate

Only five samples reported the size of the population from which the sample was drawn, and so no meaningful analysis of response rate can be drawn across the 37 samples. In one case ( Betts et al., 2019 ) the inability to calculate a response rate was due to our design rather than the study from which the data were extracted; Betts et al. (2019) reported distributing their survey to a Listserv of 65,780, but the respondents included many non-educators whose data were not relevant for our research question. It is perhaps worth noting however that their total final participant number was 929 and so their total response rate across all participant groups was 1.4%

Neutral Invitation

Nine of the 37 studies presented evidence of using a neutral invitation. None of the remaining studies provided evidence of a biased invitation; the information was simply not provided.

Briefing on Learning Styles and Matching

Two of the 37 studies reported giving participants additional information regarding Learning Styles, sufficient (in our view) for participants to be clear that they were being asked specifically about Learning Styles as defined by Coffield et al., and the matching on instruction to Learning Styles.

We find that 89.1% of 15,045 educators, surveyed from 2009 through to early 2020, self-reported a belief that individuals learn better when they receive information in their preferred Learning Style. In every study analyzed, the majority of educators reported believing in the efficacy of this matching, reaching as high as 97.6% in one study by Dundar and colleagues, which was also the largest study in our analysis, accounting for 19% of the total sample ( Dündar and Gündüz, 2016 ).

Perhaps the most concerning finding from our analysis is that there is no evidence that this belief is decreasing, despite research going back to 2004 which demonstrates that such an approach is ineffective and potentially harmful. We conducted three separate analyses to test for evidence of a decline but found none, in fact the total percentage of pre-service teachers who believe in Learning Styles (95.4%) was higher than the percentage of qualified teachers (87.8%). This finding suggests that belief in matching instruction to Learning Styles is acquired before, or during, teacher training. Tentative evidence in support for this is a preliminary indication that belief in Learning Styles may be lower in educators from Higher Education, where teacher training is less formal and not always compulsory. In addition, Van Dijk and Lane report that overall belief in neuromyths is lower in HE although they do not report this breakdown for their data on Learning Styles ( van Dijk and Lane, 2018 ). However, the studies from Higher Education are small, and two of them are also studies where more information is provided to participants about Learning Styles (see below).

From our pragmatic perspective, there are a number of issues to consider when determining whether these findings should be a cause for alarm, and what to do about them.

The data analyzed here are mostly extracted from studies which assess teacher belief in a range of so-called neuromyths. These all use some version of the questionnaire developed by Howard-Jones and co-workers ( Howard-Jones et al., 2009 ). The value of surveying belief in neuromyths has been questioned, on the basis that, in a small sample of award-winning teachers, there did not appear to be any correlation between belief in neuromyths and receiving a teaching award ( Horvath et al., 2018 ). The Horvath study ultimately proposed that awareness of neuromyths is “irrelevant” to determining teacher effectiveness and played down concerns, expressed elsewhere in the field, that belief in neuromyths might be harmful to learners, or undermine the effectiveness of educators. We have only analyzed one element of the neuromyths questionnaire (Learning Styles), but we share some of the concerns expressed by Horvath and co-workers. The majority (30/34) of the samples analyzed here measured belief in Learning Styles using the original Howard-Jones/Dekker questionnaire. A benefit of having the same questions asked across multiple studies is that there is consistency in what is being measured. However, a problem is that any limitations with that instrument are amplified within the synthesis here. One potential limitation with the Howard-Jones question set is that the “matching” question is asked in many of the same surveys as a “belief” question, as shown in the introduction, potentially leading participants to conflate or confuse the two. Any issues may then be exacerbated by a lack of consistency in what participants understand by “matching instruction to Learning Styles”; this could affect all studies. The potential for multiple interpretations of these questions regarding Learning Styles is acknowledged by some authors (e.g., Morehead et al., 2016 ), and some studies report a lack of clarity regarding the specific meaning of Learning Styles and the matching hypothesis ( Ruhaak and Cook, 2018 ; Papadatou-Pastou et al., 2020 ). This lack of clarity is reflected also in the psychometric properties of Learning Styles instruments themselves, with many failing to meet basic standards of reliability and validity required for psychometric validation ( Coffield et al., 2004 ). In addition, we have previously founds that participants, when advised against matching instruction to Learning Styles, may conclude that this means educators should eliminate any consideration of individual preferences or variety in teaching methods ( Newton and Miah, 2017 ).

Here we found no significant differences between participant responses to the question regarding belief in matching instruction vs. the question about individual preferences, with almost half the studies analyzed actually reporting a higher percentage of participants who believed in matching instruction when compared to belief that individuals have preferences for how they receive information. This is concerning from a basic methodological perspective. The question is normally thus; “ Individual learners show preferences for the mode in which they receive information (e.g., visual, auditory, kinesthetic) .” In any sample of learners, some individuals are going to express preferences. It may not be all learners, and those preferences may not be stable for all learners, and the question does not encompass all preferences, but the question, as asked, cannot be anything other than true.

More relevant for our research questions is the apparent evidence of a lack of clarity within the research instrument; it may not be clear to study participants what the matching hypothesis is and so it is difficult to conclude that the results truly represent belief in matching instruction to Learning Styles. This finding is tentatively supported by our analysis which shows that, in the two studies which give participants additional instructions and guidance to help them understand the matching hypothesis, belief in matching instruction to Learning Styles is much lower, a weighted average of 63.5% ( Dandy and Bendersky, 2014 ; Newton and Miah, 2017 ). However, these are both small studies, and both are conducted in Higher Education rather than school teaching, so the difference may be explained by other factors, for example the amount and nature of teacher-training given to educators in Higher Education when compared to school-teaching. It would be informative to conduct further studies in which more detail was provided to participants about Learning Styles, before they are asked whether or not they believed matching instruction to Learning Styles is effective.

However, even if we conclude that the findings represent, in part, a lack of clarity over the specific meaning of “matching instruction to Learning Styles,” this might itself still be a cause for concern. The theory is very common in teacher training and academic literature ( Newton, 2015 ; National Council on Teacher Quality, 2016 ; Wininger et al., 2019 ) and so we might hope that the meaning and use of it is clear to a majority of educators. An additional potential limitation is that the Howard-Jones question cites VARK as an example of Learning Styles, when there are over 70 different classifications. Thus we have almost no information about belief in other common classifications, such as those devised by Kolb, Honey and Mumford, Dunn and Dunn etc. ( Coffield et al., 2004 ).

79.7% of participants reported that they used, or planned to use, the approach of matching instruction to Learning Styles. This high percentage was surprising since our earlier work ( Newton and Miah, 2017 ) showed that only 33% of participants had used Learning Styles in the previous year. If Learning Styles are ineffective, wasteful of resources and even harmful, then we might predict that far fewer educators would actually use them. There are a number of caveats to the current results. There are only seven studies which report on this and all are small, accounting for <10% of the total sample. Most are not paired, i.e., they do not explicitly ask about belief in the efficacy of Learning Styles and then compare it to use of Learning Styles. The questions are often vague, broad and do not specifically represent an example of matching instruction to individual student Learning Styles as organized into one of the recognized classifications. For example “do you teach to accommodate those differences” (Learning Styles). Agreement with statements like these might reflect a belief that educators feel like they have to say they use them in order to respect any/all individual differences, rather than Learning Styles specifically. In addition this is still a self-report of a behavior, or planned behavior. It would be useful, in further work, to measure actual behavior; how many educators have actually designed distinct versions of educational resources, aligned to multiple specific individual student Learning Styles? This would appear to be a critical question when determining the impact of the Learning Styles neuromyth.

The studies give us little insight into why belief in Learning Styles persists. The theory is consistently promoted in teacher-training textbooks ( National Council on Teacher Quality, 2016 ) although there is some evidence that this is in decline ( Wininger et al., 2019 ). If educators are themselves screened using Learning Styles instruments as students at school, then it seems reasonable that they would then enter teacher-training with a view that the use of Learning Styles is a good thing, and so the cycle of belief would be self-perpetuating.

We have previously shown that the research literature generally paints a positive picture of the use of Learning Styles; a majority of papers which are “about” Learning Styles have been undertaken on the basis that matching instruction to Learning Styles is a good thing to do, regardless of the evidence ( Newton, 2015 ). Thus an educator who was unaware of, or skeptical of, the evidence might be influenced by this. Other areas of the literature reflect this idea. A 2005 meta-analysis published in the Journal of Educational Research attempted to test the effect of matching instruction to the Dunn and Dunn Learning Styles Model. The results were supposedly clear;

“ results overwhelmingly supported the position that matching students' learning-style preferences with complementary instruction improved academic achievement ” ( Lovelace, 2005 ).

A subsequent publication in the same journal in 2007 ( Kavale and LeFever, 2007 ) discredited the 2005 meta-analysis. A number of technical and conceptual problems were identified with the 2005 meta-analysis, including a concern that the vast majority of the included studies were dissertations supervised by Dunn and Dunn themselves, undertaken at the St. John's University Center for the Study of Learning and Teaching Styles, run by Dunn and Dunn. At the time of writing (August 2020), the 2005 meta-analysis has been cited 292 times according to Google Scholar, whereas the rebuttal has been cited 38 times. A similar pattern played out a decade earlier, when an earlier meta-analysis by R Dunn, claiming to validate the Dunn and Dunn Learning Styles model, was published in 1995 ( Dunn et al., 1995 ). This meta-analysis has been cited 610 times, whereas a rebuttal in 1998 ( Kavale et al., 1998 ), has been cited 60 times.

An early attempt by Dunn and Dunn to promote the use of their Learning Styles classification was made on the basis that teachers would be less likely to be the subject of malpractice lawsuits if they could demonstrate that they had made every effort to identify the learning styles of their students ( Dunn et al., 1977 ). This is perhaps an extreme example, but reflective of a general sense that, by identifying a supposed learning style, educators may feel they are doing something useful to help their students.

A particular issue to consider from a pragmatic perspective is that of study quality. Many of the studies did not include key indicators of the quality of survey responses ( Kelley et al., 2003 ; Bennett et al., 2011 ). For example, none of the studies use a defined, representative sample, and very few include sufficient information to allow the calculation of a response rate. From a traditional research perspective, the absence of these indicators undermines confidence in the generalizability of the findings reported here. Pragmatic research defines itself as identifying useful answers to research questions ( Newton et al., accepted ). From this perspective then, we considered it useful to still proceed with an analysis of these studies, and consider the findings holistically. It is useful for the research community to be aware of the limitations of these studies, and we report on these measures of study quality in Appendix 1 . We also think it is useful to report on the evidence, within our findings, of a lack of clarity regarding what is actually meant by the term “Learning Styles.” Taken all together these analyses could prompt further research, using a large representative sample with a high response rate, using a neutral invitation, with a clear explanation of the difference between Learning Styles and styles of Learning. Perhaps most importantly this research should focus on whether educators act on their belief, as described above.

Some of these limitations, in particular those regarding representative sampling, are tempered by the number of studies and a consistency in the findings between studies, and the overall very high rates of self-reported belief in Learning Styles. Thirty-four samples report on this question, and in all studies, the majority of participants agree with the key question. In 25 of the 34 samples, the rate of agreement is over 80%. Even if some samples were not representative, it would seem unlikely to affect the qualitative account of the main finding (although this may be undermined by the other limitations described above).

A summary conclusion from our findings then is that belief in matching instruction to Learning Styles is high and has not declined, even though there is currently no evidence to support such an approach. There are a number of methodological issues which might affect that conclusion, but when taken all together these are insufficient to completely alleviate the concerns which arise from the conclusion; a substantial majority of educators state belief in a technique for which the lack of evidence was established in 2004. In the final section of the discussion here we then consider, from a pragmatic perspective, what are the useful things that we might do with these findings, and consider what could be done to address the concerns which arise from them.

Our findings present some limited evidence that training has some effect on belief in matching instruction to Learning Styles. Only four studies looked at training, but in those studies the percentage who reported that belief in the efficacy of matching instruction to individual Learning Styles dropped from 78.4 to 37.1%. It seems reasonable to conclude that there is a risk of social desirability bias in these studies; if participants have been given training which explains the lack of evidence to support Learning Styles, then they might be reasonably expected to disagree with a statement which supports matching. Even then, for 37.1% of participants to still report that they believe this approach is effective is potentially concerning; it still represents a substantial number of educators. Perhaps more importantly these findings are, like many others discussed here, a self-report of a belief, rather than a measure of actual behavior.

There is already a substantial body of literature which identifies Learning Styles as a neuromyth, or an “urban legend.” A 2018 study analyzed the discourse used in a sample of this literature and concluded that the language used reflected a power imbalance wherein “experts” told practitioners what was true or not. A conclusion was that this language may not be helpful if we truly want to address this widespread belief in a method that is ineffective ( Smets and Struyven, 2018 ). We have previously proposed that a “debunking” approach is unlikely to be effective ( Newton and Miah, 2017 ). It takes time and effort to identify student learning styles, and much more effort to then try and design instruction to match those styles. The sorts of instructors who go to that sort of effort are likely to be motivated by a desire to help their students, and so to be told that they have been propagating a “myth” seems unlikely to be news that it is well received.

Considering these limitations from a pragmatic perspective, it does not seem that training, or debunking, is a useful approach to addressing widespread belief in Learning Styles. It is also difficult to determine whether training has been effective when we have limited data regarding the actual use of Learning Styles theory. It may be better to focus on the promotion of techniques that are demonstrably effective, such as retrieval practice and other simple techniques as described in the introduction. There is evidence that these are currently lacking from teacher training ( National Council on Teacher Quality, 2016 ). Many evidence-based techniques are simple to implement, for example the use of practice tests, the spacing of instruction, and the use of worked examples ( Young et al., 2014 ; Weinstein et al., 2018 ). Concerns exist about the generalizability of education research findings to specific contexts, but these concerns might be addressed by the use of a pragmatic approach ( Newton et al., accepted ).

In summary then, we find a substantial majority of educators, almost 90%, from samples all over the world in all types of education, report that they believe in the efficacy of a teaching technique that is demonstrably not effective and potentially harmful. There is no sign that this is declining, despite many years of work, in the academic literature and popular press, highlighting this lack of evidence. To understand this fully, future work should focus on the objective behavior of educators. How many of us actually match instruction to the individual Learning Styles of students, and what are the consequences when we do? Does it matter? Should we instead focus on promoting effective approaches rather than debunking myths?

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s.

Author Contributions

PN conceived and designed the study, undertook searches, extracted data, undertook analysis, drafted manuscript, and finalized manuscript. AS re-extracted data and provided critical comments on the manuscript. AS and PN undertook PRIMSA quality analyses.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to acknowledge the assistance of Gabriella Santiago and Michael Chau who undertook partial preliminary data extraction on a subset of papers identified in an initial search. We would also like to thank Prof Greg Fegan and Dr. Owen Bodger for advice and reassurance with the analysis.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2020.602451/full#supplementary-material

Aslaksen, K., and Lorås, H. (2018). The modality-specific learning style hypothesis: a mini-review. Front. Psychol . 9:1538. doi: 10.3389/fpsyg.2018.01538

CrossRef Full Text | Google Scholar

Aslaksen, K., and Lorås, H. (2019). Matching instruction with modality-specific learning style: effects on immediate recall and working memory performance. Educ. Sci . 9:32. doi: 10.3390/educsci9010032

Bellert, A., and Graham, L. (2013). Neuromyths and neurofacts: information from cognitive neuroscience for classroom and learning support teachers. Spec. Educ. Perspect . 22, 7–20.

Google Scholar

Bennett, C., Khangura, S., Brehaut, J. C., Graham, I. D., Moher, D., Potter, B. K., et al. (2011). Reporting guidelines for survey research: an analysis of published guidance and reporting practices. PLoS Med . 8:e1001069. doi: 10.1371/journal.pmed.1001069

PubMed Abstract | CrossRef Full Text | Google Scholar

Betts, K., Miller, M., Tokuhama-Espinosa, T., Shewokis, P., Anderson, A., Borja, C., et al. (2019). International Report: Neuromyths and Evidence-Based Practices in Higher Education . Available online at: https://onlinelearningconsortium.org/read/international-report-neuromyths-and-evidence-based-practices-in-higher-education/ (accessed September 01, 2020).

Boeker, M., Vach, W., and Motschall, E. (2013). Google Scholar as replacement for systematic literature searches: good relative recall and precision are not enough. BMC Medical Research Methodology , 13:131.

PubMed Abstract | Google Scholar

Boutron, I., Page, M. J., Higgins, J. P. T., Altman, D. G., Lundh, A., and Hróbjartsson, A. (2019). “Chapter 7: considering bias and conflicts of interest among the included studies,” in Cochrane Handbook for Systematic Reviews of Interventions version 6.0 (Cochrane). Available online at: /handbook/current/chapter-07 (accessed July 2019).

Brueck, H. (2018). There's No Such Thing as “Auditory” or “Visual” Learners. Business Insider . Available online at: https://www.businessinsider.com/auditory-visual-kinesthetic-learning-styles-arent-real-2018–l-2012 (accessed September 01, 2020).

Canbulat, T., and Kiriktas, H. (2017). Assessment of educational neuromyths among teachers and teacher candidates. J. Educ. Learn. 6:326. doi: 10.5539/jel.v6n2p326

Coffield, F., Moseley, D., Hall, E., and Ecclestone, K. (2004). Learning Styles and Pedagogy in Post 16 Learning: A Systematic and Critical Review. The Learning and Skills Research Centre . Available online at: https://www.voced.edu.au/content/ngv%3A13692 (accessed September 01, 2020).

Cuevas, J. (2015). Is learning styles-based instruction effective? A comprehensive analysis of recent research on learning styles. Theory Res. Educ . 13:308–333. doi: 10.1177/1477878515606621

Dandy, K., and Bendersky, K. (2014). Student and faculty beliefs about learning in higher education: implications for teaching. Int. J. Teach. Learn. High. Educ . 26, 358−380.

Dekker, S., Lee, N. C., Howard-Jones, P., and Jolles, J. (2012). Neuromyths in education: prevalence and predictors of misconceptions among teachers. Front. Psychol. 3:429. doi: 10.3389/fpsyg.2012.00429

Dündar, S., and Gündüz, N. (2016). Misconceptions regarding the brain: the neuromyths of preservice teachers. Mind Brain Educ . 10, 212–232. doi: 10.1111/mbe.12119

Dunn, R., Dunn, K., and Price, G. E. (1977). Diagnosing learning styles: a prescription for avoiding malpractice suits. Phi Delta Kappan . 58, 418–420.

Dunn, R., Griggs, S. A., Olson, J., Beasley, M., and Gorman, B. S. (1995). A meta-analytic validation of the Dunn and Dunn model of learning-style preferences. J. Educ. Res . 88, 353–362. doi: 10.1080/00220671.1995.9941181

Duram (2010). In L. A. Pragmatic Study, Encyclopedia of Research Design . Thousand Oaks, CA: SAGE Publications, Inc.

Feilzer, M. (2010). Doing mixed methods research pragmatically: implications for the rediscovery of pragmatism as a research paradigm. J. Mix. Methods Res . 4, 6–16. doi: 10.1177/1558689809349691

Felder, R. (2020). “OPINION: uses, misuses, and validity of learning styles,” in Advances in Engineering Education . Available online at: https://advances.asee.org/opinion-uses-misuses-and-validity-of-learning-styles/ (accessed September 01, 2020).

Gleichgerrcht, E., Luttges, B. L., Salvarezza, F., and Campos, A. L. (2015). Educational neuromyths among teachers in Latin America. Mind Brain Educ . 9, 170–178. doi: 10.1111/mbe.12086

Grospietsch, F., and Mayer, J. (2018). Professionalizing pre-service biology teachers' misconceptions about learning and the brain through conceptual change. Educ Sci . 8:120. doi: 10.3390/educsci8030120

Grospietsch, F., and Mayer, J. (2019). Pre-service science teachers' neuroscience literacy: neuromyths and a professional understanding of learning and memory. Front. Hum. Neurosci . 13:20. doi: 10.3389/fnhum.2019.00020

Grospietsch, F., and Mayer, J. (2020). Misconceptions about neuroscience – prevalence and persistence of neuromyths in education. Neuroforum 26, 63–71. doi: 10.1515/nf-2020-0006

Haddaway, N. R., Collins, A. M., Coughlin, D., and Kirk, S. (2015). The role of Google Scholar in evidence reviews and its applicability to grey literature searching. PLoS ONE 10:e0138237. doi: 10.1371/journal.pone.0138237

Horvath, J. C., Donoghue, G. M., Horton, A. J., Lodge, J. M., and Hattie, J. A. C. (2018). On the irrelevance of neuromyths to teacher effectiveness: comparing neuro-literacy levels amongst award-winning and non-award winning teachers. Front. Psychol . 9:1666. doi: 10.3389/fpsyg.2018.01666

Howard-Jones, P. A., Franey, L., Mashmoushi, R., and Liao, Y.-C. (2009). “The neuroscience literacy of trainee teachers,” in British Educational Research Association Annual Conference, Manchester .

Husmann, P. R., and O'Loughlin, V. D. (2018). Another nail in the coffin for learning styles? Disparities among undergraduate anatomy students' study strategies, class performance, and reported VARK learning styles. Anatom. Sci. Educ . 12:6–19. doi: 10.1002/ase.1777

Hyman, R., and Rosoff, B. (1984). Matching learning and teaching styles: the jug and what's in it. Theory Pract . 23:35. doi: 10.1080/00405848409543087

Jamali, H. R., and Nabavi, M. (2015). Open access and sources of full-text articles in Google Scholar in different subject fields. Scientometrics 105, 1635–1651. doi: 10.1007/s11192-015-1642-2

Kavale, K. A., Hirshoren, A., and Forness, S. R. (1998). Meta-analytic validation of the Dunn and Dunn model of learning-style preferences: a critique of what was Dunn. Learn. Disabil. Res. Pract . 13, 75–80.

Kavale, K. A., and LeFever, G. B. (2007). Dunn and Dunn model of learning-style preferences: critique of lovelace meta-analysis. J. Educ. Res . 101, 94–97. doi: 10.3200/JOER.101.2.94-98

Kelley, K., Clark, B., Brown, V., and Sitzia, J. (2003). Good practice in the conduct and reporting of survey research. Int J Qual. Health Care 15, 261–266. doi: 10.1093/intqhc/mzg031

Kilpatrick, J. T. (2012). Elementary School Teachers' Perspectives on Learning Styles, Sense of Efficacy, and Self-Theories of Intelligence [Text, Western Carolina University] . Available online at: http://libres.uncg.edu/ir/wcu/listing.aspx?id=9058 (accessed September 01, 2020).

Krätzig, G., and Arbuthnott, K. (2006). Perceptual learning style and learning proficiency: a test of the hypothesis. J. Educ. Psychol . 98, 238–246. doi: 10.1037/0022-0663.98.1.238

Lopa, J., and Wray, M. L. (2015). Debunking the matching hypothesis of learning style theorists in hospitality education. J. Hospital. Tour. Educ . 27, 120–128. doi: 10.1080/10963758.2015.1064317

Lovelace, M. K. (2005). Meta-analysis of experimental research based on the Dunn and Dunn model. J. Educ. Res . 98, 176–183. doi: 10.3200/JOER.98.3.176-183

Macdonald, K., Germine, L., Anderson, A., Christodoulou, J., and McGrath, L. M. (2017). Dispelling the myth: training in education or neuroscience decreases but does not eliminate beliefs in neuromyths. Front.Psycho. 8:1314.

Massa, L. J., and Mayer, R. E. (2006). Testing the ATI hypothesis: should multimedia instruction accommodate verbalizer-visualizer cognitive style? Learn. Indiv. Diff . 16, 321–335. doi: 10.1016/j.lindif.2006.10.001

Mayer, R. E., and Massa, L. J. (2003). Three facets of visual and verbal learners: cognitive ability, cognitive style, and learning preference. J. Educ. Psychol . 95, 833–846. doi: 10.1037/0022-0663.95.4.833

Menz, C., Spinath, B., and Seifried, E. (2020). Misconceptions die hard: prevalence and reduction of wrong beliefs in topics from educational psychology among preservice teachers. Eur. J. Psychol. Educ . doi: 10.1007/s10212-020-00474-5. [Epub ahead of print].

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., and Group, T. P. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med . 6:e1000097. doi: 10.1371/journal.pmed.1000097

Morehead, K., Rhodes, M. G., and DeLozier, S. (2016). Instructor and student knowledge of study strategies. Memory 24, 257–271. doi: 10.1080/09658211.2014.1001992

National Council on Teacher Quality (2016). Learning About Learning: What Every New Teacher Needs to Know. National Council on Teacher Quality . Available online at: http://www.nctq.org/dmsStage.do?fn=Learning_About_Learning_Report (accessed September 01, 2020).

Newton, P. (2015). The learning styles myth is thriving in higher education. Educ. Psychol . 6:1908. doi: 10.3389/fpsyg.2015.01908

Newton, P., Da Silva, A., and Berry, S. (accepted). The case for pragmatic evidence-based higher education; a useful way forward? Front. Educ. doi: 10.3389/feduc.2020.583157

CrossRef Full Text

Newton, P., and Miah, M. (2017). Evidence-based higher education – is the learning styles ‘myth’ important? Front. Psychol . 8:444. doi: 10.3389/fpsyg.2017.00444

Papadatou-Pastou, M., Touloumakos, A. K., Koutouveli, C., and Barrable, A. (2020). The learning styles neuromyth: when the same term means different things to different teachers. Eur. J. Psychol. Educ . doi: 10.1007/s10212-020-00485-2. [Epub ahead of print].

Pashler, H., McDaniel, M., Rohrer, D., and Bjork, R. (2008). Learning styles: concepts and evidence. Psychol. Sci. Public Interest 9, 105–119. doi: 10.1111/j.1539-6053.2009.01038.x

Pullmann, J. (2017). Scientists: “Learning Styles” Like Auditory, Visual, And Kinesthetic Are Bunk. The Federalist . Available online at: https://thefederalist.com/2017/03/22/brain-scientists-learning-styles-like-auditory-visual-and-kinesthetic-are-bunk/ (accessed September 01, 2020).

Riener, C., and Willingham, D. (2010). The myth of learning styles. Change Magaz. High. Learn . 42, 32–35. doi: 10.1080/00091383.2010.503139

Rogowsky, B. A., Calhoun, B. M., and Tallal, P. (2015). Matching learning style to instructional method: Effects on comprehension. J. Educ. Psychol . 107, 64–78. doi: 10.1037/a0037478

Rogowsky, B. A., Calhoun, B. M., and Tallal, P. (2020). Providing instruction based on students' learning style preferences does not improve learning. Front. Psychol . 11:164. doi: 10.3389/fpsyg.2020.00164

Rohrer, D., and Pashler, H. (2012). Learning styles: where's the evidence? Med. Educ. 46, 634–635. doi: 10.1111/j.1365-2923.2012.04273.x

Ruhaak, A. E., and Cook, B. G. (2018). The prevalence of educational neuromyths among pre-service special education teachers. Mind Brain Educ . 12, 155–161. doi: 10.1111/mbe.12181

Smets, W., and Struyven, K. (2018). Power relations in educational scientific communication—A critical analysis of discourse on learning styles. Cogent Educ . 5:1429722. doi: 10.1080/2331186X.2018.1429722

Strauss, V. (2017). Analysis | Most Teachers Believe That Kids Have Different ‘Learning Styles.’ Here's Why They Are Wrong. Washington Post . Available online at: https://www.washingtonpost.com/news/answer-sheet/wp/2017/09/05/most-teachers-believe-that-kids-have-different-learning-styles-heres-why-they-are-wrong/ (accessed September 01, 2020).

Tardif, E., Doudin, P., and Meylan, N. (2015). Neuromyths among teachers and student teachers. Mind Brain Educ . 9, 50–59. doi: 10.1111/mbe.12070

van Dijk, W., and Lane, H. B. (2018). The brain and the us education system: perpetuation of neuromyths. Exceptionality 28, 1–14. doi: 10.1080/09362835.2018.1480954

Weinstein, Y., Madan, C. R., and Sumeracki, M. A. (2018). Teaching the science of learning. Cognit. Res. Princ. Implic . 3:2. doi: 10.1186/s41235-017-0087-y

Willingham, D. T., Hughes, E. M., and Dobolyi, D. G. (2015). The scientific status of learning styles theories. Teach. Psychol . 42, 266–271. doi: 10.1177/0098628315589505

Wininger, S. R., Redifer, J. L., Norman, A. D., and Ryle, M. K. (2019). Prevalence of learning styles in educational psychology and introduction to education textbooks: a content analysis. Psychol. Learn. Teach . 18, 221–243. doi: 10.1177/1475725719830301

Young, J. Q., Merrienboer, J. V., Durning, S., and Cate, O. T. (2014). Cognitive load theory: implications for medical education: AMEE guide no. 86. Med. Teach . 36, 371–384. doi: 10.3109/0142159X.2014.889290

Keywords: evidence-based education, pragmatism, neuromyth, differentiation, VARK, Kolb, Honey and Mumford

Citation: Newton PM and Salvi A (2020) How Common Is Belief in the Learning Styles Neuromyth, and Does It Matter? A Pragmatic Systematic Review. Front. Educ. 5:602451. doi: 10.3389/feduc.2020.602451

Received: 12 September 2020; Accepted: 25 November 2020; Published: 14 December 2020.

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Copyright © 2020 Newton and Salvi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Philip M. Newton, p.newton@swansea.ac.uk

This article is part of the Research Topic

How to Improve Neuroscience Education for the Public and for a Multi-Professional Audience in Different Parts of the Globe

Learning Styles: Concepts and Evidence

Affiliations.

• 1 University of California, San Diego [email protected].
• 2 Washington University in St. Louis.
• 3 University of South Florida.
• 4 University of California, Los Angeles.
• PMID: 26162104
• DOI: 10.1111/j.1539-6053.2009.01038.x

The term "learning styles" refers to the concept that individuals differ in regard to what mode of instruction or study is most effective for them. Proponents of learning-style assessment contend that optimal instruction requires diagnosing individuals' learning style and tailoring instruction accordingly. Assessments of learning style typically ask people to evaluate what sort of information presentation they prefer (e.g., words versus pictures versus speech) and/or what kind of mental activity they find most engaging or congenial (e.g., analysis versus listening), although assessment instruments are extremely diverse. The most common-but not the only-hypothesis about the instructional relevance of learning styles is the meshing hypothesis, according to which instruction is best provided in a format that matches the preferences of the learner (e.g., for a "visual learner," emphasizing visual presentation of information). The learning-styles view has acquired great influence within the education field, and is frequently encountered at levels ranging from kindergarten to graduate school. There is a thriving industry devoted to publishing learning-styles tests and guidebooks for teachers, and many organizations offer professional development workshops for teachers and educators built around the concept of learning styles. The authors of the present review were charged with determining whether these practices are supported by scientific evidence. We concluded that any credible validation of learning-styles-based instruction requires robust documentation of a very particular type of experimental finding with several necessary criteria. First, students must be divided into groups on the basis of their learning styles, and then students from each group must be randomly assigned to receive one of multiple instructional methods. Next, students must then sit for a final test that is the same for all students. Finally, in order to demonstrate that optimal learning requires that students receive instruction tailored to their putative learning style, the experiment must reveal a specific type of interaction between learning style and instructional method: Students with one learning style achieve the best educational outcome when given an instructional method that differs from the instructional method producing the best outcome for students with a different learning style. In other words, the instructional method that proves most effective for students with one learning style is not the most effective method for students with a different learning style. Our review of the literature disclosed ample evidence that children and adults will, if asked, express preferences about how they prefer information to be presented to them. There is also plentiful evidence arguing that people differ in the degree to which they have some fairly specific aptitudes for different kinds of thinking and for processing different types of information. However, we found virtually no evidence for the interaction pattern mentioned above, which was judged to be a precondition for validating the educational applications of learning styles. Although the literature on learning styles is enormous, very few studies have even used an experimental methodology capable of testing the validity of learning styles applied to education. Moreover, of those that did use an appropriate method, several found results that flatly contradict the popular meshing hypothesis. We conclude therefore, that at present, there is no adequate evidence base to justify incorporating learning-styles assessments into general educational practice. Thus, limited education resources would better be devoted to adopting other educational practices that have a strong evidence base, of which there are an increasing number. However, given the lack of methodologically sound studies of learning styles, it would be an error to conclude that all possible versions of learning styles have been tested and found wanting; many have simply not been tested at all. Further research on the use of learning-styles assessment in instruction may in some cases be warranted, but such research needs to be performed appropriately.

© 2008 Association for Psychological Science.

• Research article
• Open access
• Published: 01 October 2021

Adaptive e-learning environment based on learning styles and its impact on development students' engagement

• Hassan A. El-Sabagh   ORCID: orcid.org/0000-0001-5463-5982 1 , 2  

International Journal of Educational Technology in Higher Education volume  18 , Article number:  53 ( 2021 ) Cite this article

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Adaptive e-learning is viewed as stimulation to support learning and improve student engagement, so designing appropriate adaptive e-learning environments contributes to personalizing instruction to reinforce learning outcomes. The purpose of this paper is to design an adaptive e-learning environment based on students' learning styles and study the impact of the adaptive e-learning environment on students’ engagement. This research attempts as well to outline and compare the proposed adaptive e-learning environment with a conventional e-learning approach. The paper is based on mixed research methods that were used to study the impact as follows: Development method is used in designing the adaptive e-learning environment, a quasi-experimental research design for conducting the research experiment. The student engagement scale is used to measure the following affective and behavioral factors of engagement (skills, participation/interaction, performance, emotional). The results revealed that the experimental group is statistically significantly higher than those in the control group. These experimental results imply the potential of an adaptive e-learning environment to engage students towards learning. Several practical recommendations forward from this paper: how to design a base for adaptive e-learning based on the learning styles and their implementation; how to increase the impact of adaptive e-learning in education; how to raise cost efficiency of education. The proposed adaptive e-learning approach and the results can help e-learning institutes in designing and developing more customized and adaptive e-learning environments to reinforce student engagement.

Introduction

In recent years, educational technology has advanced at a rapid rate. Once learning experiences are customized, e-learning content becomes richer and more diverse (El-Sabagh & Hamed, 2020 ; Yang et al., 2013 ). E-learning produces constructive learning outcomes, as it allows students to actively participate in learning at anytime and anyplace (Chen et al., 2010 ; Lee et al., 2019 ). Recently, adaptive e-learning has become an approach that is widely implemented by higher education institutions. The adaptive e-learning environment (ALE) is an emerging research field that deals with the development approach to fulfill students' learning styles by adapting the learning environment within the learning management system "LMS" to change the concept of delivering e-content. Adaptive e-learning is a learning process in which the content is taught or adapted based on the responses of the students' learning styles or preferences. (Normadhi et al., 2019 ; Oxman & Wong, 2014 ). By offering customized content, adaptive e-learning environments improve the quality of online learning. The customized environment should be adaptable based on the needs and learning styles of each student in the same course. (Franzoni & Assar, 2009 ; Kolekar et al., 2017 ). Adaptive e-learning changes the level of instruction dynamically based on student learning styles and personalizes instruction to enhance or accelerate a student's success. Directing instruction to each student's strengths and content needs can minimize course dropout rates, increase student outcomes and the speed at which they are accomplished. The personalized learning approach focuses on providing an effective, customized, and efficient path of learning so that every student can participate in the learning process (Hussein & Al-Chalabi, 2020 ). Learning styles, on the other hand, represent an important issue in learning in the twenty-first century, with students expected to participate actively in developing self-understanding as well as their environment engagement. (Klasnja-Milicevic et al., 2011 ; Nuankaew et al., 2019 ; Truong, 2016 ).

In current conventional e-learning environments, instruction has traditionally followed a “one style fits all” approach, which means that all students are exposed to the same learning procedures. This type of learning does not take into account the different learning styles and preferences of students. Currently, the development of e-learning systems has accommodated and supported personalized learning, in which instruction is fitted to a students’ individual needs and learning styles (Beldagli & Adiguzel, 2010 ; Benhamdi et al., 2017 ; Pashler et al., 2008 ). Some personalized approaches let students choose content that matches their personality (Hussein & Al-Chalabi, 2020 ). The delivery of course materials is an important issue of personalized learning. Moreover, designing a well-designed, effective, adaptive e-learning system represents a challenge due to complication of adapting to the different needs of learners (Alshammari, 2016 ). Regardless of using e-learning claims that shifting to adaptive e-learning environments to be able to reinforce students' engagement. However, a learning environment cannot be considered adaptive if it is not flexible enough to accommodate students' learning styles. (Ennouamani & Mahani, 2017 ).

On the other hand, while student engagement has become a central issue in learning, it is also an indicator of educational quality and whether active learning occurs in classes. (Lee et al., 2019 ; Nkomo et al., 2021 ; Robinson & Hullinger, 2008 ). Veiga et al. ( 2014 ) suggest that there is a need for further research in engagement because assessing students’ engagement is a predictor of learning and academic progress. It is important to clarify the distinction between causal factors such as learning environment and outcome factors such as achievement. Accordingly, student engagement is an important research topic because it affects a student's final grade, and course dropout rate (Staikopoulos et al., 2015 ).

The Umm Al-Qura University strategic plan through common first-year deanship has focused on best practices that increase students' higher-order skills. These skills include communication skills, problem-solving skills, research skills, and creative thinking skills. Although the UQU action plan involves improving these skills through common first-year academic programs, the student's learning skills need to be encouraged and engaged more (Umm Al-Qura University Agency, 2020 ). As a result of the author's experience, The conventional methods of instruction in the "learning skills" course were observed, in which the content is presented to all students in one style that is dependent on understanding the content regardless of the diversity of their learning styles.

According to some studies (Alshammari & Qtaish, 2019 ; Lee & Kim, 2012 ; Shih et al., 2008 ; Verdú, et al., 2008 ; Yalcinalp & Avc, 2019 ), there is little attention paid to the needs and preferences of individual learners, and as a result, all learners are treated in the same way. More research into the impact of educational technologies on developing skills and performance among different learners is recommended. This “one-style-fits-all” approach implies that all learners are expected to use the same learning style as prescribed by the e-learning environment. Subsequently, a review of the literature revealed that an adaptive e-learning environment can affect learning outcomes to fill the identified gap. In conclusion: Adaptive e-learning environments rely on the learner's preferences and learning style as a reference that supports to create adaptation.

To confirm the above: the author conducted an exploratory study via an open interview that included some questions with a sample of 50 students in the learning skills department of common first-year. Questions asked about the difficulties they face when learning a "learning skills" course, what is the preferred way of course content. Students (88%) agreed that the way students are presented does not differ according to their differences and that they suffer from a lack of personal learning that is compatible with their style of work. Students (82%) agreed that they lack adaptive educational content that helps them to be engaged in the learning process. Accordingly, the author handled the research problem.

This research supplements to the existing body of knowledge on the subject. It is considered significant because it improves understanding challenges involved in designing the adaptive environments based on learning styles parameter. Subsequently, this paper is structured as follows: The next section presents the related work cited in the literature, followed by research methodology, then data collection, results, discussion, and finally, some conclusions and future trends are discussed.

Theoretical framework

This section briefly provides a thorough review of the literature about the adaptive E-learning environments based on learning styles.

Adaptive e-learning environments based on learning styles

The adaptive e-learning employment in higher education has been slower to evolve, and challenges that led to the slow implementation still exist. The learning management system offers the same tools to all learners, although individual learners need different details based on learning style and preferences. (Beldagli & Adiguzel, 2010 ; Kolekar et al., 2017 ). The interactive e-learning environment requisite evaluating the learner's desired learning style, before the course delivery, such as an online quiz or during the course delivery, such as tracking student reactions (DeCapua & Marshall, 2015 ).

In e-learning environments, adaptation is constructed on a series of well-designed processes to fit the instructional materials. The adaptive e-learning framework attempt to match instructional content to the learners' needs and styles. According to Qazdar et al. ( 2015 ), adaptive e-learning (AEL) environments rely on constructing a model of each learner's needs, preferences, and styles. It is well recognized that such adaptive behavior can increase learners' development and performance, thus enriching learning experience quality. (Shi et al., 2013 ). The following features of adaptive e-learning environments can be identified through diversity, interactivity, adaptability, feedback, performance, and predictability. Although adaptive framework taxonomy and characteristics related to various elements, adaptive learning includes at least three elements: a model of the structure of the content to be learned with detailed learning outcomes (a content model). The student's expertise based on success, as well as a method of interpreting student strengths (a learner model), and a method of matching the instructional materials and how it is delivered in a customized way (an instructional model) (Ali et al., 2019 ). The number of adaptive e-learning studies has increased over the last few years. Adaptive e-learning is likely to increase at an accelerating pace at all levels of instruction (Hussein & Al-Chalabi, 2020 ; Oxman & Wong, 2014 ).

Many studies assured the power of adaptive e-learning in delivering e-content for learners in a way that fitting their needs, and learning styles, which helps improve the process of students' acquisition of knowledge, experiences and develop their higher thinking skills (Ali et al., 2019 ; Behaz & Djoudi, 2012 ; Chun-Hui et al., 2017 ; Daines et al., 2016 ; Dominic et al., 2015 ; Mahnane et al., 2013 ; Vassileva, 2012 ). Student characteristics of learning style are recognized as an important issue and a vital influence in learning and are frequently used as a foundation to generate personalized learning experiences (Alshammari & Qtaish, 2019 ; El-Sabagh & Hamed, 2020 ; Hussein & Al-Chalabi, 2020 ; Klasnja-Milicevic et al., 2011 ; Normadhi et al., 2019 ; Ozyurt & Ozyurt, 2015 ).

The learning style is a parameter of designing adaptive e-learning environments. Individuals differ in their learning styles when interacting with the content presented to them, as many studies emphasized the relationship between e-learning and learning styles to be motivated in learning situations, consequently improving the learning outcomes (Ali et al., 2019 ; Alshammari, 2016 ; Alzain et al., 2018a , b ; Liang, 2012 ; Mahnane et al., 2013 ; Nainie et al., 2010 ; Velázquez & Assar, 2009 ). The word "learning style" refers to the process by which the learner organizes, processes, represents, and combines this information and stores it in his cognitive source, then retrieves the information and experiences in the style that reflects his technique of communicating them. (Fleming & Baume, 2006 ; Jaleel & Thomas, 2019 ; Jonassen & Grabowski, 2012 ; Klasnja-Milicevic et al., 2011 ; Nuankaew et al., 2019 ; Pashler et al., 2008 ; Willingham et al., 2105 ; Zhang, 2017 ). The concept of learning style is founded based on the fact that students vary in their styles of receiving knowledge and thought, to help them recognizing and combining information in their mind, as well as acquire experiences and skills. (Naqeeb, 2011 ). The extensive scholarly literature on learning styles is distributed with few strong experimental findings (Truong, 2016 ), and a few findings on the effect of adapting instruction to learning style. There are many models of learning styles (Aldosarim et al., 2018 ; Alzain et al., 2018a , 2018b ; Cletus & Eneluwe, 2020 ; Franzoni & Assar, 2009 ; Willingham et al., 2015 ), including the VARK model, which is one of the most well-known models used to classify learning styles. The VARK questionnaire offers better thought about information processing preferences (Johnson, 2009 ). Fleming and Baume ( 2006 ) developed the VARK model, which consists of four students' preferred learning types. The letter "V" represents for visual and means the visual style, while the letter "A" represents for auditory and means the auditory style, and the letter "R/W" represents "write/read", means the reading/writing style, and the letter "K" represents the word "Kinesthetic" and means the practical style. Moreover, VARK distinguishes the visual category further into graphical and textual or visual and read/write learners (Murphy et al., 2004 ; Leung, et al., 2014 ; Willingham et al., 2015 ). The four categories of The VARK Learning Style Inventory are shown in the Fig. 1 below.

VARK learning styles

According to the VARK model, learners are classified into four groups representing basic learning styles based on their responses which have 16 questions, there are four potential responses to each question, where each answer agrees to one of the extremes of the dimension (Hussain, 2017 ; Silva, 2020 ; Zhang, 2017 ) to support instructors who use it to create effective courses for students. Visual learners prefer to take instructional materials and send assignments using tools such as maps, graphs, images, and other symbols, according to Fleming and Baume ( 2006 ). Learners who can read–write prefer to use written textual learning materials, they use glossaries, handouts, textbooks, and lecture notes. Aural learners, on the other hand, prefer to learn through spoken materials, dialogue, lectures, and discussions. Direct practice and learning by doing are preferred by kinesthetic learners (Becker et al., 2007 ; Fleming & Baume, 2006 ; Willingham et al., 2015 ). As a result, this research work aims to provide a comprehensive discussion about how these individual parameters can be applied in adaptive e-learning environment practices. Dominic et al., ( 2015 ) presented a framework for an adaptive educational system that personalized learning content based on student learning styles (Felder-Silverman learning model) and other factors such as learners' learning subject competency level. This framework allowed students to follow their adaptive learning content paths based on filling in "ils" questionnaire. Additionally, providing a customized framework that can automatically respond to students' learning styles and suggest online activities with complete personalization. Similarly, El Bachari et al. ( 2011 ) attempted to determine a student's unique learning style and then adapt instruction to that individual interests. Adaptive e-learning focused on learner experience and learning style has a higher degree of perceived usability than a non-adaptive e-learning system, according to Alshammari et al. ( 2015 ). This can also improve learners' satisfaction, engagement, and motivation, thus improving their learning.

According to the findings of (Akbulut & Cardak, 2012 ; Alshammari & Qtaish, 2019 ; Alzain et al., 2018a , b ; Shi et al., 2013 ; Truong, 2016 ), adaptation based on a combination of learning style, and information level yields significantly better learning gains. Researchers have recently initiated to focus on how to personalize e-learning experiences using personal characteristics such as the student's preferred learning style. Personal learning challenges are addressed by adaptive learning programs, which provide learners with courses that are fit to their specific needs, such as their learning styles.

• Student engagement

Previous research has emphasized that student participation is a key factor in overcoming academic problems such as poor academic performance, isolation, and high dropout rates (Fredricks et al., 2004 ). Student participation is vital to student learning, especially in an online environment where students may feel isolated and disconnected (Dixson, 2015 ). Student engagement is the degree to which students consciously engage with a course's materials, other students, and the instructor. Student engagement is significant for keeping students engaged in the course and, as a result, in their learning (Barkley & Major, 2020 ; Lee et al., 2019 ; Rogers-Stacy, et al, 2017 ). Extensive research was conducted to investigate the degree of student engagement in web-based learning systems and traditional education systems. For instance, using a variety of methods and input features to test the relationship between student data and student participation (Hussain et al., 2018 ). Guo et al. ( 2014 ) checked the participation of students when they watched videos. The input characteristics of the study were based on the time they watched it and how often students respond to the assessment.

Atherton et al. ( 2017 ) found a correlation between the use of course materials and student performance; course content is more expected to lead to better grades. Pardo et al., ( 2016 ) found that interactive students with interactive learning activities have a significant impact on student test scores. The course results are positively correlated with student participation according to previous research. For example, Atherton et al. ( 2017 ) explained that students accessed learning materials online and passed exams regularly to obtain higher test scores. Other studies have shown that students with higher levels of participation in questionnaires and course performance tend to perform well (Mutahi et al., 2017 ).

Skills, emotion, participation, and performance, according to Dixson ( 2015 ), were factors in online learning engagement. Skills are a type of learning that includes things like practicing on a daily foundation, paying attention while listening and reading, and taking notes. Emotion refers to how the learner feels about learning, such as how much you want to learn. Participation refers to how the learner act in a class, such as chat, discussion, or conversation. Performance is a result, such as a good grade or a good test score. In general, engagement indicated that students spend time, energy learning materials, and skills to interact constructively with others in the classroom, and at least participate in emotional learning in one way or another (that is, be motivated by an idea, willing to learn and interact). Student engagement is produced through personal attitudes, thoughts, behaviors, and communication with others. Thoughts, effort, and feelings to a certain level when studying. Therefore, the student engagement scale attempts to measure what students are doing (thinking actively), how they relate to their learning, and how they relate to content, faculty members, and other learners including the following factors as shown in Fig.  2 . (skills, participation/interaction, performance, and emotions). Hence, previous research has moved beyond comparing online and face-to-face classes to investigating ways to improve online learning (Dixson, 2015 ; Gaytan & McEwen, 2007 ; Lévy & Wakabayashi, 2008 ; Mutahi et al., 2017 ). Learning effort, involvement in activities, interaction, and learning satisfaction, according to reviews of previous research on student engagement, are significant measures of student engagement in learning environments (Dixson, 2015 ; Evans et al., 2017 ; Lee et al., 2019 ; Mutahi et al., 2017 ; Rogers-Stacy et al., 2017 ). These results point to several features of e-learning environments that can be used as measures of student participation. Successful and engaged online learners learn actively, have the psychological inspiration to learn, make good use of prior experience, and make successful use of online technology. Furthermore, they have excellent communication abilities and are adept at both cooperative and self-directed learning (Dixson, 2015 ; Hong, 2009 ; Nkomo et al., 2021 ).

Engagement factors

Overview of designing the adaptive e-learning environment

The paper follows the (ADDIE) Instructional Design Model: analysis, design, develop, implement, and evaluate to answer the first research question. The adaptive learning environment offers an interactive decentralized media environment that takes into account individual differences among students. Moreover, the environment can spread the culture of self-learning, attract students, and increase their engagement in learning.

Any learning environment that is intended to accomplish a specific goal should be consistent to increase students' motivation to learn. so that they have content that is personalized to their specific requirements, rather than one-size-fits-all content. As a result, a set of instructional design standards for designing an adaptive e-learning framework based on learning styles was developed according to the following diagram (Fig. 3 ).

The ID (model) of the adaptive e-learning environment

According to the previous figure, The analysis phase included identifying the course materials and learning tools (syllabus and course plan modules) used for the study. The learning objectives were included in the high-level learning objectives (C4-C6: analysis, synthesis, evaluation).

The design phase included writing SMART objectives, the learning materials were written within the modules plan. To support adaptive learning, four content paths were identified, choosing learning models, processes, and evaluation. Course structure and navigation were planned. The adaptive structural design identified the relationships between the different components, such as introduction units, learning materials, quizzes. Determining the four path materials. The course instructional materials were identified according to the following Figure 4 .

Adaptive e-course design

The development phase included: preparing and selecting the media for the e-course according to each content path in an adaptive e-learning environment. During this process, the author accomplished the storyboard and the media to be included on each page of the storyboard. A category was developed for the instructional media for each path (Fig. 5 )

Roles and deployment diagram of the adaptive e-learning environment

The author developed a learning styles questionnaire via a mobile App. as follows: https://play.google.com/store/apps/details?id=com.pointability.vark . Then, the students accessed the adaptive e-course modules based on their learning styles.

The Implementation phase involved the following: The professional validation of the course instructional materials. Expert validation is used to evaluate the consistency of course materials (syllabi and modules). The validation was performed including the following: student learning activities, learning implementation capability, and student reactions to modules. The learner's behaviors, errors, navigation, and learning process are continuously geared toward improving the learner's modules based on the data the learner gathered about him.

The Evaluation phase included five e-learning specialists who reviewed the adaptive e-learning. After that, the framework was revised based on expert recommendations and feedback. Content assessment, media evaluation in three forms, instructional design, interface design, and usage design included in the evaluation. Adaptive learners checked the proposed framework. It was divided into two sections. Pilot testing where the proposed environment was tested by ten learners who represented the sample in the first phase. Each learner's behavior was observed, questions were answered, and learning control, media access, and time spent learning were all verified.

Research methodology

Research purpose and questions.

This research aims to investigate the impact of designing an adaptive e-learning environment on the development of students' engagement. The research conceptual framework is illustrated in Fig.  6 . Therefore, the articulated research questions are as follows: the main research question is "What is the impact of an adaptive e-learning environment based on (VARK) learning styles on developing students' engagement? Accordingly, there are two sub research questions a) "What is the instructional design of the adaptive e-learning environment?" b) "What is the impact of an adaptive e-learning based on (VARK) learning styles on development students' engagement (skills, participation, performance, emotional) in comparison with conventional e-learning?".

The conceptual framework (model) of the research questions

Research hypotheses

The research aims to verify the validity of the following hypothesis:

There is no statistically significant difference between the students' mean scores of the experimental group that exposed to the adaptive e-learning environment and the scores of the control group that was exposed to the conventional e-learning environment in pre-application of students' engagement scale.

There is a statistically significant difference at the level of (0.05) between the students' mean scores of the experimental group (adaptive e-learning) and the scores of the control group (conventional e-learning) in post-application of students' engagement factors in favor of the experimental group.

Research design

This research was a quasi-experimental research with the pretest-posttest. Research variables were independent and dependent as shown in the following Fig. 7 .

Research "Experimental" design

Both groups were informed with the learning activities tracks, the experimental group was instructed to use the adaptive learning environment to accomplish the learning goals; on the other hand, the control group was exposed to the conventional e-learning environment without the adaptive e-learning parameters.

Research participants

The sample consisted of students studying the "learning skills" course in the common first-year deanship aged between (17–18) years represented the population of the study. All participants were chosen in the academic year 2109–2020 at the first term which was taught by the same instructors. The research sample included two classes (118 students), selected randomly from the learning skills department. First-group was randomly assigned as the control group (N = 58, 31 males and 27 females), the other was assigned as experimental group (N = 60, 36 males and 24 females) was assigned to the other class. The following Table 1 shows the distribution of students' sample "Demographics data".

The instructional materials were not presented to the students before. The control group was expected to attend the conventional e-learning class, where they were provided with the learning environment without adaptive e-learning parameter based on the learning styles that introduced the "learning skills" course. The experimental group was exposed to the use of adaptive e-learning based on learning styles to learn the same course instructional materials within e-course. Moreover, all the student participants were required to read the guidelines to indicate their readiness to participate in the research experiment with permission.

Research instruments

In this research, the measuring tools included the VARK questionnaire and the students' engagement scale including the following factors (skills, participation/interaction, performance, emotional). To begin, the pre-post scale was designed to assess the level of student engagement related to the "learning skills" course before and after participating in the experiment.

VARK questionnaire

Questionnaires are a common method for collecting data in education research (McMillan & Schumacher, 2006 ). The VARK questionnaire had been organized electronically and distributed to the student through the developed mobile app and registered on the UQU system. The questionnaire consisted of 16 items within the scale as MCQ classified into four main factors (kinesthetic, auditory, visual, and R/W).

Reliability and Validity of The VARK questionnaire

For reliability analysis, Cronbach’s alpha is used for evaluating research internal consistency. Internal consistency was calculated through the calculation of correlation of each item with the factor to which it fits and correlation among other factors. The value of 0.70 and above are normally recognized as high-reliability values (Hinton et al., 2014 ). The Cronbach's Alpha correlation coefficient for the VARK questionnaire was 0.83, indicating that the questionnaire was accurate and suitable for further research.

Students' engagement scale

The engagement scale was developed after a review of the literature on the topic of student engagement. The Dixson scale was used to measure student engagement. The scale consisted of 4 major factors as follows (skills, participation/interaction, performance, emotional). The author adapted the original "Dixson scale" according to the following steps. The Dixson scale consisted of 48 statements was translated and accommodated into Arabic by the author. After consulting with experts, the instrument items were reduced to 27 items after adaptation according to the university learning environment. The scale is rated on a 5-point scale.

The final version of the engagement scale comprised 4 factors as follows: The skills engagement included (ten items) to determine keeping up with, reading instructional materials, and exerting effort. Participation/interaction engagement involved (five items) to measure having fun, as well as regularly engaging in group discussion. The performance engagement included (five items) to measure test performance and receiving a successful score. The emotional engagement involved (seven items) to decide whether or not the course was interesting. Students can access to respond engagement scale from the following link: http://bit.ly/2PXGvvD . Consequently, the objective of the scale is to measure the possession of common first-year students of the basic engagement factors before and after instruction with adaptive e-learning compared to conventional e-learning.

Reliability and validity of the engagement scale

The alpha coefficient of the scale factors scores was presented. All four subscales have a strong degree of internal accuracy (0.80–0.87), indicating strong reliability. The overall reliability of the instruments used in this study was calculated using Alfa-alpha, Cronbach's with an alpha value of 0.81 meaning that the instruments were accurate. The instruments used in this research demonstrated strong validity and reliability, allowing for an accurate assessment of students' engagement in learning. The scale was applied to a pilot sample of 20 students, not including the experimental sample. The instrument, on the other hand, had a correlation coefficient of (0.74–0.82), indicating a degree of validity that enables the instrument's use. Table 2 shows the correlation coefficient and Cronbach's alpha based on the interaction scale.

On the other hand, to verify the content validity; the scale was to specialists to take their views on the clarity of the linguistic formulation and its suitability to measure students' engagement, and to suggest what they deem appropriate in terms of modifications.

Research procedures

To calculate the homogeneity and group equivalence between both groups, the validity of the first hypothesis was examined which stated "There is no statistically significant difference between the students' mean scores of the experimental group that exposed to the adaptive e-learning environment and the scores of the control group that was exposed to the conventional e-learning environment in pre-application of students' engagement scale", the author applied the engagement scale to both groups beforehand, and the scores of the pre-application were examined to verify the equivalence of the two groups (experimental and control) in terms of students' engagement.

The t-test of independent samples was calculated for the engagement scale to confirm the homogeneity of the two classes before the experiment. The t-values were not significant at the level of significance = 0.05, meaning that the two groups were homogeneous in terms of students' engagement scale before the experiment.

Since there was no significant difference in the mean scores of both groups ( p  > 0.05), the findings presented in Table 3 showed that there was no significant difference between both experimental and control groups in engagement as a whole, and each student engagement factor separately. The findings showed that the two classes were similar before start of research experiment.

Learner content path in adaptive e-learning environment

The previous well-designed processes are the foundation for adaptation in e-learning environments. There are identified entries for accommodating materials, including classification depending on learning style.: kinesthetic, auditory, visual, and R/W. The present study covered the 1st semester during the 2019/2020 academic year. The course was divided into modules that concentrated on various topics; eleven of the modules included the adaptive learning exercise. The exercises and quizzes were assigned to specific textbook modules. To reduce irrelevant variation, all objects of the course covered the same content, had equal learning results, and were taught by the same instructor.

The experimental group—in which students were asked to bring smartphones—was taught, where the how-to adaptive learning application for adaptive learning was downloaded, and a special account was created for each student, followed by access to the channel designed by the through the application, and the students were provided with instructions and training on how entering application with the appropriate default element of the developed learning objects, while the control group used the variety of instructional materials in the same course for the students.

In this adaptive e-course, students in the experimental group are presented with a questionnaire asked to answer that questions via a developed mobile App. They are provided with four choices. Students are allowed to answer the questions. The correct answer is shown in the students' responses to the results, but the learning module is marked as incomplete. If a student chooses to respond to a question, the correct answer is found immediately, regardless of the student's reaction.

Figure  8 illustrates a visual example from learning styles identification through responding VARK Questionnaire. The learning process experienced by the students in this adaptive Learning environment is as shown in Fig.  4 . Students opened the adaptive course link by tapping the following app " https://play.google.com/store/apps/details?id=com.pointability.vark ," which displayed the appropriate positioning of both the learning skills course and the current status of students. It directed students to the learning skills that they are interested in learning more. Once students reached a specific situation in the e-learning environment, they could access relevant digital instructional materials. Students were then able to progress through the various styles offered by the proposed method, giving them greater flexibility in their learning pace.

Visual example from "learning of the learning styles" identification and adaptive e-learning course process

The "flowchart" diagram below illustrates the learner's path in an adaptive e-learning environment, depending on the (VARK) learning styles (visual, auditory, kinesthetic, reading/writing) (Fig. 9 ).

Student learning path

According to the previous design model of the adaptive framework, the students responded "Learning Styles" questionnaire. Based on each student's results, the orientation of students will direct to each of "Visual", "Aural", "Read-Write", and "Kinesthetic". The student took at the beginning the engagement scale online according to their own pace. When ready, they responded "engagement scale".

Based on the results, the system produced an individualized learning plan to fill in the gap based on the VARK questionnaire's first results. The learner model represents important learner characteristics such as personal information, knowledge level, and learning preferences. Pre and post measurements were performed for both experimental and control groups. The experimental group was exposed only to treatment (using the adaptive learning environment).

To address the second question, which states: “What is the impact "effect" of adaptive e-learning based on (VARK) learning styles on development students' engagement (skills, participation/interaction, performance, emotional) in comparison with conventional e-learning?

The validity of the second hypothesis of the research hypothesis was tested, which states " There is a statistically significant difference at the level of (0.05) between the students' mean scores of the experimental group (adaptive e-learning) and the scores of the control group (conventional e-learning) in post-application of students' engagement factors in favor of the experimental group". To test the hypothesis, the arithmetic means, standard deviations, and "T"-test values were calculated for the results of the two research groups in the application of engagement scale factors".

Table 4 . indicates that students in the experimental group had significantly higher mean of engagement post-test (engagement factors items) scores than students in the control group ( p  < 0.05).

The experimental research was performed to evaluate the impact of the proposed adaptive e-learning. Independent sample t-tests were used to measure the previous behavioral engagement of the two groups related to topic of this research. Subsequently, the findings stated that the experimental group students had higher learning achievement than those who were taught using the conventional e-learning approach.

To verify the effect size of the independent variable in terms of the dependent variable, Cohen (d) was used to investigate that adaptive learning can significantly students' engagement. According to Cohen ( 1992 ), ES of 0.20 is small, 0.50 is medium, and 0.80 is high. In the post-test of the student engagement scale, however, the effect size between students' scores in the experimental and control groups was calculated using (d and r) using means and standard deviations. Cohen's d = 0.826, and Effect-size r = 0.401, according to the findings. The ES of 0.824 means that the treated group's mean is in the 79th percentile of the control group (Large effect). Effect sizes can also be described as the average percentile rank of the average treated learner compared to the average untreated learner in general. The mean of the treated group is at the 50th percentile of the untreated group, indicating an ES of 0.0. The mean of the treated group is at the 79th percentile of the untreated group, with an ES of 0.8. The results showed that the dependent variable was strongly influenced in the four behavioral engagement factors: skills: performance, participation/interaction, and emotional, based on the fact that effect size is a significant factor in determining the research's strength.

Discussions and limitations

This section discusses the impact of an adaptive e-learning environment on student engagement development. This paper aimed to design an adaptive e-learning environment based on learning style parameters. The findings revealed that factors correlated to student engagement in e-learning: skills, participation/interaction, performance, and emotional. The engagement factors are significant because they affect learning outcomes (Nkomo et al., 2021 ). Every factor's items correlate to cognitive process-related activities. The participation/interaction factor, for example, referred to, interactions with the content, peers, and instructors. As a result, student engagement in e-learning can be predicted by interactions with content, peers, and instructors. The results are in line with previous research, which found that customized learning materials are important for increasing students' engagement. Adaptive e-learning based on learning styles sets a strong emphasis on behavioral engagement, in which students manage their learning while actively participating in online classes to adapt instruction according to each learning style. This leads to improved learning outcomes (Al-Chalabi & Hussein, 2020 ; Chun-Hui et al., 2017 ; Hussein & Al-Chalabi, 2020 ; Pashler et al., 2008 ). The experimental findings of this research showed that students who learned through adaptive eLearning based on learning styles learned more; as learning styles are reflected in this research as one of the generally assumed concerns as a reference for adapting e-content path. Students in the experimental group reported that the adaptive eLearning environment was very interesting and able to attract their attention. Those students also indicated that the adaptive eLearning environment was particularly useful because it provided opportunities for them to recall the learning content, thus enhancing their overall learning impression. This may explain why students in the experimental group performed well in class and showed more enthusiasm than students in the control group. This research compared an adaptive e-learning environment to a conventional e-learning approach toward engagement in a learning skills course through instructional content delivery and assessment. It can also be noticed that the experimental group had higher participation than the control group, indicating that BB activities were better adapted to the students' learning styles. Previous studies have agreed on the effectiveness of adaptive learning; it provides students with quality opportunity that is adapted to their learning styles, and preferences (Alshammari, 2016 ; Hussein & Al-Chalabi, 2020 ; Roy & Roy, 2011 ; Surjono, 2014 ). However, it should be noted that this study is restricted to one aspect of content adaptation and its factors, which is learning materials adapting based on learning styles. Other considerations include content-dependent adaptation. These findings are consistent with other studies, such as (Alshammari & Qtaish, 2019 ; Chun-Hui et al., 2017 ), which have revealed the effectiveness of the adaptive e-learning environment. This research differs from others in that it reflects on the Umm Al-Qura University as a case study, VARK Learning styles selection, engagement factors, and the closed learning management framework (BB).

The findings of the study revealed that adaptive content has a positive impact on adaptive individuals' achievement and student engagement, based on their learning styles (kinesthetic; auditory; visual; read/write). Several factors have contributed to this: The design of adaptive e-content for learning skills depended on introducing an ideal learning environment for learners, and providing support for learning adaptation according to the learning style, encouraging them to learn directly, achieving knowledge building, and be enjoyable in the learning process. Ali et al. ( 2019 ) confirmed that, indicating that education is adapted according to each individual's learning style, needs, and characteristics. Adaptive e-content design that allows different learners to think about knowledge by presenting information and skills in a logical sequence based on the adaptive e-learning framework, taking into account its capabilities as well as the diversity of its sources across the web, and these are consistent with the findings of (Alshammari & Qtaish, 2019 ).

Accordingly, the previous results are due to the following: good design of the adaptive e-learning environment in light of the learning style and educational preferences according to its instructional design (ID) standards, and the provision of adaptive content that suits the learners' needs, characteristics, and learning style, in addition to the diversity of course content elements (texts, static images, animations, and video), variety of tests and activities, diversity of methods of reinforcement, return and support from the instructor and peers according to the learning style, as well as it allows ease of use, contains multiple and varied learning sources, and allows referring to the same point when leaving the environment.

Several studies have shown that using adaptive eLearning technologies allows students to improve their learning knowledge and further enhance their engagement in issues such as "skills, performance, interaction, and emotional" (Ali et al., 2019 ; Graf & Kinshuk, 2007 ; Murray & Pérez, 2015 ); nevertheless, Murray and Pérez ( 2015 ) revealed that adaptive learning environments have a limited impact on learning outcome.

The restricted empirical findings on the efficacy of adapting teaching to learning style are mixed. (Chun-Hui et al., 2017 ) demonstrated that adaptive eLearning technologies can be beneficial to students' learning and development. According to these findings, adaptive eLearning can be considered a valuable method for learning because it can attract students' attention and promote their participation in educational activities. (Ali et al., 2019 ); however, only a few recent studies have focused on how adaptive eLearning based on learning styles fits in diverse cultural programs. (Benhamdi et al., 2017 ; Pashler et al., 2008 ).

The experimental results revealed that the proposed environment significantly increased students' learning achievements as compared to the conventional e-learning classroom (without adaptive technology). This means that the proposed environment's adaptation could increase students' engagement in the learning process. There is also evidence that an adaptive environment positively impacts other aspects of quality such as student engagement (Murray & Pérez, 2015 ).

Conclusions and implications

Although this field of research has stimulated many interests in recent years, there are still some unanswered questions. Some research gaps are established and filled in this study by developing an active adaptive e-learning environment that has been shown to increase student engagement. This study aimed to design an adaptive e-learning environment for performing interactive learning activities in a learning skills course. The main findings of this study revealed a significant difference in learning outcomes as well as positive results for adaptive e-learning students, indicating that it may be a helpful learning method for higher education. It also contributed to the current adaptive e-learning literature. The findings revealed that adaptive e-learning based on learning styles could help students stay engaged. Consequently, adaptive e-learning based on learning styles increased student engagement significantly. According to research, each student's learning style is unique, and they prefer to use different types of instructional materials and activities. Furthermore, students' preferences have an impact on the effectiveness of learning. As a result, the most effective learning environment should adjust its output to the needs of the students. The development of high-quality instructional materials and activities that are adapted to students' learning styles will help them participate and be more motivated. In conclusion, learning styles are a good starting point for creating instructional materials based on learning theories.

This study's results have important educational implications for future studies on the effect of adaptive e-learning on student interaction. First, the findings may provide data to support the development and improvement of adaptive environments used in blended learning. Second, the results emphasize the need for more quasi-experimental and descriptive research to better understand the benefits and challenges of incorporating adaptive e-learning in higher education institutions. Third, the results of this study indicate that using an adaptive model in an adaptive e-learning environment will encourage, motivate, engage, and activate students' active learning, as well as facilitate their knowledge construction, rather than simply taking in information passively. Fourth, new research is needed to design effective environments in which adaptive learning can be used in higher education institutions to increase academic performance and motivation in the learning process. Finally, the study shows that adaptive e-learning allows students to learn individually, which improves their learning and knowledge of course content, such as increasing their knowledge of learning skills course topics beyond what they can learn in a conventional e-learning classroom.

Contribution to research

The study is intended to provide empirical evidence of adaptive e-learning on student engagement factors. This research, on the other hand, has practical implications for higher education stakeholders, as it is intended to provide university faculty members with learning approaches that will improve student engagement. It is also expected to offer faculty a framework for designing personalized learning environments based on learning styles in various learning situations and designing more adaptive e-learning environments.

Research implication

Students with their preferred learning styles are more likely to enjoy learning if they are provided with a variety of instructional materials such as references, interactive media, videos, podcasts, storytelling, simulation, animation, problem-solving, games, and accessible educational tools in an e-learning environment. Also, different learning strategies can be accommodated. Other researchers would be able to conduct future studies on the use of the "adaptive e-learning" approach throughout the instructional process, at different phases of learning, and in various e-courses as a result of the current study. Meanwhile, the proposed environment's positive impact on student engagement gained considerable interest for future educational applications. Further research on learning styles in different university colleges could contribute to a foundation for designing adaptive e-courses based on students' learning styles and directing more future research on learning styles.

Implications for practice or policy:

Adaptive e-learning focused on learning styles would help students become more engaged.

Proving the efficacy of an adaptive e-learning environment via comparison with conventional e-learning .

Availability of data and materials

The author confirms that the data supporting the findings of this study are based on the research tools which were prepared and explained by the author and available on the links stated in the research instruments sub-section. The data analysis that supports the findings of this study is available on request from the corresponding author.

Akbulut, Y., & Cardak, C. (2012). Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Computers & Education . https://doi.org/10.1016/j.compedu.2011.10.008 .

Article   Google Scholar  

Al-Chalabi, H., & Hussein, A. (2020). Analysis & implementation of personalization parameters in the development of computer-based adaptive learning environment. SAR Journal Science and Research., 3 (1), 3–9. https://doi.org/10.18421//SAR31-01 .

Aldosari, M., Aljabaa, A., Al-Sehaibany, F., & Albarakati, S. (2018). Learning style preferences of dental students at a single institution in Riyadh Saudi Arabia, evaluated using the VARK questionnaire . Advances in Medical Education and Practice. https://doi.org/10.2147/AMEP.S157686 .

Ali, N., Eassa, F., & Hamed, E. (2019). Personalized Learning Style for Adaptive E-Learning System, International Journal of Advanced Trends in Computer Science and Engineering . 223-230. Retrieved June 26, 2020 from http://www.warse.org/IJATCSE/static/pdf/file/ijatcse4181.12019.pdf .

Alshammari, M., & Qtaish, A. (2019). Effective adaptive e-learning systems according to learning style and knowledge level. JITE Research, 18 , 529–547. https://doi.org/10.28945/4459 .

Alshammari, M. (2016). Adaptation based on learning style and knowledge level in e-learning systems, Ph.D. thesis , University of Birmingham.  Retrieved April 18, 2019 from http://etheses.bham.ac.uk//id/eprint/6702/ .

Alshammari, M., Anane, R., & Hendley, R. (2015). Design and Usability Evaluation of Adaptive E-learning Systems based on Learner Knowledge and Learning Style. Human-Computer Interaction Conference- INTERACT , Vol. (9297), (pp. 157–186). https://doi.org/10.1007/978-3-319-22668-2_45 .

Alzain, A., Clack, S., Jwaid, A., & Ireson, G. (2018a). Adaptive education based on learning styles: Are learning style instruments precise enough. International Journal of Emerging Technologies in Learning (iJET), 13 (9), 41–52. https://doi.org/10.3991/ijet.v13i09.8554 .

Alzain, A., Clark, S., Ireson, G., & Jwaid, A. (2018b). Learning personalization based on learning style instruments. Advances in Science Technology and Engineering Systems Journal . https://doi.org/10.25046/aj030315 .

Atherton, M., Shah, M., Vazquez, J., Griffiths, Z., Jackson, B., & Burgess, C. (2017). Using learning analytics to assess student engagement and academic outcomes in open access enabling programs”. Journal of Open, Distance and e-Learning, 32 (2), 119–136.

Barkley, E., & Major, C. (2020). Student engagement techniques: A handbook for college faculty . Jossey-Bass . 10:047028191X.

Google Scholar  

Becker, K., Kehoe, J., & Tennent, B. (2007). Impact of personalized learning styles on online delivery and assessment. Campus-Wide Information Systems . https://doi.org/10.1108/10650740710742718 .

Behaz, A., & Djoudi, M. (2012). Adaptation of learning resources based on the MBTI theory of psychological types. IJCSI International Journal of Computer Science, 9 (2), 135–141.

Beldagli, B., & Adiguzel, T. (2010). Illustrating an ideal adaptive e-learning: A conceptual framework. Procedia - Social and Behavioral Sciences, 2 , 5755–5761. https://doi.org/10.1016/j.sbspro.2010.03.939 .

Benhamdi, S., Babouri, A., & Chiky, R. (2017). Personalized recommender system for e-Learning environment. Education and Information Technologies, 22 , 1455–1477. https://doi.org/10.1007/s10639-016-9504-y .

Chen, P., Lambert, A., & Guidry, K. (2010). Engaging online learners: The impact of Web-based learning technology on college student engagement. Computers & Education, 54 , 1222–1232.

Chun-Hui, Wu., Chen, Y.-S., & Chen, T. C. (2017). An adaptive e-learning system for enhancing learning performance: based on dynamic scaffolding theory. Eurasia Journal of Mathematics, Science and Technology Education. https://doi.org/10.12973/ejmste/81061 .

Cletus, D., & Eneluwe, D. (2020). The impact of learning style on student performance: mediate by personality. International Journal of Education, Learning and Training. https://doi.org/10.24924/ijelt/2019.11/v4.iss2/22.47Desmond .

Cohen, J. (1992). Statistical power analysis. Current Directions in Psychological Science., 1 (3), 98–101. https://doi.org/10.1111/1467-8721.ep10768783 .

Daines, J., Troka, T. and Santiago, J. (2016). Improving performance in trigonometry and pre-calculus by incorporating adaptive learning technology into blended models on campus. https://doi.org/10.18260/p.25624 .

DeCapua, A. & Marshall, H. (2015). Implementing a Mutually Adaptive Learning Paradigm in a Community-Based Adult ESL Literacy Class. In M. Santos & A. Whiteside (Eds.). Low Educated Second Language and Literacy Acquisition. Proceedings of the Ninth Symposium (pps. 151-171). Retrieved Nov. 14, 2020 from https://www.researchgate.net/publication/301355138_Implementing_a_Mutually_Adaptive_Learning_Paradigm_in_a_Community-Based_Adult_ESL_Literacy_Class .

Dixson, M. (2015). Measuring student engagement in the online course: The online student engagement scale (OSE). Online Learning . https://doi.org/10.24059/olj.v19i4.561 .

Dominic, M., Xavier, B., & Francis, S. (2015). A Framework to Formulate Adaptivity for Adaptive e-Learning System Using User Response Theory. International Journal of Modern Education and Computer Science, 7 , 23. https://doi.org/10.5815/ijmecs.2015.01.04 .

El Bachari, E., Abdelwahed, E., & M., El. . (2011). E-Learning personalization based on Dynamic learners’ preference. International Journal of Computer Science and Information Technology., 3 , 200–216. https://doi.org/10.5121/ijcsit.2011.3314 .

El-Sabagh, H. A., & Hamed, E. (2020). The Relationship between Learning-Styles and Learning Motivation of Students at Umm Al-Qura University. Egyptian Association for Educational Computer Journal . https://doi.org/10.21608/EAEC.2020.25868.1015 ISSN-Online: 2682-2601.

Ennouamani, S., & Mahani, Z. (2017). An overview of adaptive e-learning systems. Eighth International ConfeRence on Intelligent Computing and Information Systems (ICICIS) . https://doi.org/10.1109/INTELCIS.2017.8260060 .

Evans, S., Steele, J., Robertson, S., & Dyer, D. (2017). Personalizing post titles in the online classroom: A best practice? Journal of Educators Online, 14 (2), 46–54.

Fleming, N., & Baume, D. (2006). Learning styles again: VARKing up the Right Tree! Educational Developments, 7 , 4–7.

Franzoni, A., & Assar, S. (2009). Student learning style adaptation method based on teaching strategies and electronic media. Journal of Educational Technology & Society , 12(4), 15–29. Retrieved March 21, 2020, from http://www.jstor.org/stable/jeductechsoci.12.4.15 .

Fredricks, J., Blumenfeld, P., & Paris, A. (2004). School Engagement: Potential of the Concept . State of the Evidence: Review of Educational Research. https://doi.org/10.3102/00346543074001059 .

Book   Google Scholar  

Gaytan, J., & McEwen, M. (2007). Effective Online Instructional and Assessment Strategies. American Journal of Distance Education, 21 (3), 117–132. https://doi.org/10.1080/08923640701341653 .

Graf, S. & Kinshuk. K. (2007). Providing Adaptive Courses in Learning Management Systems with respect to Learning Styles. Proceeding of the World Conference on eLearning in Corporate. Government. Healthcare. and Higher Education (2576–2583). Association for the Advancement of Computing in Education (AACE). Retrieved January 18, 2020 from  https://www.learntechlib.org/primary/p/26739/ . ISBN 978-1-880094-63-1.

Guo, P., Kim, V., & Rubin, R. (2014). How video production affects student engagement: an empirical study of MOOC videos. Proceedings of First ACM Conference on Learning @ Scale Confernce . March 2014, (pp. 41-50). https://doi.org/10.1145/2556325.2566239 .

Hinton, P. R., Brownlow, C., McMurray, I., & Cozens, B. (2014). SPSS Explained (2nd ed., pp. 339–354). Routledge Taylor & Francis Group.

Hong, S. (2009). Developing competency model of learners in distance universities. Journal of Educational Technology., 25 , 157–186.

Hussain, I. (2017). Pedagogical implications of VARK model of learning. Journal of Literature, Languages and Linguistics, 38 , 33–37.

Hussain, M., Zhu, W., Zhang, W., & Abidi, S. (2018). Student engagement predictions in an e-learning system and their impact on student course assessment scores. Computational Intelligence, and Neuroscience. https://doi.org/10.1155/2018/6347186 .

Hussein, A., & Al-Chalabi, H. (2020). Pedagogical Agents in an Adaptive E-learning System. SAR Journal of Science and Research., 3 , 24–30. https://doi.org/10.18421/SAR31-04 .

Jaleel, S., & Thomas, A. (2019). Learning styles theories and implications for teaching learning . Horizon Research Publishing. 978-1-943484-25-6.

Johnson, M. (2009). Evaluation of Learning Style for First-Year Medical Students. Int J Schol Teach Learn . https://doi.org/10.20429/ijsotl.2009.030120 .

Jonassen, D. H., & Grabowski, B. L. (2012). Handbook of individual differences, learning, and instruction. Routledge . https://doi.org/10.1016/0022-4405(95)00013-C .

Klasnja-Milicevic, A., Vesin, B., Ivanovic, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56 (3), 885–899. https://doi.org/10.1016/j.compedu.2010.11.001 .

Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2017). Prediction of learner’s profile based on learning styles in adaptive e-learning system. International Journal of Emerging Technologies in Learning, 12 (6), 31–51. https://doi.org/10.3991/ijet.v12i06.6579 .

Lee, J., & Kim, D. (2012). Adaptive learning system applied bruner’ EIS theory. International Conference on Future Computer Supported Education, IERI Procedia, 2 , 794–801. https://doi.org/10.1016/j.ieri.2012.06.173 .

Lee, J., Song, H.-D., & Hong, A. (2019). Exploring factors, and indicators for measuring students’ sustainable engagement in e-learning. Sustainability, 11 , 985. https://doi.org/10.3390/su11040985 .

Leung, A., McGregor, M., Sabiston, D., & Vriliotis, S. (2014). VARK learning styles and student performance in principles of Micro-vs. Macro-Economics. Journal of Economics and Economic Education Research, 15 (3), 113.

Lévy, P. & Wakabayashi, N. (2008). User's appreciation of engagement in service design: The case of food service design. Proceedings of International Service Innovation Design Conference 2008 - ISIDC08 . Busan, Korea. Retrieved October 28, 2019 from https://www.researchgate.net/publication/230584075 .

Liang, J. S. (2012). The effects of learning styles and perceptions on application of interactive learning guides for web-based. Proceedings of Australasian Association for Engineering Education Conference AAEE . Melbourne, Australia. Retrieved October 22, 2019 from https://aaee.net.au/wpcontent/uploads/2018/10/AAEE2012-Liang.-Learning_styles_and_perceptions_effects_on_interactive_learning_guide_application.pdf .

Mahnane, L., Laskri, M. T., & Trigano, P. (2013). A model of adaptive e-learning hypermedia system based on thinking and learning styles. International Journal of Multimedia and Ubiquitous Engineering, 8 (3), 339–350.

Markey, M. K. & Schmit, K, J. (2008). Relationship between learning style Preference and instructional technology usage. Proceedings of American Society for Engineering Education Annual Conference & Expodition . Pittsburgh, Pennsylvania. Retrieved March 15, 2020 from https://peer.asee.org/3173 .

McMillan, J., & Schumacher, S. (2006). Research in education: Evidence-based inquiry . Pearson.

Murphy, R., Gray, S., Straja, S., & Bogert, M. (2004). Student learning preferences and teaching implications: Educational methodologies. Journal of Dental Education, 68 (8), 859–866.

Murray, M., & Pérez, J. (2015). Informing and performing: A study comparing adaptive learning to traditional learning. Informing Science. The International Journal of an Emerging Transdiscipline , 18, 111–125. Retrieved Febrauary 4, 2021 from http://www.inform.nu/Articles/Vol18/ISJv18p111-125Murray1572.pdf .

Mutahi, J., Kinai, A. , Bore, N. , Diriye, A. and Weldemariam, K. (2017). Studying engagement and performance with learning technology in an African classroom, Proceedings of Seventh International Learning Analytics & Knowledge Conference , (pp. 148–152), Canada: Vancouver.

Nainie, Z., Siraj, S., Abuzaiad, R. A., & Shagholi, R. (2010). Hypothesized learners’ technology preferences based on learning styles dimensions. The Turkish Online Journal of Educational Technology, 9 (4), 83–93.

Naqeeb, H. (2011). Learning Styles as Perceived by Learners of English as a Foreign Language in the English Language Center of The Arab American University—Jenin. Palestine. an Najah Journal of Research, 25 , 2232.

Nkomo, L. M., Daniel, B. K., & Butson, R. J. (2021). Synthesis of student engagement with digital technologies: a systematic review of the literature. International Journal of Educational Technology in Higher Education . https://doi.org/10.1186/s41239-021-00270-1 .

Normadhi, N. B., Shuib, L., Nasir, H. N. M., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers & Education, 130 , 168–190. https://doi.org/10.1016/j.compedu.2018.11.005 .

Nuankaew, P., Nuankaew, W., Phanniphong, K., Imwut, S., & Bussaman, S. (2019). Students model in different learning styles of academic achievement at the University of Phayao, Thailand. International Journal of Emerging Technologies in Learning (iJET)., 14 , 133. https://doi.org/10.3991/ijet.v14i12.10352 .

Oxman, S. & Wong, W. (2014). White Paper: Adaptive Learning Systems. DV X Innovations DeVry Education Group. Retrieved December 14, 2020 from shorturl.at/hnsS8 .

Ozyurt, Ö., & Ozyurt, H. (2015). Learning style-based individualized adaptive e-learning environments: Content analysis of the articles published from 2005 to 2014. Computers in Human Behavior, 52 , 349–358. https://doi.org/10.1016/j.chb.2015.06.020 .

Pardo, A., Han, F., & Ellis, R. (2016). Exploring the relation between self-regulation, online activities, and academic performance: a case study. Proceedings of Sixth International Conference on Learning Analytics & Knowledge , (pp. 422-429). https://doi.org/10.1145/2883851.2883883 .

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: concepts and evidence. Psychology Faculty Publications., 9 (3), 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x .

Qazdar, A., Cherkaoui, C., Er-Raha, B., & Mammass, D. (2015). AeLF: Mixing adaptive learning system with learning management system. International Journal of Computer Applications., 119 , 1–8. https://doi.org/10.5120/21140-4171 .

Robinson, C., & Hullinger, H. (2008). New benchmarks in higher education: Student engagement in online learning. Journal of Education for Business, 84 , 101–109.

Rogers-Stacy, C., Weister, T., & Lauer, S. (2017). Nonverbal immediacy behaviors and online student engagement: Bringing past instructional research into the present virtual classroom. Communication Education, 66 (1), 37–53.

Roy, S., & Roy, D. (2011). Adaptive e-learning system: a review. International Journal of Computer Trends and Technology (IJCTT), 1 (1), 78–81. ISSN:2231-2803.

Shi, L., Cristea, A., Foss, J., Qudah, D., & Qaffas, A. (2013). A social personalized adaptive e-learning environment: a case study in topolor. IADIS International Journal on WWW/Internet., 11 , 13–34.

Shih, M., Feng, J., & Tsai, C. (2008). Research and trends in the field of e-learning from 2001 to 2005: A content analysis of cognitive studies in selected journals. Computers & Education, 51 (2), 955–967. https://doi.org/10.1016/j.compedu.2007.10.004 .

Silva, A. (2020). Towards a Fuzzy Questionnaire of Felder and Solomon for determining learning styles without dichotomic in the answers. Journal of Learning Styles, 13 (15), 146–166.

Staikopoulos, A., Keeffe, I., Yousuf, B. et al., (2015). Enhancing student engagement through personalized motivations. Proceedings of IEEE 15th International Conference on Advanced Learning Technologies , (pp. 340–344), Taiwan: Hualien. https://doi.org/10.1109/ICALT.2015.116 .

Surjono, H. D. (2014). The evaluation of Moodle-based adaptive e-learning system. International Journal of Information and Education Technology, 4 (1), 89–92. https://doi.org/10.7763/IJIET.2014.V4.375 .

Truong, H. (2016). Integrating learning styles and adaptive e-learning system: current developments, problems, and opportunities. Computers in Human Behavior, 55 (2016), 1185–1193. https://doi.org/10.1016/j.chb.2015.02.014 .

Umm Al-Qura University Agency for Educational Affairs (2020). Common first-year Deanship, at Umm Al-Qura University. Retrieved February 3, 2020 from https://uqu.edu.sa/en/pre-edu/70021 .

Vassileva, D. (2012). Adaptive e-learning content design and delivery based on learning style and knowledge level. Serdica Journal of Computing, 6 , 207–252.

Veiga, F., Robu, V., Appleton, J., Festas, I & Galvao, D. (2014). Students' engagement in school: Analysis according to self-concept and grade level. Proceedings of EDULEARN14 Conference 7th-9th July 2014 (pp. 7476-7484). Barcelona, Spain. Available Online at: http://hdl.handle.net/10451/12044 .

Velázquez, A., & Assar, S. (2009). Student learning styles adaptation method based on teaching strategies and electronic media. Educational Technology & SocieTy., 12 , 15–29.

Verdú, E., Regueras, L., & De Castro, J. (2008). An analysis of the research on adaptive Learning: The next generation of e-learning. WSEAS Transactions on Information Science and Applications, 6 (5), 859–868.

Willingham, D., Hughes, E., & Dobolyi, D. (2015). The scientific status of learning styles theories. Teaching of Psychology., 42 (3), 266–271. https://doi.org/10.1177/0098628315589505 .

Yalcinalp & Avcı. (2019). Creativity and emerging digital educational technologies: A systematic review. The Turkish Online Journal of Educational Technology, 18 (3), 25–45.

Yang, J., Huang, R., & Li, Y. (2013). Optimizing classroom environment to support technology enhanced learning. In A. Holzinger & G. Pasi (Eds.), Human-computer interaction and knowledge discovery in complex (pp. 275–284). Berlin: Springer.

Zhang, H. (2017). Accommodating different learning styles in the teaching of economics: with emphasis on fleming and mills¡¯s sensory-based learning style typology. Applied Economics and Finance, 4 (1), 72–78.

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Acknowledgements

The author would like to thank the Deanship of Scientific Research at Umm Al-Qura University for the continuous support. This work was supported financially by the Deanship of Scientific Research at Umm Al-Qura University to Dr.: Hassan Abd El-Aziz El-Sabagh. (Grant Code: 18-EDU-1-01-0001).

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Hassan A. El-Sabagh is an assistant professor in the E-Learning Deanship and head of the Instructional Programs Department, Umm Al-Qura University, Saudi Arabia, where he has worked since 2012. He has extensive experience in the field of e-learning and educational technologies, having served primarily at the Educational Technology Department of the Faculty of Specific Education, Mansoura University, Egypt since 1997. In 2011, he earned a Ph.D. in Educational Technology from Dresden University of Technology, Germany. He has over 14 papers published in international journals/conference proceedings, as well as serving as a peer reviewer in several international journals. His current research interests include eLearning Environments Design, Online Learning; LMS-based Interactive Tools, Augmented Reality, Design Personalized & Adaptive Learning Environments, and Digital Education, Quality & Online Courses Design, and Security issues of eLearning Environments. (E-mail: [email protected]; [email protected]).

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El-Sabagh, H.A. Adaptive e-learning environment based on learning styles and its impact on development students' engagement. Int J Educ Technol High Educ 18 , 53 (2021). https://doi.org/10.1186/s41239-021-00289-4

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Posted on April 28, 2016

Learning styles: what does the research say?

Dylan Wiliam

Category: Cognitive Science

This post is the third in a periodic series exploring common misconceptions around how students learn. We first touched on these misconceptions in our September 2015 report,  The Science of Learning , and will be exploring them in more depth over the next few months.

In today’s post, Dr. Dylan Wiliam explores what the research tells us about learning styles. Dylan Wiliam is Emeritus Professor of Educational Assessment at the Institute of Education, University College London. He served as dean and head of the School of Education (and later assistant principal) at King’s College London, senior research director at the Educational Testing Service in Princeton, New Jersey, and deputy director (provost) of the Institute of Education, University of London. Since 2010, he has devoted most of his time to research and teaching.

Since the beginning of Psychology as a field of study, psychologists have been categorizing people: as introverts and extroverts, in terms of their conscientiousness, their openness to experience, and so on. While many of these classification systems examine general personality, a number of classifications look specifically at the way people think—what is sometimes called their cognitive style. When solving problems, for example, some people like to focus on getting the evidence that is most likely to be relevant to the problem at hand, while others have a tendency to “think out of the box.”

More specifically still, many psychologists have moved from cognitive style—how people think—to the idea of learning style—how people learn (Adey, Fairbrother, Wiliam, Johnson, & Jones, 1999).

The basic idea is, of course, very attractive. We know that a particular piece of instruction might be effective for some students, and not for others, so it seems plausible that if the instruction was specifically designed to take into account a particular student’s preferred learning style, then it would be more effective for that student. This is what psychologists call the general learning-styles hypothesis—the idea that instruction students receive will be more (or less) effective if the instruction takes (or does not take) into account the student’s learning-style preferences.

Within education, a version of the learning-styles hypothesis, known by psychologists as the  meshing  hypothesis, has been of particular interest: the idea that students will learn more if they receive instruction that specifically matches their learning-style preferences. In other words, visual learners will learn better if they receive instruction that emphasizes visual ways of presenting information, and auditory learners will learn best by listening.

In their review of research on learning styles for the Association for Psychological Science, Pashler, McDaniel, Rohrer, and Bjork (2008) came to a stark conclusion: “If classification of students’ learning styles has practical utility, it remains to be demonstrated.” (p. 117)

Pashler et al pointed out that experiments designed to investigate the meshing hypothesis would have to satisfy three conditions:

• Based on some assessment of their presumed learning style, learners would be allocated to two or more groups (e.g., visual, auditory and kinesthetic learners).
• Learners within each of the learning-style groups would be randomly allocated to at least two different methods of instruction (e.g., visual and auditory based approaches).
• All students in the study would be given the same final test of achievement.

In such experiments, the meshing hypothesis would be supported if the results showed that the learning method that optimizes test performance of one learning-style group is different than the learning method that optimizes the test performance of a second learning-style group.

In their review, Pashler et al found only one study that gave even partial support to the meshing hypothesis, and two that clearly contradicted it.

Now, the fact that there is currently no evidence that knowing students’ learning styles helps us design more effective instruction does not mean that learning styles will never be useful in the future—absence of evidence is not the same as evidence of absence. Some psychologists are no doubt likely to continue to look for new ways to look at learning styles, even though there are at least 71 different learning-style classification systems already in existence (Coffield, Moseley, Hall, & Ecclestone, 2004).  However, it could be that the whole idea of learning-styles research is misguided because its basic assumption—that the purpose of instructional design is to make learning easy—may just be incorrect.

Over the last 30 years, psychologists have found that performance on a learning task is a poor predictor of long-term retention. More precisely, when learners do well on a learning task, they are likely to forget things more quickly than if they do badly on the learning task; good instruction creates “desirable difficulties” (Bjork, 1994 p. 193) for the learner. In Daniel Willingham’s memorable phrase, “memory is the residue of thought” (Willingham, 2009). By trying to match our instruction to our students’ preferred learning style, we may, in fact, be reducing learning. If students do not have to work hard to make sense of what they are learning, then they are less likely to remember it in six weeks’ time.

Attempting to synthesize such a large and complex body of research is almost certainly a fool’s errand, but it seems to me that the important “takeaway” from the research on learning styles is that  teachers need to know about learning styles if only to avoid the trap of teaching in the style they believe works best for them.  As long as teachers are varying their teaching style, then it is likely that all students will get some experience of being in their comfort zone and some experience of being pushed beyond it. Ultimately, we have to remember that teaching is interesting because our students are so different, but only possible because they are so similar. Of course each of our students is a unique individual, but it is extraordinary how effective well-planned group instruction can be.

Adey, P. S., Fairbrother, R. W., Wiliam, D., Johnson, B., & Jones, C. (1999).  A review of research related to learning styles and strategies . London, UK: King’s College London Centre for the Advancement of Thinking.

Bjork, R. A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe & A. P. Shimamura (Eds.),  Metacognition: Knowing about knowing  (pp. 188-205). Cambridge, MA: MIT Press.

Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004).  Learning styles and pedagogy in post-16 learning: A systematic and critical review . London, UK: Learning and Skills Development Agency.

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. A. (2008). Learning styles: Concepts and evidence.  Psychological Science in the Public Interest, 9 (3), 105-119.

Willingham, D. T. (2009).  Why don’t students like school: A cognitive scientist answers questions about how the mind works and what it means for your classroom . San Francisco, CA: Jossey-Bass.

Learning Styles Debunked: There is No Evidence Supporting Auditory and Visual Learning, Psychologists Say

• Auditory Perception
• Child Development
• Cognitive Psychology
• Learning Styles
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• Psychological Science in the Public Interest
• Visual Perception

Are you a verbal learner or a visual learner? Chances are, you’ve pegged yourself or your children as either one or the other and rely on study techniques that suit your individual learning needs. And you’re not alone— for more than 30 years, the notion that teaching methods should match a student’s particular learning style has exerted a powerful influence on education. The long-standing popularity of the learning styles movement has in turn created a thriving commercial market amongst researchers, educators, and the general public.

The wide appeal of the idea that some students will learn better when material is presented visually and that others will learn better when the material is presented verbally, or even in some other way, is evident in the vast number of learning-style tests and teaching guides available for purchase and used in schools. But does scientific research really support the existence of different learning styles, or the hypothesis that people learn better when taught in a way that matches their own unique style?

Unfortunately, the answer is no, according to a major report published in Psychological Science in the Public Interest , a journal of the Association for Psychological Science. The report, authored by a team of eminent researchers in the psychology of learning—Hal Pashler (University of San Diego), Mark McDaniel (Washington University in St. Louis), Doug Rohrer (University of South Florida), and Robert Bjork (University of California, Los Angeles)—reviews the existing literature on learning styles and finds that although numerous studies have purported to show the existence of different kinds of learners (such as “auditory learners” and “visual learners”), those studies have not used the type of randomized research designs  that would make their findings credible.

Nearly all of the studies that purport to provide evidence for learning styles fail to satisfy key criteria for scientific validity. Any experiment designed to test the learning-styles hypothesis would need to classify learners into categories and then randomly assign the learners to use one of several different learning methods, and the participants would need to take the same test at the end of the experiment. If there is truth to the idea that learning styles and teaching styles should mesh, then learners with a given style, say visual-spatial, should learn better with instruction that meshes with that style. The authors found that of the very large number of studies claiming to support the learning-styles hypothesis, very few used this type of research design.  Of those that did, some provided evidence flatly contradictory to this meshing hypothesis, and the few findings in line with the meshing idea did not assess popular learning-style schemes.

No less than 71 different models of learning styles have been proposed over the years. Most have no doubt been created with students’ best interests in mind, and to create more suitable environments for learning. But psychological research has not found that people learn differently, at least not in the ways learning-styles proponents claim. Given the lack of scientific evidence, the authors argue that the currently widespread use of learning-style tests and teaching tools is a wasteful use of limited educational resources.

Could you please direct me to the source material for this? Thank you.

I found the study here: https://www.psychologicalscience.org/journals/pspi/PSPI_9_3.pdf

The study is here: http://www.psychologicalscience.org/journals/pspi/PSPI_9_3.pdf

I doubt a valid study could be created. There are too many variables. I expect we learn by a combination of all inputs. How could a study overcome the issues of quality of the teachers’ presentation, quality of visuals used compared to quality of auditory materials?

Larry, speaking as a statistics student, I’ll propose an answer to the issue of how a “valid study” can be designed. Feel free to call me out if there is an inherent flaw with my proposal.

I will be referring to American students specifically since this is an issue debated for the American school system. I assume the author is talking about the same thing, but I’ll admit I don’t know if this teaching idea is prevalent in other countries. For the sake of this argument, it really doesn’t matter anyways as this variable is easily changed.

The sample is the most difficult part here, I expect there to be a lot of chosen students who’s parents do not wish their children to be a part of the study for some reason or another. It would also have to be conducted locally, or over a short period of time, though doing it locally would have a greater chance of acceptance among chosen participants. The greatest effort should be made to account for demographics, but, again, this would be difficult. (^Not a great way to start, apologies, but I’m sure a seasoned statistician could come up with the solution that I’m afraid I can’t)

Now, you have your grouping of students, say 1,500 for a reasonable number that would provide relatively a relatively small margin of error. Split each of these students into groups of 500, and assign them to a 25 student-per-teacher classroom that each taught only through auditory, visual, or “hands-on” learning. The students are specifically instructed not to take notes. For this example, let’s say they are learning the properties of liquids. The visual classes are taught through packets that each student is given. The “hands-on” class is given a sheet instructing them how to perform a lab and giving them blanks to fill in. Obviously, for this one, a teacher will tell them how to properly handle equipment and said equipment will be protected against the children hurting themselves inadvertently.(ie, no bunsen burners, but maybe a low-heat burner with students only able to turn it on/off and not touch the hot surface) The hearing group will be given a lecture on the subject, with questions being allowed afterward. After a few days learning this way, every student in every class would be given the same test. Then they would all switch, this time learning about the properties of a solid through the same methods, before being tested on it. Lastly, they would switch to learning and testing on the properties of a gas. As a control, through the same selection process, 500 students could be selected to be taught using all three of the described methods in the same timeframe. That is, instead of a packet, a lecture, or a lab, they could receive a lecture while being shown a powerpoint, followed by a lab.

To prevent previous learning bias, I would suggest all students in the sampling population be the same age, while having not received formal education prior. Also, every student should be taught to use the equipment before the experiment so that the “hands-on” group wouldn’t be at an initial disadvantage.

I’m not a teacher, a psychologist, or a professional statistician. This is just my proposal using my current knowledge of statistics. Take it with a grain of salt and form your own opinions, this is simply being put forth in the effort to show that such an experiment seems to be viable given the proper infrastructure and coordination.

Of course, your method makes sense, but it borders on unethical because it is wrong to teach a child anything in a way that they will not understand. I’m not saying you are unethical, but that any scheme that teaches inappropriately (“don’t take notes”) for more than 5 minutes is unethical.

What a bunch of arrogant people to think that they know if there exists one learning style…!? The only learning style we know is the one in our head. How can you say that there is no other creative ways of learning? What about Autistic people? What about Blind people? What about Deaf people? And Bipolar people? And what about Dyslexic people? And people who have a part of verbal speech comprehensions damages in their brain???? Why give so much importance to a little psychology paper? Any body can do a 3 year psychology degree and then write a paper claiming blabla bla

That’s not what they’re saying at all. They’re saying that there are no categories, or boxes, that people can be put in based on their learning style. They’re not saying there is just one way to learn. No need to get so worked up. People with damage to specific parts of their brains or sensory organs are obviously the outlier. Obviously they are going to be radically different.

And publishing a paper in an esteemed journal takes a _little_ bit more than a 3 year BSc in psychology. It’s that comment that really reveales the depth of your ignorance.

As someone diagnosed with high-functioning autism and currently in a concurrent education course, it is much more dangerous to tell someone they should be okay with only learning in one way rather than teaching them to be flexible and learn to absorb information from all sorts of mediums. So I’m gonna assume you’re blind, dyslexic, and autistic because you’ve assumed you can speak for all of them, yes? Your example of someone being blind also helps to further disprove learning theory — which implies nature over nurture — because clearly the ‘visual’ learners who are rendered blind must learn to learn in a different way (which statistically is shown to affect their learning no differently).

…SOMEBODY doesn’t at all understand the scientific method, reasoning or science in general.

I hope that we can finally move past these always dubious “sensory” learning styles. They’re really “modes,” different ways of learning. I’ve long argued that anyone who feels weak at using any of them needs to practice using that mode more, not less. But another old branch of learning styles based on differing neurotransmitter biases seemed to have better prospects, even if I’ve seen little done with it for decades now. I hope we don’t toss out the entire learning style baby with the dirty “sensory style” bathwater. With our updated technology, we could probably go much farther with it. For background, see dated and rather poorly written but better reasoned explanatory work by Jane Gear.

“I’ve long argued that anyone who feels weak at using any of them needs to practice using that mode more, not less.” As a kid already struggling through school with learning disabilities and the resulting long term stress and exhaustion the last thing I needed was to make things more difficult.

Allow me to state categorically that there are learning styles of which to speak specific to learners. To get the issue on hand, the methods proposed by these researchers as a way to disregard the widespread validity or to invalidate the validity of learning proclivities as a concept is not only inapposite, but also akin to saying that every learner approaches the universe of learning in the exact same way. If that is the measure of what we are to agree on as what constitutes scientific efficacy on any issue, then all forms of research are mitigatable and a suspect in the sense of their nature, methods, outcomes, and overall usefulness.

Such a view to research pieces is clearly misguided, ill-informed and half-scientific … even from a commonsense perspective. It serves no social and scientific utility, but for the interest of the investigators.

Mind you, we are not referring to the efficacy of styles presumptive of or correlative to bettering grade acquisition; rather that it should be argued that there are humane, less torturous, comfortable, less arduous and even naturalistic way of teaching students by emphasizing their uniquely preferred styles, wherever determinable.

Even where indeterminable, instructors are to be encouraged to vary their teaching methods to accommodate the learning needs of their captive audience, in this case, their students, and especially not to think that students learn essentially in the very same way as, for example, their instructors.

To think that all learners learn the same way whether in styles or approaches and to even suppose that instruction is a form of a “straight-jacket” and should work with all “body sizes” is in itself a form of miseducation, misrepresentation and,or a type of stiff recalcitrance that should not ever conduce to the mind of an educator, much less a group of psychologists.

Conbach and Snow’s [in the 60’s] work on learning differences, along with findings affecting Trait/Factor analysis are some of few materials that may well serve as enviable pivots for the current exchange.

When it comes to research concerning learning styles…the human dynamics of learning is so complex that attempting to isolate independent variables that may affect learning is like trying to determine the direction of an automobile by studying petroleum chemistry.

The big problem of understanding this is that people don’t focus on the clear and precise language being used, and don’t understand how experimental science works.

What is being said is that “learning styles” theories which denote specific “auditory” and “visual” learning styles do not have any scientific evidence for them. Those who are evaluated to be predominantly “auditory” in terms of a “learning style” do not in fact perform better or differently when taught “visually” and vice versa.

This is important, because while it seems intuitively true that some people might learn better with a specific medium, there is no evidence for it. What there is evidence for is the superiority of multi-modal or multi-media instruction, in terms of learning outcomes.

The main point is don’t waste time on something that has no evidence to support it. See a ranking of effect size on educational reforms to see what is most important, and what is least: https://visible-learning.org/hattie-ranking-influences-effect-sizes-learning-achievement/

I am currently studying to be an ESL teacher and have come across these “learning styles” with in the course. I do have a rather concerning view about them.

I can see that many minds are put behind how we are going to teach and get the “message” across to learners, but sadly i feel like there is an overdose of ego on “who has the better way to teach”. I know that’s a pretty heavy assumption but i can’t conclude much else except maybe there is a fear that the future generation may not learn correctly, which if this is the case, this manifests into over thinking techniques and deviding the way how individuals learn. I do however believe that segregating ways in which people learn is crazy and an over analysed attempt.

As i was studying this i couldn’t help but scrunch my nose in confusion when alot of the individual “learning styles” were something that i have as a “whole” and as an “individual”. I strongly believe that everything works hand in hand.

If i was to simply hold up a picture of someone playing golf and not attach a word or action to it, they would simply know what it looks like but not know what to call it or how it works. Auditory and kinaesthetic would be eliminated and the student will be deprived. But what concerns me is, that i would be compelled to put action to something like this(in a teachers mind) and tell them what we call it (golf). So to be segregating “learning styles” you must be going against a law within your conscience as to how we ALL “learn” this seriously is a no brainer for me.

I must say though not everything is based on science, simply using your brain can solve many of complications. I say that encouragingly not as a rivalry. Hope this was helpful.

Teaching golf would integrate all 4 learning styles. Why not use kinesthetic methods to complement visual, auditory, and logical when appropriate?

Howdy folks! I’d like to understand this a little better. So these guys invalidated all of the studies because they didn’t meet their standard and for that reason they declare that everyone has the same learning style? Is that what they are saying? I don’t see that they setup and carried out a scientific study that meets their own criteria to prove their hypothesis that we all the same learning style. Did I miss something there? Let’s just say the science wasn’t good enough as they say, then that only means that the science hasn’t proved anything. If the science isn’t good enough to prove it right…. then I’m thinking that it doesn’t prove it wrong either. Wouldn’t that just mean that the hypothesis just remains unproven? I wonder too if someone can explain what learning style I’m using when I’m learning how to play my drums? So I’m trying to learn a double stroke roll and feeling the stick bounce and snapping my fingers and wrist at the right moment… it’s all about the feel. To me that’s my kinesthetic learning channel. I’m programming my “muscle memory” is yet another frame for explaining it. Does their conclusion invalidate this learning channel? When it comes to learning songs, I listen by sound. I listen and repeat. I have friends who can only play along with sheet music. They read and play. I didn’t carry out a study to figure this out. I just talk to other drummers and there’s clearly 2 sets of learning styles right there. Many drummers can only sight read. I can’t. I ask then… how is this possible if we all have the same learning style? And the argument is that we should stop wasting money trying to make education better? Really? I think I’ll disengage my gullible learning style and turn on my critical thinking style. …or does that not exist either?

You have to learn to read sheet music , just like reading a book. but it takes effort and once you learn it is good. learning by ear is more natural and maybe you will be more creative because music is audio. The Beatles could not read music. They seem to be saying it has not been determined if audio or visual leaning styles exist. Not whether one is better than the other and if we don’t know if they exist then why spend money behind them. You could invent many other plausible teaching methods and theories and spend a lot of money but maybe the best money spent is on things we know make a difference.

I dont what most people in here are even talking about. Scientific research? In the end it comes down to enjoyment.

The individual is as diverse from one another both in appearance and behaviours. It is not been proven that learning styles are debunked, only that on review by some eminent scientists, a shadow of doubt challenges the premise. Thus if we are diverse creatures it follows we will take in the world in diverse ways, some of us will have more developed auditory facilities, some hardwiring may mean visuals are easier – this is not a study but a fact. We as humans do everything differently than others, perhaps the universal categories should not be bandied about carelessly. But in education in particular, we certainly do take our world in in many and varied forms, construct how we see it and enact a life we see fit, all embedded in our social environment

I was encouraged that the psychologists got put in their place by those of us(teachers) who understand that all children learn differently. Why would you want to frustrate any child with visual learning material that leads to nothing but failure, when the same child can find success with teaching methods that match the child’s learning style?

Byron Thorne author of Toward A Failure-Proof Methodology for Learning To Read.

I love the article and the follow up debate. As a long time educator and student of education it is positive to see all the different perspectives. How to make things manageable for learners? Multi-modal presentations with options for showing one’s understanding and learning. Any teaching can be presented in various ways concurrently as long as we give the students what they need to have access to. My question would be more about how to best engage students so they would be engaged and self-motivated. Love the conversation.

I don’t agree or believe you! I am a visual and auditory learner. It works for me! I was a teacher and everyone has preferred learning styles. Some people do better with a snack. Some are tactile. Your study may be flawed but your conclusions are wrong.c Nance

They can say what they like, but I have seen very different foci in various individuals, with the same-system adherants failing miserably, more often than not, relative to more flexible instructors. I myself cannot grasp complex ideas without first having, or mentally generating, a visual reference.

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Learning styles, what are learning styles, why are they so popular.

The term  learning styles is widely used to describe how learners gather, sift through, interpret, organize, come to conclusions about, and “store” information for further use.  As spelled out in VARK (one of the most popular learning styles inventories), these styles are often categorized by sensory approaches:   v isual, a ural, verbal [ r eading/writing], and k inesthetic.  Many of the models that don’t resemble the VARK’s sensory focus are reminiscent of Felder and Silverman’s Index of Learning Styles , with a continuum of descriptors for how learners process and organize information:  active-reflective, sensing-intuitive, verbal-visual, and sequential-global.

There are well over 70 different learning styles schemes (Coffield, 2004), most of which are supported by “a thriving industry devoted to publishing learning-styles tests and guidebooks” and “professional development workshops for teachers and educators” (Pashler, et al., 2009, p. 105).

Despite the variation in categories, the fundamental idea behind learning styles is the same: that each of us has a specific learning style (sometimes called a “preference”), and we learn best when information is presented to us in this style.  For example, visual learners would learn any subject matter best if given graphically or through other kinds of visual images, kinesthetic learners would learn more effectively if they could involve bodily movements in the learning process, and so on.  The message thus given to instructors is that “optimal instruction requires diagnosing individuals’ learning style[s] and tailoring instruction accordingly” (Pashler, et al., 2009, p. 105).

Despite the popularity of learning styles and inventories such as the VARK, it’s important to know that there is no evidence to support the idea that matching activities to one’s learning style improves learning .  It’s not simply a matter of “the absence of evidence doesn’t mean the evidence of absence.”  On the contrary, for years researchers have tried to make this connection through hundreds of studies.

In 2009, Psychological Science in the Public Interest commissioned cognitive psychologists Harold Pashler, Mark McDaniel, Doug Rohrer, and Robert Bjork to evaluate the research on learning styles to determine whether there is credible evidence to support using learning styles in instruction.  They came to a startling but clear conclusion:  “Although the literature on learning styles is enormous,” they “found virtually no evidence” supporting the idea that “instruction is best provided in a format that matches the preference of the learner.”  Many of those studies suffered from weak research design, rendering them far from convincing.  Others with an effective experimental design “found results that flatly contradict the popular” assumptions about learning styles (p. 105). In sum,

“The contrast between the enormous popularity of the learning-styles approach within education and the lack of credible evidence for its utility is, in our opinion, striking and disturbing” (p. 117).

Pashler and his colleagues point to some reasons to explain why learning styles have gained—and kept—such traction, aside from the enormous industry that supports the concept.  First, people like to identify themselves and others by “type.” Such categories help order the social environment and offer quick ways of understanding each other.  Also, this approach appeals to the idea that learners should be recognized as “unique individuals”—or, more precisely, that differences among students should be acknowledged —rather than treated as a number in a crowd or a faceless class of students (p. 107). Carried further, teaching to different learning styles suggests that “ all people have the potential to learn effectively and easily if only instruction is tailored to their individual learning styles ” (p. 107).

There may be another reason why this approach to learning styles is so widely accepted. They very loosely resemble the concept of metacognition , or the process of thinking about one’s thinking.  For instance, having your students describe which study strategies and conditions for their last exam worked for them and which didn’t is likely to improve their studying on the next exam (Tanner, 2012).  Integrating such metacognitive activities into the classroom—unlike learning styles—is supported by a wealth of research (e.g., Askell Williams, Lawson, & Murray-Harvey, 2007; Bransford, Brown, & Cocking, 2000; Butler & Winne, 1995; Isaacson & Fujita, 2006; Nelson & Dunlosky, 1991; Tobias & Everson, 2002).

Importantly, metacognition is focused on planning, monitoring, and evaluating any kind of thinking about thinking and does nothing to connect one’s identity or abilities to any singular approach to knowledge.  (For more information about metacognition, see CFT Assistant Director Cynthia Brame’s “ Thinking about Metacognition ” blog post, and stay tuned for a Teaching Guide on metacognition this spring.)

There is, however, something you can take away from these different approaches to learning—not based on the learner, but instead on the content being learned .  To explore the persistence of the belief in learning styles, CFT Assistant Director Nancy Chick interviewed Dr. Bill Cerbin, Professor of Psychology and Director of the Center for Advancing Teaching and Learning at the University of Wisconsin-La Crosse and former Carnegie Scholar with the Carnegie Academy for the Scholarship of Teaching and Learning.  He points out that the differences identified by the labels “visual, auditory, kinesthetic, and reading/writing” are more appropriately connected to the nature of the discipline:

“There may be evidence that indicates that there are some ways to teach some subjects that are just better than others , despite the learning styles of individuals…. If you’re thinking about teaching sculpture, I’m not sure that long tracts of verbal descriptions of statues or of sculptures would be a particularly effective way for individuals to learn about works of art. Naturally, these are physical objects and you need to take a look at them, you might even need to handle them.” (Cerbin, 2011, 7:45-8:30 )

Pashler and his colleagues agree: “An obvious point is that the optimal instructional method is likely to vary across disciplines” (p. 116). In other words, it makes disciplinary sense to include kinesthetic activities in sculpture and anatomy courses, reading/writing activities in literature and history courses, visual activities in geography and engineering courses, and auditory activities in music, foreign language, and speech courses.  Obvious or not, it aligns teaching and learning with the contours of the subject matter, without limiting the potential abilities of the learners.

• Askell-Williams, H., Lawson, M. & Murray, Harvey, R. (2007). ‘ What happens in my university classes that helps me to learn?’: Teacher education students’ instructional metacognitive knowledge. International Journal of the Scholarship of Teaching and Learning , 1. 1-21.
• Bransford, J. D., Brown, A. L. & Cocking, R. R., (Eds.). (2000). How people learn: Brain, mind, experience, and school (Expanded Edition). Washington, D.C.: National Academy Press.
• Butler, D. L., & Winne, P. H. (1995) Feedback and self-regulated learning: A theoretical synthesis . Review of Educational Research , 65, 245-281.
• Cerbin, William. (2011). Understanding learning styles: A conversation with Dr. Bill Cerbin .  Interview with Nancy Chick. UW Colleges Virtual Teaching and Learning Center .
• Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning. A systematic and critical review . London: Learning and Skills Research Centre.
• Isaacson, R. M. & Fujita, F. (2006). Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning . Journal of the Scholarship of Teaching and Learning , 6, 39-55.
• Nelson, T.O. & Dunlosky, J. (1991). The delayed-JOL effect: When delaying your judgments of learning can improve the accuracy of your metacognitive monitoring. Psychological Science , 2, 267-270.
• Pashler, Harold, McDaniel, M., Rohrer, D., & Bjork, R.  (2008). Learning styles: Concepts and evidence . Psychological Science in the Public Interest . 9.3 103-119.
• Tobias, S., & Everson, H. (2002). Knowing what you know and what you don’t: Further research on metacognitive knowledge monitoring . College Board Report No. 2002-3 . College Board, NY.

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• Published: 26 April 2024

Relationship between learning styles and clinical competency in nursing students

• Seyed Kazem Mousavi 1 , 2 ,
• Ali Javadzadeh 3 ,
• Hanieh Hasankhani 3 &
• Zahra Alijani Parizad 3  

BMC Medical Education volume  24 , Article number:  469 ( 2024 ) Cite this article

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The acquisition of clinical competence is considered the ultimate goal of nursing education programs. This study explored the relationship between learning styles and clinical competency in undergraduate nursing students.

A descriptive-correlational study was conducted in 2023 with 276 nursing students from the second to sixth semesters at Abhar School of Nursing, Zanjan University of Medical Sciences, Iran. Data were collected using demographic questionnaires, Kolb’s learning styles, and Meretoja’s clinical competence assessments completed online by participants. Data were analyzed using SPSS version 16, employing descriptive statistics and inferential tests (independent T-test, ANOVA, Pearson correlation) at a significance level 0.05.

The predominant learning styles among nursing students were divergent (31.2%), and the least common was convergent (18.4%). The overall clinical competency score was 77.25 ± 12.65. Also, there was a significant relationship between learning styles and clinical competency, so the clinical competency of students with accommodative and converging learning styles was higher. ( P  < 0.05).

The results of this study showed the association between learning styles and clinical competence in nursing students. It is recommended that educational programs identify talented students and provide workshops tailored to strengthen various learning styles associated with enhanced clinical competence.

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Introduction

Clinical competency is a multifaceted and nuanced concept that has been extensively explored and examined from various perspectives in recent years [ 1 ]. Its significance is underscored by the World Health Organization (WHO), which has identified the assessment and enhancement of nurses’ competencies as fundamental principles to uphold the quality of care. WHO defines nurses as competent when they can fulfill their professional responsibilities at the appropriate level, grade, and standard [ 2 ]. Factors such as evolving healthcare systems, the imperative for safe and cost-effective services, heightened community awareness of health issues, escalating expectations for quality care, and the demand for skilled healthcare professionals have elevated the importance of clinical competence in nursing and related fields [ 3 ]. Clinical competency is considered the ultimate objective and benchmark of nursing education effectiveness [ 4 ]. Notably, clinical education constitutes a pivotal component of nursing training, with over half of nursing programs dedicated to practical training [ 5 ]. A nursing student’s ability to become a proficient nurse at the bedside hinges on acquiring essential skills during their academic journey and attaining requisite qualifications [ 6 ]. Scholars argue that continual efforts to enhance educational quality are essential for upholding nursing care standards and improving clinical competence [ 7 , 8 ]. Understanding how learners acquire knowledge is crucial for enhancing educational quality, with learning styles playing a pivotal role in this process [ 9 ].

Learning refers to the relatively enduring behavioral changes from experiences [ 10 ]. Learning styles, a concept widely embraced by educational theorists in recent decades, pertain to individuals’ distinct approaches to processing information and acquiring knowledge [ 11 ]. These styles encompass cognitive and psychosocial traits that are relatively stable indicators of learners’ engagement with and response to their learning environments [ 12 ]. Among the myriad theories on learning styles, Kolb’s learning theory is particularly influential [ 13 ]. According to Kolb, learning can be categorized into four primary modes: concrete experience, abstract conceptualization, reflective observation, and active experimentation, yielding four learning styles—converging, diverging, assimilating, and accommodating [ 14 ].

People with a converging learning style excel at problem-solving, decision-making, and practical application by engaging in abstract conceptualization and active experimentation [ 15 ]. Diverging learners thrive on experiencing and closely observing situations, possessing a unique ability to view scenarios from multiple perspectives and synthesize information into a cohesive whole [ 16 ]. The assimilating learning style is characterized by a preference for deep thinking and thorough examination, with individual’s adept at organizing information and employing abstract concepts to comprehend complex situations [ 13 ]. Accommodating learners learn best through hands-on experiences and activities, demonstrating proficiency in working with tangible objects and gaining new insights through practical engagement [ 10 ]. Learning styles are essential in nursing education because the primary mission of nursing education programs is to train nurses who have the necessary knowledge, attitude, and skills to maintain and improve the health of society members, and in other words, have sufficient competence in providing their job duties [ 17 ].

So far, separate studies have been conducted on nursing students’ learning styles and clinical competency [ 4 , 8 , 13 , 17 ]. However, the relationship between these two concepts has received less attention from researchers. The first step in ensuring students’ academic success is to determine their learning style [ 11 ]. Professors’ awareness of the student’s learning styles and the relationship between these styles and the level of clinical competency provide a favorable opportunity to identify the styles with higher clinical competency and encourage students to use them as much as possible. Considering this importance, the researchers decided to design and implement the present study to investigate the relationship between learning styles and clinical competency in nursing students.

Materials and methods

Study design and sampling.

This study was a descriptive-correlational study conducted in 2023, investigating the relationship between learning styles and the clinical competency of undergraduate nursing students. The research involved all second to sixth-semester undergraduate nursing students from the Abhar School of Nursing affiliated with Zanjan University of Medical Sciences, Iran. Sampling was carried out as a census, with 276 students selected to participate in the study. The inclusion criteria included willingness to participate in the study, full-time employment in nursing, no prior clinical work experience, and no reported psychiatric diseases or medication use. Incomplete questionnaire completion was set as an exclusion criterion.

Instruments

The data collection tools included demographic questionnaires, Kolb’s learning styles questionnaire, and a modified Meretoja nursing clinical competency questionnaire. The demographic questionnaire gathered age, gender, marital status, semester, Grade Point Average (GPA), and interest in nursing.

The data collection tools included demographic questionnaires, Kolb’s learning styles questionnaire, and a modified Meretoja nursing clinical competency questionnaire. The demographic questionnaire gathered information such as age, gender, marital status, semester, Grade Point Average (GPA), and interest in nursing.

Kolb’s III learning styles questionnaire comprised 12 questions with four options each, requiring the student to select the option most similar to them. Each option represented one of the four main learning methods: concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), and active experimentation (AE). Scores for the four learning styles were obtained from the total questions across the sections. By subtracting scores, two dimensions (AC - CE) and (AE - RO) were derived, determining the student’s learning styles as converging, diverging, accommodating, or assimilating [ 18 ]. This questionnaire has been used in various studies over the past 30 years, demonstrating validity and reliability. In Iran, Ghahrani et al. used the internal consistency method to determine the reliability of the questionnaire. They obtained Cronbach’s alpha of 71% in concrete experience, 68% in reflective observation, 71% in abstract conceptualization, and 71% in active experimentation [ 19 ]. Also, In the present study, the reliability value of this questionnaire was determined using Cronbach’s alpha method of 0.94.

Meretoja’s revised nursing clinical competency questionnaire contained 47 items across 5 areas of clinical competency: assisting patients (7 skills), teaching and guidance (12 skills), diagnostic measures (8 skills), therapeutic measures (5 skills), and occupational responsibilities (15 skills). Skills were rated on a four-point Likert scale, assessing the degree of skill utilization [ 20 ]. This questionnaire was psychometrically evaluated in Iran by Bahreini et al., and its validity was qualitatively determined at the optimal level, and its reliability was determined between 70 and 85%. Also, in this study, the reliability value of this questionnaire was determined using Cronbach’s alpha method of 0.91 [ 21 ].

Data collection and statistical analysis

Following ethical approval and research permission, questionnaires, consent forms, and contact information for the researchers were provided to students online through the Porsline system ( www.porsline.ir ) for completion. Data analysis was performed using SPSS version 16 software, employing descriptive (frequency, percentage, mean, and standard deviation) and inferential (independent T-test, ANOVA, Pearson correlation) statistics at a significance level of 0.05.

Out of 276 participants, 10 students were excluded due to incomplete questionnaire responses, leaving 266 participants for analysis. The average age of students was 22.33, with 63.4% being female. Most participants were single (79.2%), and 46.2% had a GPA between 16 and 18. Also, 80.7% of students declared that they are interested in nursing. Then, the results of the questionnaires on learning styles and clinical competency were examined. Based on this, the findings showed that divergent (31.2%) and convergent (18.4%) styles were the study participants’ most and least-used learning styles, respectively. Also, the overall students’ clinical competence score was 77.25 ± 12.65 (Table  1 ).

The relationship between the participants’ learning styles and clinical competency was examined in the next step. Initially, the study data underwent a normality assessment. The Kolmogorov-Smirnov test results indicated that parametric statistical tests were applicable ( p  > 0.05). Subsequently, an ANOVA test was conducted to explore the relationship between learning styles and clinical competency, revealing a significant association between learning style and clinical competency with moderate effect size ( p  < 0.05) (Table  2 ).

The correlation between demographic variables, learning styles, and student clinical competency was investigated in the final phase of analyzing the findings. Parametric independent t-tests, Pearson’s correlation coefficient, and ANOVA were employed for this analysis. The results indicated that none of the learning styles exhibited a statistically significant relationship with the demographic characteristics of the participants ( p  > 0.05). However, a significant correlation was observed between participants’ demographic variables, such as age, academic semester, GPA, and interest in nursing, and their clinical competencies ( p  < 0.05) (Table  3 ).

This study explored the relationship between learning styles and clinical competency in undergraduate nursing students. The research initially focused on examining the variables and subsequently explored their interrelation. According to this, the most prevalent learning style among nursing students was divergent. This finding aligns with the outcomes of various domestic studies like Mehni et al. [ 22 ] and Shirazi et al. [ 16 ], as well as numerous international studies such as those by Campos et al. in Brazil and the United States [ 23 ], Nosheen in Pakistan [ 24 ], Madu et al. in Nigeria [ 25 ], and AbuAssi et al. in Saudi Arabia [ 26 ]. It should be said that the dominant abilities of people with divergent styles are concrete experience and reflective observation. They see the situation from multiple angles, emphasize brainstorming and generating ideas, have a strong imagination, are more sensitive to values, respect the feelings of others, and listen with an open mind and without bias [ 5 ]. Therefore, these people have high cultural interests and are more inclined towards humanities fields such as sociology, psychology, counseling, and nursing.

Upon reviewing studies in this field, it is concluded that findings often vary. They can be influenced by factors including individual student traits, educators’ teaching styles, learning environments, and tasks [ 12 ]. Also, this study noted no significant association between learning styles and participants’ demographic characteristics, consistent with similar research in the field [ 22 , 23 ]. In this regard, Dantas et al. emphasized that learning styles predominantly reflect individuals’ traits and are minimally impacted by demographic variables [ 12 ].

Furthermore, the clinical competency level of nursing students was reported to be at an average level, consistent with findings from studies conducted in Iran [ 6 , 27 , 28 ] and other countries [ 29 , 30 , 31 ]. Some studies, however, have yielded differing results compared to the present study. For instance, Ghafari et al. [ 1 ], Katebi et al. [ 32 ], and Fung et al. [ 33 ] found that nursing students participating in their studies exhibited a higher level of clinical competency. Notably, participants in all three mentioned studies were in their final year of study undergoing the arena course. Hence, the emphasis on passing diverse training units and gaining more clinical experiences could justify the high clinical competency score achieved. Also, In the present study, the relationship between academic semesters and students’ clinical competency was significant, which confirms the above argument. Conversely, certain studies have reported a lower level of clinical competency among nursing students. For example, Getie et al. found that only one-third of nursing students demonstrated acceptable clinical competency [ 34 ]. These discrepancies in findings can stem from the questionnaire and the data collection method used. Notably, Getie et al. evaluated students’ clinical competency through assessments by cooperating nurses rather than self-assessment. Additionally, adjusting the questionnaire averages to reflect higher clinical competence could impact the reported competency levels. Various factors, such as individual, environmental, organizational, and educational characteristics, influence the acquisition of clinical competency in nursing students [ 2 ]. Hence, diverse study outcomes exist in this field. Also, besides academic semesters, relationships were observed between age, GPA, interest in the field, and nursing students’ clinical competency. These results align with the findings of many studies in this area [ 27 , 28 , 29 , 32 , 33 ]. Older students are often in their final semesters, potentially showcasing higher clinical competency due to their exposure to clinical environments. Madjid et al. highlighted in their study that good grades obtained by learners in any field indicate their interest in the subject. This aspect holds particular significance in nursing—a complex and demanding profession where success hinges on a genuine interest and academic excellence [ 35 ].

Also, the study revealed a significant relationship between nursing students’ learning styles and clinical competency. According to this, students employing accommodating and converging learning styles reported heightened levels of clinical competency. In their study, Ebrahimi Fakhar et al. noted that medical students utilizing reflective observation and active experimentation learning methods exhibited enhanced clinical competency upon course completion. As these attributes align with accommodative and convergent learning style characteristics, the present study’s results are consistent with these findings [ 36 ]. Moon et al. found that nursing students with converging and accommodating styles reported increased competence in clinical practices [ 37 ]. Similarly, Lundell Rudberg et al. revealed that nursing students with these learning styles demonstrated greater professional responsibility, a key aspect of clinical competence. This correlation indirectly supports the present study’s outcomes [ 38 ]. Generally, it can be said that people with convergent and accommodating learning styles typically gravitate toward practical learning. They are inclined towards hands-on activities, deriving their learning mainly through experience and active participation [ 12 , 15 ]. Therefore, they are expected to engage more in clinical settings, fostering heightened clinical competency. Furthermore, the study’s latest findings indicated that students with an assimilating learning style exhibited lower levels of clinical competency, aligning with Moon et al.‘s study [ 37 ]. Similarly, Figueiredo et al. explored nurses’ learning styles based on Kolb’s theory in qualitative research, noting that nurses with assimilating learning styles are inclined towards abstract and subjective concepts over practical content and may have less enthusiasm for immersive clinical environments [ 39 ].

Limitations

One limitation of this study was the potential for inaccuracies in questionnaire completion due to the electronic data collection method and the extensive number of questions. To address this concern, participants were provided with researchers’ contact details for clarifications during data collection. Another limitation was the reliance on self-report tools and the omission of considering individuals’ personality traits in measuring the research variables, factors beyond researchers’ direct control.

The study’s findings underscore the relationship between learning styles and clinical competency in undergraduate nursing students. Therefore, considering the high importance of acquiring clinical competency in these students, it is recommended that educational administrators identify students prone to declining clinical competency based on their learning styles and organize workshops to enhance styles associated with superior clinical competency. Also, Given the complexity of learning styles and clinical competency as constructs, future studies may benefit from exploring additional theories and tools to delve deeper into these concepts, employing qualitative or mixed-method approaches for comprehensive analysis.

Data availability

Data is provided within the manuscript or supplementary information files.

Ghafari S, Atashi V, Taleghani F, Irajpour A, Sabohi F, Yazdannik A. Comparison the Effect of two methods of internship and apprenticeship in the field on clinical competence of nursing students. RME. 2022;14(1):64–72.

Article   Google Scholar  

Ghanbari-Afra L, Sharifi K. Clinical competence and its related factors in Iranian nurses: a systematic review. Qom Univ Med Sci J. 2022;16(1):2–17.

Najafi B, Nakhaee M, Vagharseyyedin SA. Clinical competence of nurses: a systematic review study. Q J Nurs Manage. 2022;11(1):1–9.

Google Scholar  

Taylor I, Bing-Jonsson PC, Finnbakk E, et al. Development of clinical competence– a longitudinal survey of nurse practitioner students. BMC Nurs. 2021;20(1):1–15.

Shoja M, Arsalani N, Rasouli P, Babanataj R, Shirozhan S, Fallahi-Khoshknab M. Challenges of clinical education for Iranian undergraduate nursing students: a review of the literature. 2022; 2(2):46–60.

Khashei s, Ziaeirad M. The relationship between moral intelligence and clinical competence of nursing students in the internship course. Nurs Midwifery J. 2021;19(6):437–48.

Rahmah NM, Hariyati TS, Sahar J. Nurses’ efforts to maintain competence: a qualitative study. J Public Health Res. 2021;11(2):2736.

Matlhaba KL, Nkoane NL. Understanding the learning needs to enhance clinical competence of new professional nurses in public hospitals of South Africa: a qualitative study. Belitung Nurs J. 2022;8(5):414–21.

Den Hertog R, Boshuizen HPA. Learning Professional Knowledge: bachelor nursing students’ experiences in Learning and Knowledge Quality outcomes in a competence-based curriculum. Vocations Learn. 2022;15(1):21–47.

Cheng YC, Huang LC, Yang CH, Chang HC. Experiential Learning Program to strengthen self-reflection and critical thinking in Freshmen nursing students during COVID-19: a quasi-experimental study. Int J Environ Res Public Health. 2020;17(15):1–8.

Dantas LA, Cunha A. An integrative debate on learning styles and the learning process. Social Sci Humanit Open. 2020;2(1):1–5.

Al-Roomy MA. The relationship among students’ learning styles, Health sciences Colleges, and Grade Point Average (GPA). Adv Med Educ Pract. 2023;14(1):203–13.

Helou N, Aoudé J, Sobral G. Undergraduate students’ perceptions of learning nursing theories: a descriptive qualitative approach. Nurse Educ Pract. 2022;61(1):103325.

Figueiredo LDF, Silva NC, Prado ML. Primary care nurses’ learning styles in the light of David Kolb. Rev Bras Enferm. 2022;75(1):1–7.

Khozaei SA, Zare NV, Moneghi HK, Sadeghi T, Mahdizadeh Taraghdar M. Effects of quantum-learning and conventional teaching methods on learning achievement, motivation to learn, and retention among nursing students during critical care nursing education. Smart Learn Environ. 2022;18(9):1–11.

Shirazi F, Heidari S. The relationship between critical thinking skills and learning styles and academic achievement of nursing students. J Nurs Res. 2019;27(4):e38.

Lundell Rudberg S, Lachmann H, Sormunen T, et al. The impact of learning styles on attitudes to interprofessional learning among nursing students: a longitudinal mixed methods study. BMC Nurs. 2023;68(22):1–9.

Kolb A, Kolb D. The Kolb Learning Style Inventory—Version 3.1 2005 Technical Specifi cations 2005.

Ghahremani Z, Amini K, Roohani M, Aghvamy MA. The Relationship between Preferred Learning styles and Academic Achievement of Zanjan Nursing and midwifery students. J Med Educ Dev. 2013;6(12):51–61.

Meretoja R, Isoaho H, Leino-Kilpi H. Nurse competence scale: development and psychometric testing. J Adv Nurs. 2004;47(2):124–33.

Bahreini M, Moatary M, Akaberian S, Mirzaie K. Determining nurses’ clinical competence in hospitals of Bushehr University of Medical Sciences by self assessment method. Iran South Med J. 2008;11(1):69–75.

Mehni S, Chahartangi F, Tahergorabi M, Dastyar N, Mehralizadeh A, Amirmijani A. Relationship between Kolb’s Learning styles and Readiness for E-learning: a crosssectional study in the Covid-19 pandemic. Interdiscip J Virtual Learn Med Sci. 2023;14(2):99–10.

Campos DG, Alvarenga MRM, Morais SCRV, Gonçalves N, Silva TBC, Jarvill M, Oliveira Kumakura ARS. A multi-centre study of learning styles of new nursing students. J Clin Nurs. 2022;31(1–2).

Nosheen N, Hussain M. The Association between learning style, learning strategies with academic performance among nursing students. J Health Med Nurs. 2020;72:62–7.

Madu OT, Ogbonnaya NP, Chikeme PC, Omotola NJ. A study to assess the learning style preference of undergraduate nursing students in Southeast, Nigeria. Asian J Nurs Educ Res. 2019;9:177–84.

AbuAssi N, Alkorashy H. Relationship between learning style and readiness for self-directed learning among nursing students at king Saud university, Saudi Arabia. Int J Adv Nurs Stud. 2016;5:109–16.

Tohidi S, KarimiMoonaghi H, Shayan A, Ahmadinia H. The Effect of Self-learning Module on nursing students’ clinical competency: a pilot study. Iran J Nurs Midwifery Res. 2019;24(2):91–5.

Motefakker S, Shirinabadi Farahani A, Nourian M, Nasiri M, Heydari F. The impact of the evaluations made by Mini-CEX on the clinical competency of nursing students. BMC Med Educ. 2022;22(1):1–8.

Lee KC, Ho CH, Yu CC, Chao YF. The development of a six-station OSCE for evaluating the clinical competency of the student nurses before graduation: a validity and reliability analysis. Nurse Educ Today. 2020;84:104247.

Yu M, Tong H, Li S, Wu XV, Hong J, Wang W. Clinical competence and its association with self-efficacy and clinical learning environments among Chinese undergraduate nursing students. Nurse Educ Pract. 2021;53:103055.

Green G, Ofri L, Tesler R. The role of fundamental nursing practices Simulation on Students’ competencies and learning satisfaction: repeated measured design. Healthc (Basel). 2022;10(5):1–8.

Katebi MS, Arab Ahmadi A, Jahani H, Mohalli F, Rahimi M, Jafari F. The effect of portfolio training and clinical evaluation method on the clinical competence of nursing students. J Nurs Midwifery Sci. 2020;7:233–40.

Fung JTC, Zhang W, Yeung MN, Pang MTH, Lam VSF, Chan BKY, Wong JY. Evaluation of students’ perceived clinical competence and learning needs following an online virtual simulation education programme with debriefing during the COVID-19 pandemic. Nurs Open. 2021;8(6):3045–54.

Getie A, Tsige Y, Birhanie E, Tlaye KG, Demis A. Clinical practice competencies and associated factors among graduating nursing students attending at universities in Northern Ethiopia: institution-based cross-sectional study. BMJ Open. 2021;11(4):e044119.

Madjid FT, Villacorte LM, Cajigal JV, Rosario-Hussein CD, Saguban RB, Gudoy N, Madjid ZNT. Factors influencing the clinical competency among nursing students: a cross-sectional study. Hail J Health Sci. 2023;5(1):7–12.

Ebrahimi Fakhar A, Adhami Moghadam F, Merati F, Sahebalzamani M. The relationship of learning styles with Basic sciences and pre-internships Comprehensive Examination scores and students’ results of the clinical competency test at the end of the General practitioner course. Educational Dev JundiShapur. 2019;10(3):219–29.

Moon MY. Relationship between clinical competency and Kolb’s learning style for clinical practice education in nursing students. Int J Adv Nurs Educ Res. 2019;4(2):1–6.

Lundell Rudberg S, Lachmann H, Sormunen T, Scheja M, Westerbotn M. The impact of learning styles on attitudes to interprofessional learning among nursing students: a longitudinal mixed methods study. BMC Nurs. 2023;22(1):68.

Figueiredo LDF, Silva NCD, Prado MLD. Primary care nurses’ learning styles in the light of David Kolb. Rev Bras Enferm. 2022;75(6):e20210986.

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Acknowledgements

The authors acknowledge the students who participated in the study.

Zanjan University of Medical Sciences, Iran.

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Department of Nursing, Abhar School of Nursing, Zanjan University of Medical Sciences, Zanjan, Iran

Seyed Kazem Mousavi

Ph.D Candidate in Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran

Department of Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran

Ali Javadzadeh, Hanieh Hasankhani & Zahra Alijani Parizad

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Contributions

All the authors helped design the study. AJ and HH collected the data. SKM, and AJ analyzed and interpreted the data. All the authors helped write the manuscript and read and approved the final version.Funding.

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Correspondence to Seyed Kazem Mousavi .

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Permission to conduct the present study was obtained from the Research Department and Ethics Committee of Zanjan University of Medical Sciences (IR.ZUMS.REC.1402.095. available at: https://ethics.research.ac.ir/ ). All the study participants were informed about the objectives of the study, the confidentiality of the information, and the voluntary nature of their participation, and all students completed the informed consent form.

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Mousavi, S.K., Javadzadeh, A., Hasankhani, H. et al. Relationship between learning styles and clinical competency in nursing students. BMC Med Educ 24 , 469 (2024). https://doi.org/10.1186/s12909-024-05432-z

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The Relationship Among Students’ Learning Styles, Health Sciences Colleges, and Grade Point Average (GPA)

Muhammad a al-roomy.

1 Department of English, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia

Introduction

Learning styles are an increasingly important area in education, affecting different aspects of the learning arena. They can predict how students might process information and solve learning problems differently even when engaged in the same learning activities.

This study aimed to investigated the relationship among health sciences students’ learning styles, college majors, and grade point averages (GPAs). A total of 247 male students belonging to King Saud bin Abdulaziz University at Riyadh were chosen intentionally for this study, which employed a quantitative procedure for collecting and analysing data.

The study used a survey research design, and data were obtained from the Perceptual Learning Style Preference Questionnaire (PLSPQ), which the students had to answer online. The collected data were analysed using the Statistical Package for the Social Sciences (SPSS 16). Descriptive analysis methods – such as means, standard deviations, frequency counts, and correlations – were employed.

The results indicated that the students demonstrated a range of learning style preferences. The most frequently preferred style was the auditory learning style, followed by the kinaesthetic and individual learning styles. The least preferred style was group learning. The results also showed significant differences in the students’ learning styles across colleges – with preferences towards the auditory, individual, and group learning styles – and insignificant differences for the visual preference, kinaesthetic, and tactile preference learning styles. Finally, the relationship between learning style and GPA was only positive with the individual learning style and negative with the group learning style.

These findings support the notion that the total learning environment affects students’ learning styles and suggest several courses of action for students, teachers, and policymakers.

Every person’s learning technique is unique. Students bring to the learning process their own variations, which then influence how they approach learning as a whole. Such learners tend to resort to their learning habits and draw on life experiences by linking them to new ones. As a result of these variations, some learners do better than others and achieve high outcomes. However, the extent to which individual differences can predict success or failure is yet to be determined because some of these beliefs are based on personal experiences and are not suitable for all learning situations and might hinder learning. 1 What prevents some people from learning, despite knowing their areas of weakness and strength, is their belief that they have a fixed identity – ie they cannot expand their capabilities to adopt new learning styles. 2 Himmele and Himmele 3 (p27) explain this barrier by stating that “they may internalize the belief that they can only learn when content is presented in that particular way”.

Learning styles are defined by Reid 4 (p8) as “an individual’s” natural, habitual, and preferred way(s) of absorbing, processing, and retaining new information and skills’. Among other individual variables such as language aptitude, language anxiety, and learning strategies, learning styles are an increasingly important area that has gained support and recognition in second language acquisition. 5–10

Learners’ individual differences are divided into two categories: (a) innate attributes, such as gender, age, and learning style; and (b) acquired attributes, such as attitude, motivation, and learning strategy use. 11 Otherwise, they fall into physiological and affective factors as opposed to cognitive factors. 5 , 6 These individual differences are also interrelated and affect one another. Students tend to use their strategic competence while implementing different learning styles, ie metacognitive, social, and affective strategies. They utilise these strategies consciously or unconsciously to learn and that by combining these strategies with other individual variables – such as motivation, feelings, and perceptual preferences – students wind up with learning styles. 11

As such, different learning style models emerged based on some influential learning theories, including cognitive psychology. Cognitivists are concerned with mental processes such as reasoning, memory retention, problem solving, creativity, and so on because they aim to understand how people think and process information. When individuals become aware of their own mental processes, they become metacognitively aware of their learning. 5 For example, in perceptual learning and the implementation of perceptual modalities, the visual, auditory, kinaesthetic, and tactile styles are included to design his Perceptual Learning Style Preference Questionnaire (PLSPQ). 4 It is suggests that experience is at the heart of learning and the development of learning and that knowledge is constructed via grasping and transforming experiences. 12 Four modes of learning inform his experiential model theory, ie abstract conceptualisation, concrete experience, active experimentation, and reflective observation. Student–student and student–teacher interaction inform Grasha’s model of learning, called the Grasha Riechmann Student Learning Styles Scale (GRSLSS), which categorises students’ real responses in learning environments into four groups, ie independent, avoidant, cooperative, dependent, competitive, and participant. 13

Learning style research can be traced back to the seminal work of Witkin et al 14 who maintain that individuals fall into the dimension of an analytic predisposition (ie field independence) or a more global dimension to the processing of information (ie field dependence). Much of the research on second language learning styles has examined cognitive styles and their association with learning styles on the one hand and cognitive styles with affective variables on the other hand. 15 , 16 However, more variables should be considered in this research, such as the level of education, gender, and specification and their relationships with culture. 17 How students prefer to be taught is another predictor of the students’ learning styles. Simulation-based learning in science courses allows students to show a high level of engagement and satisfaction when combined with kinaesthetic learning. 18

Learning style awareness is of potential value to all language teachers, students, and policymakers. The learning styles that students implement often come into conflict with their teachers’ styles, or the students fail to suit their learning styles to different learning situations. 19 As a result, students’ learning might not be as successful as expected. If teachers can identify these learning styles, then they can train their students to use them efficiently and be flexible in using different styles by providing the necessary instructional support. Teachers can also employ different teaching methods and activities. 7 , 8

Learning is affected by learning styles. The ability of students to adapt to various situations for their learning needs will result in high learning outcomes. 20–23 An additional benefit of learning style research is that policymakers can consider other factors related to learning and teaching, such as curriculum design, materials development, student orientation, and teacher training. 21 , 24 Nevertheless, some cognitive researchers have alleged that learning styles are “neuromyths” rather than valid psychological constructs. They cite an absence of conclusive validation studies of learning style assessment instruments and of convincing demonstrations that students taught consistently with their learning styles learn more than students whose styles are mismatched to their instruction. The previous account clearly shows that the number of publications both supporting and opposing this point of view is large and continually growing. 25 , 26

However, while several studies in the Saudi context deal with learning styles and academic success – mainly in EFL contexts, as far as this literature review is concerned – no single study has examined the relationship between learning styles and both GPA and health sciences colleges. To that end, the purpose of this study is to fill in this gap and investigate health sciences students’ learning styles in relation to their GPA and colleges at King Saud bin Abdulaziz University for Health Sciences. The study used a survey research design, ie quantitative in nature, and data were obtained from the PLSPQ. Three research questions were thus formulated for this investigation:

• What are the learning style preferences of health sciences students?
• Is there a significant correlation between learning style preference and health sciences colleges?
• Is there a significant correlation between learning style preference and GPA?

Literature Review

Learning styles: definitions and importance.

Learning style has several definitions in the literature. For example, Reid 4 (p8) defines it as “an individual’s” natural, habitual, and preferred way(s) of absorbing, processing, and retaining new information and skills’. Oxford 7 (p3) states that ‘learning styles are the general approaches – for example, global or analytic, auditory or visual – that students use in acquiring a new language or in learning any other subject’. Cornett 27 (p9) gives a similar definition – “the overall patterns that give general direction to learning behavior”. The word “style” by comparing it with two other terms, ie “process” and “strategy”. 5 It is maintained that “process” is the most general among the three, while “strategy” is the most specific. According to Brown 5 (p104), “style” is “a term that refers to consistent and rather enduring tendencies or preferences within an individual”. Some learning styles are associated with personality traits, such as anxiety, while others are linked to cognitive factors, such as ambiguity tolerance. In light of the above definitions, one would struggle to say that one definition is superior to another because every definition considers one angle and is from the researcher’s area of interest or expertise. 5 Nevertheless, all the definitions fit under the umbrellas of psychology, physiology, and cognition. 10

Another way to define learning styles is to compare them with learning abilities. The term “learning styles” is not the same as the term “abilities”, but it explains how people prefer to implement their abilities to learn. Learning styles and thinking processes interact at different levels when students respond to different tasks. 28 Thus, it is emphasised that instead of saying that some learners have the abilities and potential to be “good learners”, it would be better to focus on different strategies that effective learners employ and relate this to success. Further, it is useful to relate success to students’ styles and preferences. 9

The existing literature maintains the importance of learning styles for different aspects of learning. For example, despite similarities or universal aspects between first and second language acquisition, these similarities do not account for individual differences or tell us how individuals prefer to learn and tackle different learning problems. 5 Others relate styles to academic success in language learning or learning in general and to the relationship between reading strategies and learning styles. 29 Learning styles and learning strategies are associated with effective language learners who call on different learning strategies and are also aware of their learning process as a whole and their personal learning styles in particular. 6 , 7 Another benefit of learning style research is its capacity to deploy learning style models for developing learning and teaching processes on the one hand and to make use of learning style models to guide young learners’ preferences and development on the other hand. 30

Models of Learning Styles

Several learning style models and approaches account for differences in individuals’ learning. These models fall into three dichotomous learning style models, ie simple (eg convergent/divergent thinking), compound (eg Kolb’s, active/reflective, abstract/concrete), and complex (eg perceptual styles). 30 In L2 learning, four dimensions of learning styles – ie sensory preferences (eg visual or auditory), personality types (eg extroverted or introverted), desired degree of generality (eg global or analytic), and biological differences (eg biorhythms and location) – suggesting that the biological aspects are the least important to be considered by teachers. 7

However, as different models of learning are highlighted in the literature and are used loosely, these models should be combined with the factors, characteristics, and features of each learning style to define them well. 16 The factors affecting students’ style preferences include gender, field of study, current environment, and level of education. Some of these are genetic, while others are related to the student’s environment and experiences. 12 Brown 5 (p105) clarifies that “people”s styles are determined by the way they internalise their total environment, and since that internalisation process is not strictly cognitive, we find that physical, affective, and cognitive domains merge in learning styles’.

Dunn and Griggs 31 (p3) hold that the interaction between learning styles and other characteristics is such that ‘[l]earning style is the biologically and developmentally imposed set of characteristics that make the same teaching method wonderful for some and terrible for others’. The previous quotes clearly show that several factors affect learning styles and have a direct connection to the learning process. 12

Another important factor is the impact of environmental and cultural dimensions on learning styles. 10 For this reason, one can say that styles are not fixed or stable traits in learning because their existence would be affected by learning contexts and situations. Such styles are guided by the total environment and not restricted to the cognitive domain but rather combine the physical and affective domains. 5 , 7

On the other hand, some scholars doubt the impact of learning styles on students’ learning. More research support is needed regarding the consequences of endorsing individual learning styles. 32 In response, it is suggested that both teachers and students should devote their time and effort to learning theories and practices that are empirically proven to be effective rather than examining learning styles. 33

Finally, given many models and categories to address learning styles, several tools for assessing these styles have emerged. Among those available are Kolb’s Learning Style Inventory (1984), the Felder–Silverman Learning/Teaching Style Model (1988), and Reid’s (1995) PLSPQ, originally developed in 1984). Different perceptual learning styles include the visual, auditory, kinaesthetic, tactile, individual, and group learning dimensions. 4 , 12 , 15 Although this measure was first used by an L2 researcher with L2 learners, it was not L2 specific because its items could be applicable in other contexts other than the L2 field. 34 Since this questionnaire has been used in different contexts by many researchers, it shows a high degree of reliability and validity.

Studies on Learning Styles

Studies pertaining to the relationship between learning styles and how they might interact with success in language learning, academic achievement, or the major area of study have been carried out in different contexts. In the international context, the learning preferences of ESL students from different language backgrounds and compared learning styles with other variables, such as sex, level of education, field of study, and age have examined. 21 The findings show that for medicine students, the auditory style is a major learning preference, and that the students hold a negative preference for group work. The impact of cultural differences and education-related variables such as gender, level of education, and area of specification on learning styles and find that the biggest impact is related to cultural differences, level of education, and area of specification and the smallest to age and gender. 17 Similarly, the learning styles of clinical lab students and find that these students prefer multiple learning styles that enable them to retain information and enhance their learning experiences. 35 Their study concludes with the need for educators to create learning resources that cater to diverse learning styles and to allow teachers to try out different teaching methods.

Regarding the impact of learning styles on academic performance, the relationship between learning styles and the English achievement of Taiwan EFL college students belonging to three different levels of language mastery. The study reveals that while both high and intermediate students favour visual learning styles, including auditory and haptic, basic-level students prefer the haptic style. 36 Another study carried out to determine the relationship among learning styles, teaching styles, and academic performance and have found that the relationship between learning styles and teaching styles is positive for students who demonstrate visual learning styles, followed by those who prefer kinaesthetic styles, and that students who are visual and kinaesthetic perform better than students with bimodal learning styles. 36 Also the relationship between students’ academic achievement and their learning styles and find no significant relationship between the two. 37 In a recent study on medical students, the relationship among learning styles, gender, and academic performance, more than half the students favour implementing two or more learning styles, specifically kinaesthetic and auditory. However, they have found no relationship between learning styles and gender on the one hand and learning styles and academic performance on the other hand. 38

In the local context, the preferred learning styles of 120 Saudi EFL learners and finds that students prefer the kinaesthetic and tactile learning styles over other learning styles. 39 Another study investigates Saudi EFL students’ learning preferences and find that they prefer tactile and visual learning styles, followed by auditory, group, and kinaesthetic learning styles. 40 In the same study, Saudi students express a preference for pair and group work and de-emphasised individual work given their collectivist culture. In different setting, 137 healthcare students enrolled in six different courses, investigating the relationship between learning styles and academic achievement, and find that most prefer visual, reading and writing, kinaesthetic, and auditory learning styles, in that order. 23 Similarly, the difference between students’ preferred learning styles and teachers’ preferred teaching styles is investigated to show a mismatch between the two categories; while the students prefer sensing, visual, active, and sequential learning styles, the teachers prefer abstract, verbal, passive, and global teaching styles. 19 Finally, the relationship between students’ learning styles and their satisfaction with the educational activities offered to them by the educational programme. They have found that although the students adopt several learning styles, there is no significant relationship between their learning styles and satisfaction on the one hand and their current university GPAs or other scores on the other hand. 41

The convenience sampling technique, which is a non-probability sampling method where the sample is easily approached, was implemented to collect data. This technique was employed because it saved time and effort; it would have been difficult for the researcher to visit different colleges to access students. A total of 247 male students belonging to King Saud bin Abdulaziz University at Riyadh took the questionnaire survey. The students were all second-year students and were assigned to different colleges based on their GPAs in their orientation year. Those with the highest GPAs would join the College of Medicine, followed by the College of Dentistry, the College of Pharmacy, the College of Nursing, the College of Public Health and Health Informatics, and the College of Applied Medical Sciences. The distribution of the sample is shown in Table 1 .

Distribution of Study Sample According to College

The questionnaire used for data collection was (PLSPQ). This questionnaire made use of the existing literature on perceptual modalities to develop this questionnaire and added two domains to account for L2 learning classrooms, ie sociological and social. The questionnaire items were organised to measure the following: items 6, 10, 12, 24, and 29 reflected the visual preference score; items 1, 7, 9, 17, and 20 indicated auditory learners; items 2, 8, 15, 19, and 26 indicated kinaesthetic learners; items 11, 14, 16, 22, and 25 reflected the tactile preference score; items 3, 4, 5, 21, and 23 measured the group preference learning score; and items 13, 18, 27, 28, and 30 reflected the individual learning score. The students were provided the link to the questionnaire via email and were encouraged to answer it at their convenience. After one week, all the students completed the questionnaire.

To ensure the clarity and readability of the items and to check its internal validity, the questionnaire was given beforehand to three English teachers and 15 students for piloting, and their concerns and feedback were acknowledged. To check the external validity of the questionnaire, a random pilot sample of 35 students was employed. The Pearson correlation indicated that all the items had a high level of validity and were significant at the 0.01 level. For questionnaire reliability, Cronbach’s alpha was run and ranged from 0.74 to 0.95, which meant that the questionnaire was reliable.

Data Collection and Analysis

As this research is quantitative in nature, for the data collection procedure, the questionnaire was distributed to second-year students at King Saud bin Abdulaziz University in Riyadh who had already been assigned to different colleges. The questionnaire items required about 15 minutes to complete, but some students were given more time as requested. To analyse the collected data, the Statistical Package for the Social Sciences (SPSS 16) was utilised. The descriptive analysis methods included means, standard deviations, and frequency counts, according to the research questions.

The following section will be devoted to an analysis of the questionnaire by answering the three research questions, starting with the first: “What are the learning styles of health sciences students?”.

Table 2 provides a general overview of the health sciences students’ learning style preferences. The most frequently apparent preference style was the auditory learning style, followed by the kinaesthetic and individual learning styles. The least popular was group learning.

Means and Std. Deviations of the Respondents’ Answers to Determine Their Preferred Learning Styles

Abbreviations : Std., Standard.

The following account answers the second research question, “Is there a significant correlation between learning style preference and GPA?” To answer this question, the researcher used one-way analysis of variance (ANOVA) to indicate the differences among more than two independent groups regarding their preferred learning styles according to their colleges.

Table 3 shows that the (F) values are not significant with regard to the learning styles of visual preference, kinaesthetic learners, and tactile preference, which indicates that there are no statistically significant differences among the responses in the study sample regarding the degree of the respondents’ preferences for these learning styles based on their colleges. Table 3 also shows that the values of (F) are significant at the level of 0.05 or less among auditory learners, those with a preference for group learning, and those with a preference for individual learning, which indicates statistically significant differences in the responses of the study sample regarding the degree of their preference for these learning styles given the different colleges to which they belong. Using the least significant difference (LSD) test, the sources of these differences were revealed, as indicated in Table 4 , and this answers the second research question.

One-Way Analysis of Variance (F-Test) for the Difference in the Study Participants’ Responses About Their Preferred Learning Style Across Colleges

Abbreviations : Sig, significant; df., degree of freedom.

Multiple Range Tests: Least Significant Difference (LSD) Test for the Differences in the Respondents’ Answers About Their Preferred Learning Styles According to Their Colleges

Notes : *Indicates significant differences in the table The mean difference is significant at the 0.050 level.

Table 4 clearly shows significant differences at the 0.05 level on the health sciences students’ learning style preferences according to their colleges, as follows:

• There are significant differences for auditory learners between the sample members in the Colleges of Medicine and Dentistry and the sample members in the College of Public Health and Health Informatics, in favour of the latter.
• There are significant differences for auditory learners between the sample members in the College of Dentistry and the sample members in the College of Applied Medical Sciences, in favour of the latter.
• There are significant differences in the preference for group learning between the sample members in the Colleges of Medicine and Pharmacy and the sample members in the College of Public Health and Health Informatics, in favour of the latter.
• There are significant differences in the preference for group learning between the sample members in the College of Medicine and the sample members in the College of Applied Medical Sciences, in favour of the latter.
• There are significant differences in the preference for individual learning between the sample members in the Colleges of Pharmacy and Public Health and Health Informatics and the sample members in the College of Medicine, in favour of the latter.

Finally, the following section answers the third research question: “Is there a significant correlation between learning style and GPA and health sciences college?” To answer this question, the researcher used the Pearson correlation coefficient to measure the relationship between the responses of the sample about their preferred learning styles and their cumulative grades. The following table shows the results obtained.

Table 5 clearly shows an almost non-existent relationship between the respondents’ degree of preference for their learning styles – visual, auditory, kinaesthetic, and tactile – and their cumulative GPAs, and these results were not statistically significant. The table also reveals an inverse (negative) relationship between the sample’s preference for group learning and their cumulative GPA averages, which indicates that the greater the students’ preference for this style of learning, the more likely a decrease in their cumulative averages, and this relationship was statistically significant at the 0.01 level. However, Table 5 also shows a direct (positive) relationship between the sample’s preference for individual learning and their cumulative GPAs, which indicates that the greater the students’ preference for individual learning, the more likely a higher cumulative average, and this relationship was statistically significant at the 0.01 level.

Pearson Correlation Coefficients to Measure the Relationship Between Health Sciences Students’ Preferred Learning Styles and Their GPAs

Note : **The correlation is significant at the 0.01 level (2-tailed).

In response to the first research question – “What are the learning style preferences of health sciences students?” – the students employed different learning styles and expressed a preference for the auditory, kinaesthetic, and individual learning styles, in that order. The auditory learners chose items such as “When teachers tell me the instructions, I understand [them] better” and “I remember things I have heard in the class better than things I [have] read”. However, the findings are not inconsistent with those studies carried out in Saudi context. 19 , 39–41 One possible reason for favouring the auditory style over other learning styles might be that the students enter the classroom with their own beliefs about learning, and such beliefs direct the way they approach learning and what activities they consider useful for learning. 2 , 3 Before entering college, the students value rote learning and memorisation and believe that these approaches are beneficial to learning, where the students sit quietly and passively receive the information from their teachers. In this case, the students see their teacher as their only reliable source of knowledge. Teachers might encourage such a belief by designing completely teacher-centred approaches. For this reason, the students would not receive enough learning experiences to become familiar with effective learning styles. 29

Also, unlike other studies whose respondents expressed a preference for group work, 40 the findings showed that the respondents preferred group work the least. The students were in favour of items such as “When I study alone, I remember things better” and “When I work alone, I learn better”. The reason behind this might be related to the students’ past learning experiences. As students finish their first year of study, they develop strategies for effective learning and become more independent – that is, the students prefer to look for the solutions themselves and deploy other learning styles which can be done individually.

In response to the second and third research questions, no significant relationship was found between the students’ visual, auditory, kinaesthetic, and tactile learning style preferences and their GPAs. However, the relationship was positive for those with a preference for individual learning and negative for those with a preference for group work. This suggests that the more the students prefer individual work, the more likely they are to earn a higher GPA, while a preference for group work would lead to a decrease in the GPA. Interestingly, these findings are consistent with those of other studies which found no relation between learning style and academic performance. 37 , 41 This suggests a positive correlation between the two. 9 , 22 , 29 , 36

The positive relationship between individual preference and learning style might be due to other factors that are related to learning styles that could positively or negatively affect students’ preferences. 12 , 31 This confirms that learning styles are not stable traits. 5 , 7 Learning situations include environmental and cultural dimensions that are crucial in encouraging or discouraging students to call upon specific learning styles. 10 For example, the teaching methods and styles that teachers prefer and deploy can lead their students to look for learning styles that match the teachers’ preferences. Another important factor is the form of assessment that teachers implement in the classroom. If teachers focus on summative assessments that emphasise quizzes and written exams and avoid project work, this can result in a competitive rather than collaborative environment among students.

With reference to the correlation between learning style preference and college major, several findings were revealed. First, there was a significant difference between students enrolled in the Colleges of Medicine and Pharmacy and those enrolled in the College of Public Health and Health Informatics, who preferred the auditory learning style. Also, there was a significant difference between dentistry students and applied medical sciences students, who preferred the auditory learning style as well. Second, a significant difference was found between medicine and pharmacy students and those of public health and health informatics, who showed a preference for the group learning style. This finding disagrees with that of some studies, that found that medicine students prefer the auditory and group learning styles. 21 Similarly, there was a significant difference between medicine students and applied medical science students, who preferred the group learning style.

Finally, a significant difference was found between pharmacy students and public health and health informatics students, who showed a preference for the individual learning style compared to medicine students. Interestingly, for students who preferred the auditory and group learning styles, their GPAs after their orientation year were not as high as those of students belonging to the Colleges of Medicine or Pharmacy, for instance. When students finish their orientation year, they are assigned to different colleges based on their GPAs. The best (A+ and A students) would go to the medicine college, followed by (B+ and below students) the pharmacy, dentistry, public health and health informatics, and applied medical sciences colleges. For this reason, students belonging to these colleges prefer to listen to their teachers all the time and follow instructions. They also prefer to get help from their peers by working in groups. In addition, teachers’ teaching styles and the curriculum designs could affect students’ learning preferences. 19 , 21

This study examined the relationship among health sciences students’ learning style preferences, GPAs, and college majors in the Saudi context. Based on the findings and discussion above, one can conclude that the most preferred style was the auditory learning style, followed by the kinaesthetic and individual learning styles; the least preferred one was group learning. Students of the Colleges of Public Health and Health Informatics and Applied Medical Sciences showed a preference to auditory learning compared to the medicine, pharmacy, and dentistry colleges. Also, while public health and health informatics and applied medical sciences students favoured the group learning style, students of medicine did not. Finally, no relationship was found between the students’ learning styles and GPAs except for those expressing a preference for group and individual learning. While the relationship was positive for individual learning, it was negative for the group learning style, and pharmacy students were more inclined towards the former.

These findings suggest several courses of action for students, teachers, and policymakers, and they support the notion that the total cultural environment has an effect on learning styles. 5 , 7 , 12 Students being aware of their own learning styles will enable them to implement multifarious learning-style preferences that are suited to different situations. 1 , 3 This will also activate their metacognitive awareness, which would empower them to take control over their own learning. This can be applied beyond their classes, upon graduation, when they go to their workplaces. They need to diversify their learning styles so as to deal with their colleges and patients to best communicate and achieve their desired goals. By the same token, if teachers are aware of their students’ learning styles, this will help them figure out any mismatch between teaching styles and learning styles and therefore expand their repertoire of teaching styles. For policymakers, it is important to consider learning styles in preservice teacher training and in designing educational programmes, as this is an important step to improve the quality of education.

The study has two limitations. The sample consisted of 247 health sciences students, and the study was conducted in a Saudi health sciences university. Another limitation was that the recruited sample comprised all male students. Future studies should include bigger and more varied samples from other health science colleges, eg both male and female students, while determining different factors that affect learning styles. Also, future studies using data collection tools other than questionnaires are needed to gain deeper insights into students’ learning styles.

The author reports no conflicts of interest in this work.