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5.2: Factors Influencing Learning

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University of Illinois at Urbana-Champaign

Learning is a complex process that defies easy definition and description. This module reviews some of the philosophical issues involved with defining learning and describes in some detail the characteristics of learners and of encoding activities that seem to affect how well people can acquire new memories, knowledge, or skills. At the end, we consider a few basic principles that guide whether a particular attempt at learning will be successful or not.

learning Objectives

• Consider what kinds of activities constitute learning.
• Name multiple forms of learning.
• List some individual differences that affect learning.
• Describe the effect of various encoding activities on learning.
• Describe three general principles of learning.

Introduction

What do you do when studying for an exam? Do you read your class notes and textbook (hopefully not for the very first time)? Do you try to find a quiet place without distraction? Do you use flash cards to test your knowledge? The choices you make reveal your theory of learning, but there is no reason for you to limit yourself to your own intuitions. There is a vast and vibrant science of learning, in which researchers from psychology, education, and neuroscience study basic principles of learning and memory.

In fact, learning is a much broader domain than you might think. Consider: Is listening to music a form of learning? More often, it seems listening to music is a way of avoiding learning. But we know that your brain’s response to auditory information changes with your experience with that information, a form of learning called auditory perceptual learning (Polley, Steinberg, & Merzenich, 2006). Each time we listen to a song, we hear it differently because of our experience. When we exhibit changes in behavior without having intended to learn something, that is called implicit learning (Seger, 1994), and when we exhibit changes in our behavior that reveal the influence of past experience even though we are not attempting to use that experience, that is called implicit memory (Richardson-Klavehn & Bjork, 1988).

Other well-studied forms of learning include the types of learning that are general across species. We can’t ask a slug to learn a poem or a lemur to learn to bat left-handed, but we can assess learning in other ways. For example, we can look for a change in our responses to things when we are repeatedly stimulated. If you live in a house with a grandfather clock, you know that what was once an annoying and intrusive sound is now probably barely audible to you. Similarly, poking an earthworm again and again is likely to lead to a reduction in its retraction from your touch. These phenomena are forms of nonassociative learning , in which single repeated exposure leads to a change in behavior (Pinsker, Kupfermann, Castelluci, & Kandel, 1970). When our response lessens with exposure, it is called habituation , and when it increases (like it might with a particularly annoying laugh), it is called sensitization . Animals can also learn about relationships between things, such as when an alley cat learns that the sound of janitors working in a restaurant precedes the dumping of delicious new garbage (an example of stimulus-stimulus learning called classical conditioning ), or when a dog learns to roll over to get a treat (a form of stimulus-response learning called operant conditioning ). These forms of learning will be covered in the module on Conditioning and Learning ( http://noba.to/ajxhcqdr ).

Here, we’ll review some of the conditions that affect learning, with an eye toward the type of explicit learning we do when trying to learn something. Jenkins (1979) classified experiments on learning and memory into four groups of factors (renamed here): learners, encoding activities, materials, and retrieval. In this module, we’ll focus on the first two categories; the module on Memory ( http://noba.to/bdc4uger ) will consider other factors more generally.

People bring numerous individual differences with them into memory experiments, and many of these variables affect learning. In the classroom, motivation matters (Pintrich, 2003), though experimental attempts to induce motivation with money yield only modest benefits (Heyer & O’Kelly, 1949). Learners are, however, quite able to allocate more effort to learning prioritized over unimportant materials (Castel, Benjamin, Craik, & Watkins, 2002).

In addition, the organization and planning skills that a learner exhibits matter a lot (Garavalia & Gredler, 2002), suggesting that the efficiency with which one organizes self-guided learning is an important component of learning. We will return to this topic soon.

One well-studied and important variable is working memory capacity. Working memory describes the form of memory we use to hold onto information temporarily. Working memory is used, for example, to keep track of where we are in the course of a complicated math problem, and what the relevant outcomes of prior steps in that problem are. Higher scores on working memory measures are predictive of better reasoning skills (Kyllonen & Christal, 1990), reading comprehension (Daneman & Carpenter, 1980), and even better control of attention (Kane, Conway, Hambrick, & Engle, 2008).

Anxiety also affects the quality of learning. For example, people with math anxiety have a smaller capacity for remembering math-related information in working memory, such as the results of carrying a digit in arithmetic (Ashcraft & Kirk, 2001). Having students write about their specific anxiety seems to reduce the worry associated with tests and increases performance on math tests (Ramirez & Beilock, 2011).

One good place to end this discussion is to consider the role of expertise. Though there probably is a finite capacity on our ability to store information (Landauer, 1986), in practice, this concept is misleading. In fact, because the usual bottleneck to remembering something is our ability to access information, not our space to store it, having more knowledge or expertise actually enhances our ability to learn new information. A classic example can be seen in comparing a chess master with a chess novice on their ability to learn and remember the positions of pieces on a chessboard (Chase & Simon, 1973). In that experiment, the master remembered the location of many more pieces than the novice, even after only a very short glance. Maybe chess masters are just smarter than the average chess beginner, and have better memory? No: The advantage the expert exhibited only was apparent when the pieces were arranged in a plausible format for an ongoing chess game; when the pieces were placed randomly, both groups did equivalently poorly. Expertise allowed the master to chunk (Simon, 1974) multiple pieces into a smaller number of pieces of information—but only when that information was structured in such a way so as to allow the application of that expertise.

Encoding Activities

What we do when we’re learning is very important. We’ve all had the experience of reading something and suddenly coming to the realization that we don’t remember a single thing, even the sentence that we just read. How we go about encoding information determines a lot about how much we remember.

You might think that the most important thing is to try to learn. Interestingly, this is not true, at least not completely. Trying to learn a list of words, as compared to just evaluating each word for its part of speech (i.e., noun, verb, adjective) does help you recall the words—that is, it helps you remember and write down more of the words later. But it actually impairs your ability to recognize the words—to judge on a later list which words are the ones that you studied (Eagle & Leiter, 1964). So this is a case in which incidental learning —that is, learning without the intention to learn—is better than intentional learning .

Such examples are not particularly rare and are not limited to recognition. Nairne, Pandeirada, and Thompson (2008) showed, for example, that survival processing—thinking about and rating each word in a list for its relevance in a survival scenario—led to much higher recall than intentional learning (and also higher, in fact, than other encoding activities that are also known to lead to high levels of recall). Clearly, merely intending to learn something is not enough. How a learner actively processes the material plays a large role; for example, reading words and evaluating their meaning leads to better learning than reading them and evaluating the way that the words look or sound (Craik & Lockhart, 1972). These results suggest that individual differences in motivation will not have a large effect on learning unless learners also have accurate ideas about how to effectively learn material when they care to do so.

So, do learners know how to effectively encode material? People allowed to freely allocate their time to study a list of words do remember those words better than a group that doesn’t have control over their own study time, though the advantage is relatively small and is limited to the subset of learners who choose to spend more time on the more difficult material (Tullis & Benjamin, 2011). In addition, learners who have an opportunity to review materials that they select for restudy often learn more than another group that is asked to restudy the materials that they didn’t select for restudy (Kornell & Metcalfe, 2006). However, this advantage also appears to be relatively modest (Kimball, Smith, & Muntean, 2012) and wasn’t apparent in a group of older learners (Tullis & Benjamin, 2012). Taken together, all of the evidence seems to support the claim that self-control of learning can be effective, but only when learners have good ideas about what an effective learning strategy is.

One factor that appears to have a big effect and that learners do not always appear to understand is the effect of scheduling repetitions of study. If you are studying for a final exam next week and plan to spend a total of five hours, what is the best way to distribute your study? The evidence is clear that spacing one’s repetitions apart in time is superior than massing them all together (Baddeley & Longman, 1978; Bahrick, Bahrick, Bahrick, & Bahrick, 1993; Melton, 1967). Increasing the spacing between consecutive presentations appears to benefit learning yet further (Landauer & Bjork, 1978).

A similar advantage is evident for the practice of interleaving multiple skills to be learned: For example, baseball batters improved more when they faced a mix of different types of pitches than when they faced the same pitches blocked by type (Hall, Domingues, & Cavazos, 1994). Students also showed better performance on a test when different types of mathematics problems were interleaved rather than blocked during learning (Taylor & Rohrer, 2010).

One final factor that merits discussion is the role of testing . Educators and students often think about testing as a way of assessing knowledge, and this is indeed an important use of tests. But tests themselves affect memory, because retrieval is one of the most powerful ways of enhancing learning (Roediger & Butler, 2013). Self-testing is an underutilized and potent means of making learning more durable.

General Principles of Learning

We’ve only begun to scratch the surface here of the many variables that affect the quality and content of learning (Mullin, Herrmann, & Searleman, 1993). But even within this brief examination of the differences between people and the activities they engage in can we see some basic principles of the learning process.

The value of effective metacognition

To be able to guide our own learning effectively, we must be able to evaluate the progress of our learning accurately and choose activities that enhance learning efficiently. It is of little use to study for a long time if a student cannot discern between what material she has or has not mastered, and if additional study activities move her no closer to mastery. Metacognition describes the knowledge and skills people have in monitoring and controlling their own learning and memory. We can work to acquire better metacognition by paying attention to our successes and failures in estimating what we do and don’t know, and by using testing often to monitor our progress.

Transfer-appropriate processing

Sometimes, it doesn’t make sense to talk about whether a particular encoding activity is good or bad for learning. Rather, we can talk about whether that activity is good for learning as revealed by a particular test . For example, although reading words for meaning leads to better performance on a test of recall or recognition than paying attention to the pronunciation of the word, it leads to worse performance on a test that taps knowledge of that pronunciation, such as whether a previously studied word rhymes with another word (Morris, Bransford, & Franks, 1977). The principle of transfer-appropriate processing states that memory is “better” when the test taps the same type of knowledge as the original encoding activity. When thinking about how to learn material, we should always be thinking about the situations in which we are likely to need access to that material. An emergency responder who needs access to learned procedures under conditions of great stress should learn differently from a hobbyist learning to use a new digital camera.

The value of forgetting

Forgetting is sometimes seen as the enemy of learning, but, in fact, forgetting is a highly desirable part of the learning process. The main bottleneck we face in using our knowledge is being able to access it. We have all had the experience of retrieval failure—that is, not being able to remember a piece of information that we know we have, and that we can access easily once the right set of cues is provided. Because access is difficult, it is important to jettison information that is not needed—that is, to forget it. Without forgetting, our minds would become cluttered with out-of-date or irrelevant information. And, just imagine how complicated life would be if we were unable to forget the names of past acquaintances, teachers, or romantic partners.

But the value of forgetting is even greater than that. There is lots of evidence that some forgetting is a prerequisite for more learning. For example, the previously discussed benefits of distributing practice opportunities may arise in part because of the greater forgetting that takes places between those spaced learning events. It is for this reason that some encoding activities that are difficult and lead to the appearance of slow learning actually lead to superior learning in the long run (Bjork, 2011). When we opt for learning activities that enhance learning quickly, we must be aware that these are not always the same techniques that lead to durable, long-term learning.

To wrap things up, let’s think back to the questions we began the module with. What might you now do differently when preparing for an exam? Hopefully, you will think about testing yourself frequently, developing an accurate sense of what you do and do not know, how you are likely to use the knowledge, and using the scheduling of tasks to your advantage. If you are learning a new skill or new material, using the scientific study of learning as a basis for the study and practice decisions you make is a good bet.

Outside Resources

Discussion Questions

• How would you best design a computer program to help someone learn a new foreign language? Think about some of the principles of learning outlined in this module and how those principles could be instantiated in “rules” in a computer program.
• Would you rather have a really good memory or really good metacognition? How might you train someone to develop better metacognition if he or she doesn’t have a very good memory, and what would be the consequences of that training?
• In what kinds of situations not discussed here might you find a benefit of forgetting on learning?
• Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology : General, 130, 224–237.
• Baddeley, A. D., & Longman, D. J. A. (1978). The influence of length and frequency of training session on the rate of learning to type. Ergonomics , 21, 627–635.
• Bahrick, H. P., Bahrick, L. E., Bahrick, A. S., & Bahrick, P. O. (1993). Maintenance of foreign language vocabulary and the spacing effect. Psychological Science , 4, 316–321.
• Bjork, R. A. (2011). On the symbiosis of learning, remembering, and forgetting. In A. S. Benjamin (Ed.), Successful remembering and successful forgetting: A Festschrift in honor of Robert A. Bjork (pp. 1–22). London, UK: Psychology Press.
• Castel, A. D., Benjamin, A. S., Craik, F. I. M., & Watkins, M. J. (2002). The effects of aging on selectivity and control in short-term recall. Memory & Cognition , 30, 1078–1085.
• Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology , 4, 55–81.
• Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior , 11, 671–684.
• Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior , 19, 450–466.
• Eagle, M., & Leiter, E. (1964). Recall and recognition in intentional and incidental learning. Journal of Experimental Psychology , 68, 58–63.
• Garavalia, L. S., & Gredler, M. E. (2002). Prior achievement, aptitude, and use of learning strategies as predictors of college student achievement. College Student Journal , 36, 616–626.
• Hall, K. G., Domingues, D. A., & Cavazos, R. (1994). Contextual interference effects with skilled baseball players. Perceptual and Motor Skills , 78, 835–841.
• Heyer, A. W., Jr., & O’Kelly, L. I. (1949). Studies in motivation and retention: II. Retention of nonsense syllables learned under different degrees of motivation. Journal of Psychology: Interdisciplinary and Applied , 27, 143–152.
• Jenkins, J. J. (1979). Four points to remember: A tetrahedral model of memory experiments. In L. S. Cermak & F. I. M. Craik (Eds.), Levels of processing and human memory (pp. 429–446). Hillsdale, NJ: Erlbaum.
• Kane, M. J., Conway, A. R. A., Hambrick, D. Z., & Engle, R. W. (2008). Variation in working memory capacity as variation in executive attention and control. In A. R. A. Conway, C. Jarrold, M. J. Kane, A. Miyake, & J. N. Towse (Eds.), Variation in Working Memory (pp. 22–48). New York, NY: Oxford University Press.
• Kimball, D. R., Smith, T. A., & Muntean, W. J. (2012). Does delaying judgments of learning really improve the efficacy of study decisions? Not so much. Journal of Experimental Psychology: Learning, Memory, and Cognition , 38, 923–954.
• Kornell, N., & Metcalfe, J. (2006). Study efficacy and the region of proximal learning framework. Journal of Experimental Psychology: Learning, Memory, & Cognition, 32, 609–622.
• Kyllonen, P. C., & Christal, R. E. (1990). Reasoning ability is (little more than) working memory capacity. Intelligence , 14, 389–433.
• Landauer, T. K. (1986). How much do people remember? Some estimates of the quantity of learned information in long-term memory. Cognitive Science, 10 , 477–493.
• Landauer, T. K., & Bjork, R. A. (1978). Optimum rehearsal patterns and name learning. In M. M. Gruneberg, P. E. Morris, & R. N. Sykes (Eds.), Practical aspects of memory (pp. 625–632). London: Academic Press.
• Melton, A. W. (1967). Repetition and retrieval from memory. Science , 158, 532.
• Morris, C. D., Bransford, J. D., & Franks, J. J. (1977). Levels of processing versus transfer appropriate processing. Journal of Verbal Learning and Verbal Behavior , 16, 519–533.
• Mullin, P. A., Herrmann, D. J., & Searleman, A. (1993). Forgotten variables in memory theory and research. Memory , 1, 43–64.
• Nairne, J. S., Pandeirada, J. N. S., & Thompson, S. R. (2008). Adaptive memory: the comparative value of survival processing. Psychological Science , 19, 176–180.
• Pinsker, H., Kupfermann, I., Castelluci, V., & Kandel, E. (1970). Habituation and dishabituation of the gill-withdrawal reflex in Aplysia. Science , 167, 1740–1742.
• Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology , 95, 667–686.
• Polley, D. B., Steinberg, E. E., & Merzenich, M. M. (2006). Perceptual learning directs auditory cortical map reorganization through top-down influences. The Journal of Neuroscience , 26, 4970–4982.
• Ramirez, G., & Beilock, S. L. (2011). Writing about testing worries boosts exam performance in the classroom. Science , 331, 211–213.
• Richardson-Klavehn, A. & Bjork, R.A. (1988). Measures of memory. Annual Review of Psychology , 39, 475–543.
• Roediger, H. L., & Butler, A.C. (2013). Retrieval practice (testing) effect. In H. L. Pashler (Ed.), Encyclopedia of the mind . Los Angeles, CA: Sage Publishing Co.
• Seger, C. A. (1994). Implicit learning. Psychological Bulletin , 115, 163–196.
• Simon, H. A. (1974). How big is a chunk? Science , 4124, 482–488.
• Taylor, K., & Rohrer, D. (2010). The effects of interleaved practice. Applied Cognitive Psychology , 24, 837–848.
• Tullis, J. G., & Benjamin, A. S. (2012). Consequences of restudy choices in younger and older learners. Psychonomic Bulletin & Review , 19, 743–749.
• Tullis, J. G., & Benjamin, A. S. (2011). On the effectiveness of self-paced learning. Journal of Memory and Language , 64, 109–118.

Management Notes

Factors Affecting Learning in Psychology – 6 Major Factors Affecting Learning | General Psychology

Learning is a permanent change in behavior due to experience, training, and practice. It brings some modification in behavior and once an individual learns new things it will last at least for some period of time. Generally, learning is need-directed.

Learning is directly or indirectly related to organizational behavior in terms of increasing competency, leadership ability, and motivation at work. It is necessary for every organization to generate new ideas, knowledge, concepts, strategies, technologies, understanding, behaviors, skill values, attitudes, preferences, etc. to cope with the changing environment of the organization.

Factors Affecting Learning in Psychology

Learning is a relatively permanent change in behavior by some practice, training, and experiences. It modifies some undesirable behavior to desirable behavior. The process of learning is comprehensive. There are many factors that affect this process, including the learner, the teacher, the process, and the content.

Teachers and parents can benefit greatly from a thorough understanding of these factors for guiding their children’s learning. Many internal and external factors influence the learning process. Some of the factors influencing learning are given below.

1) Individual motives

A motive is a person’s reason for choosing a specific behavior from among several alternatives. Motives are derived from needs. Human motives are created whenever there is a physiological or psychological imbalance and try to fill such imbalance.

It is the reason for action that gives purpose and direction to certain behavior. Motives are drivers that encourage people to act and learn.

2) Physiological factors

It includes the physical condition of a person like sense perception, physical health, fatigue, time of learning, food drink, atmospheric condition, age, etc.

a) Sensation and Perception

The psychological factors that contribute to learning, in addition to the general health of the student, are sensation and perception. Perception begins with sensation. The five sense organs are the skin, the ears, the tongue, the eyes, and the nose.

These sense organs serve as the gateways for knowledge and aid in the perception of various stimuli in the surrounding environment. A defect in any sense organ will affect learning and, therefore, knowledge acquisition.

b) Fatigue and Boredom

Fatigue is mental or physical exhaustion that impairs performance and competency. Boredom, on the other hand, is a lack of desire or aversion to work.

When one has such an aversion, one feels fatigued without actually feeling tired. When one studies, one rarely feels fatigued. Apart from causing the impression of fatigue, boredom leads to decreased student learning efficiency.

c) Age and Maturation

Learning is directly related to age and maturity. An individual cannot learn unless they are mature enough to do so. Depending on their age, some children are able to learn certain subjects more quickly, while others take longer.

d) Emotional Conditions

Learning is improved by desirable emotional conditions. Joy, satisfaction and happiness enhance learning in any situation. Conversely, adverse emotional conditions hinder learning. A number of studies have established that emotional strain, stress, tensions, disturbances, etc, are extremely detrimental to academic achievement.

e) Food and Drink

Proper nutrition is essential for mental performance. Learning is adversely affected by poor nutrition. The type of food also matters. Tobacco, caffeine, and alcohol, among other addictive items, have adverse effects on the neuromuscular system and consequently on learning capacity.

f) Atmospheric conditions

Humidity and high temperatures impact mental performance. Noise, inadequate lighting, low ventilation and physical discomfort (as we find in overcrowded schools and factories) hinder learning. There are a number of distractions that affect the ability to concentrate and therefore the efficiency of learning.

In the same way, learning is also influenced by mental ability like mental health, motivation and interest, success, praise, and blame, etc. All these factors are the outcome of genes and chromosomes from their parents. They are somehow uncontrollable.

For example, an individual suffering from bad health cannot think of learning new things.

3) Social factors

Social factors encourage learning for individuals. It includes social needs, rewards, and punishment, competition, suggestions, cooperation, etc. Social cultures encourage learning new knowledge having accepted by society and discourage knowledge gained discarded by society.

Similarly, learning whose consequences are rewarded by society is continued and those learning which are punishable are not continued. In general view, learning should be always positive and good result oriented.

Thus, society provides guidelines and support to individuals. Social factors include parents, family, peers, teachers, managers, reference groups, etc.

4) Environmental factors

Natural factors affecting learning are light, noise, cold, temperature, etc. learning needs a proper environment so that they can maintain patience and care.

Besides this environmental factors include working conditions, organizational setup, etc. all the surrounding should be in favor of learning. Only then a good learner can learn as effectively as he can.

a) Working conditions

Poor working conditions, distractions, noise, poor lighting, poor ventilation, overcrowding, awkward seating arrangements, and uncomfortable accommodations impede learning. School location, interior design, accommodation, decorations, and the quality of health and sanitary conditions play a major role in the efficiency of learning.

b) Organisational set-up

Learning is also affected by the organisational setup of the school. In order to draw the schedule, the psychological principles must be followed. Boredom and fatigue should be avoided. It is best to teach challenging subjects in the morning.

There should be a break after some periods. A democratic organization encourages a healthy learning environment. In order for pupils to be motivated to learn, there should be a healthy relationship between the teacher and the pupils. There should also be some competition between them.

Competitions between classes or between houses will motivate the students to test themselves harder in order to outdo each other. Jealousy and rivalry should, however, be avoided. We should strengthen group emulation and encourage pupils to participate actively.

They should not act passively when learning. The choice of subjects and activities for the pupils should be based on their age, ability, and aptitude. Children who are unguided may oscillate from one subject to another, and so don’t form any significant groups.

For example, a person cannot learn well in a noisy environment due to a bad organizational setup. He needs a peaceful environment to perceive new learning.

5) Nature of learning materials

The availability of learning materials affects the learning process of individuals. People need learning material on the basis of their area, and level of education.

Proper presentation and organization of materials, practical implementation of learning, special methods of learning, and timely testing can help to learn effectively.

The faculty also affect the learning pattern like management student need to learn by presentation, science needs to learn by a lab test, and art students need to learn by practice.

Similarly, the level of education and understanding also affect learning patterns. For example, an illiterate person needs more attention to learn a basic thing than that of a literate.

6) Methodology of Instructions

• Presentation and Organization of Materials

The learning material should be well-planned and organized. It should be graded according to the mental level of the students. The presentation should be meaningful and interesting.

• Learning by Doing

The only way to become perfect is to practice. Practicing and repetition are essential to learning. Students should be encouraged to engage in active learning. A practical application of knowledge, experimentation, and personal application should replace theoretical teaching.

Experiencing something personally makes learning better. Verbalization should be limited to the bare minimum.

• Special Methods of Learning

Some special methods have been found to be more effective. Both the whole method and the part method have been advocated when learning poetry. It is sometimes helpful to recall what is learned and recite it from memory. Gestalt psychologists don’t believe in ‘trial and error learning’.

Instead, they promote learning by insight. In other words, they discourage mechanical repetition without understanding.

• Timely Testing

The learner knows his exact achievement through tests, so there is no room for overestimation or underestimation. Periodic and occasional testing motivates students to be regular in their studies.

Factors Affecting Learning Quiz/MCQs

This is a factor that is defined by how you see yourself a) Intelligence b) Self c) Emotions d) Cumulative learning

What is punishment? a) Anything that the subject will work to avoid b) Any unpleasant stimulus c) Anything that the subject dislikes d) Anything that decreases the frequency of a behavior

Tend to increase the chances that a particular behavior will be repeated a) Reward b) Punishment c) Reinforcement d) Signals

One of the factors that contributes to learning when you tend to relate what you see and hear a) The self b) Past experience c) Intelligence d) Motivation

What is the factor of learning that makes you want to learn? a) Self b) Intelligence c) Emotions d) Motivation

When enthusiasm for a subject can make you want to learn more a) Intelligence b) Emotions c) Past experiences d) Feedback

A learning factor that helps when you receive help from a more experienced person a) Novelty b) Past experiences c) Cumulative learning d) Guidance

This learning factor uses videos, debates, and other fun activities is called what? a) Cumulative learning b) Novelty c) Emotions d) Intelligence

What learning facto is used when you receive the test results quickly? a)Novelty b) Feedback c) Intelligence d) Cumulative learning

This is the factor that is a sum of a number specific abilities and enables the person to solve problems and get new information quickly a) Self b) Feedback c) Intelligence d) Cumulative Learning

This is a factor that just builds on itself and knowledge is an ongoing process of learning a) Cumulative learning b) Past experience c) Motivation d) Self

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Passive Perception

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Psychology Discussion

Factors influencing learning | education.

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The following points highlight the four main factors influencing learning. The factors are: 1. Physiological Factors 2. Psychological Factors 3. Environmental Factors 4. Methodology of Instructions.

1. physiological factors:.

The physiological factors are sense perception, physical health, fatigue time and day of learning, food and drink, age and atmospheric conditions.

1. Sense-perception:

Sensation and perception are the basis of all cognitive learning. Weaker the power of perception, lesser the amount of learning. A blind man learns far less than a normal person. Impairment of sense organs is a handicap in the process of learning.

2. Physical Health:

Ill health hampers learning. Sound mind is only in a sound body. Sound physical health gives vigour and vitality to pursue learning activities for a longer education. A diseased person is handicapped by the normal physical strength necessary for any mental activity.

3. Fatigue:

Muscular or sensory fatigue causes mental boredom and indolence. A number of factors in the home and school environment may cause physical and mental fatigue, such as lack of accommodation, bad seating arrangement, unhealthy clothing, inadequate ventilation, poor light, noise over crowdingness, and pure nutrition. Longer homes of study also cause fatigue which affects the learning capacity.

4. Time of Learning:

Morning and evening hours are the best periods of study. During the day, there is decline in the mental capacity. Experiments on children have shown that there are great variations in learning efficiency during the different hours of the day.

5. Food and Drink:

Nutrition is responsible for efficient mental activity. Poor nutrition adversely affects learning. The type of food also has some effect. The alcoholic drinks, caffeine, tobacco and such addictive items have adverse effect on neuro-muscular system, and consequently upon the learning capacity.

6. Atmospheric conditions:

High temperature and humidity lower the mental efficiency. Low ventilation, lack of proper illumination, noise and physical discomfort (as we find in factories and overcrowded schools) hamper the learning capacity. Distractions of all sorts affect power of concentration and consequently the efficiency of learning.

Learning capacity varies with age. Some subjects can better be learnt at the early age, and some during adulthood. On the evidence of experiments conducted. Thorndike says that mental development does not stop at 16 or 18 but increases upto 23, and halts after 40. Learning proceeds rapidly between 18 and 20, remains stagnant till 25, and declines upto 35. Age accompanies mental maturation. So some complex problems cannot be solved till the person is sufficiently mature.

Children learn the school subjects more easily than uneducated adults can learn. This is perhaps because the children’s minds are not burdened with worldly problems, and they have more flexible nervous system. But there are instance when person of 50 made remarkable progress in learning new subjects like music, a foreign language. Mahatma Gandhi studied Hindi at the age of 40. Tagore began study fresh scientific subjects even after 50.

2. Psychological Factors:

1. Mental Health:

Mental tension, complexes, conflicts, mental illnesses and mental diseases hamper learning. A maladjusted child finds it difficult to concentrate. Concentration needs mental poise and absence of mental conflict or complex. Some pupils find it difficult to prepare for the university examination, simply because of fear of the examination and anxiety neurosis. A calm, serene and balanced mind her the power to concentrate and learn better.

2. Motivation and Interest:

No learning take place unless it is motivated. Purposeless learning is no learning at all. Every child is impelled by some motive to learn new things. In the absence of motivation, can he does not feel interested in the act of learning. A child’s behaviour in learning is energised by motives, selected by motives and directed by motives.

(i) Motives energise behaviour:

Hunger and thirst induce acquisition of food. Reward induces further success. Punishment or failure induces action for achievement.

(ii) Motives select behaviour:

Only those acts of learning are selected which are supported by some motive. A boy visits a village fair. He sees only those toys, objects or things that interest him.

(iii) Motives direct behaviour:

These activate the person, enthuse him and impel him to do the desired action. These direct his energies to reach the desired action. These direct his energies to reach the desired goal. Sultan of Kohlar was directed by hunger to reach the bananas, and that way he strived and learnt the way.

It will be desirable to create motivation in the instructional programme of the school. Children find their studies dull and boring without motivation. Hence learning should be made purposeful and meaningful. In words of Gates “Learning experiences are meaningful when they are invoked in his living, when they not only contribute to the purposes at the time but enable him to more intelligent adjustments in the future, when they invoke discovery and problem solving rather than formal drill or mere memorisation and when they result in satisfying social relationship.”

It does not mean that the teacher should always present false incentives for learning. False incentives prove harmful in the long run. What is needed is presentation of motives at the right moment and in the right way.

3. Success, Praise and Blame:

Nothing succeeds like success. Thorndike’s law of effect, is applicable most commonly. Experimental evidences show that praise stimulates small children to work and learn, although it does not produce much effect on superior and elder children. Elder children are more sensitive towards reproof and blame, than younger children are.

4. Rewards and Punishment:

Rewards of all sorts are powerful incentives to learn. But these days in India school rewards are more abused than used properly. A first division of distinction in the examination is a false reward. Work is its own rewards. Pupils forget this point. They become over-dependent on rewards. They refuse to work without any incentive of reward. All learning should not be and cannot be rewarded immediately.

Punishments, arousing fear in anticipation, may influence the pupil to work and learn, but not in all the cases. Sometimes punishment creates bad reaction, retaliation, hatred and disgust. Experimental studies show that punishment interfere with complex learning activities, when punishments become frequent. Absence of punishment becomes a basis of low activity on the part of the pupil. In the absence of fear, they disobey and waste time.

3. Environmental Factors:

1. Working conditions:

Learning is hampered by bad working conditions such as distraction, noise, poor illumination, bad ventilation, overcrowding, bad seating arrangement, and uncomfortable stay both at home and school. The location of the school, the internal set-up, the accommodation, decoration and healthful and sanitary conditions are very important for efficient learning.

2. Organisational set-up:

The organisational set-up of the school also influences learning.

(i) The time-table must be drawn, in accordance with the psychological principles. It should avoid fatigue and boredom. Difficult subjects should be taught in the morning. There should be interval after some periods.

(ii) The democratic organisation promotes a healthy atmosphere for learning.

(iii) The teacher-pupil relations should be healthy, so that there is mental cooperation and the pupils are motivated to learn.

(iv) There should be some sort of competition. The inter-class or inter- house competitions will stimulate the pupils to work more in order to outshine others. Rivalry and jealousy should, however, be avoided. Group emulation should be strengthened.

(v) The participation on the part of the pupils should be active. The pupil should not act as a passive learner.

(vi) Guidance in the selection of subjects and activities in accordance with age and ability and aptitude of the pupils should be provided. Unguided children may oscillate from one subject to another, and thus gather no mass.

4. Methodology of Instructions:

1. Presentation and Organisation of Material:

The learning material should be properly planned and organised. It should be graded to suit the mental level of the pupils. It should be presented in a meaningful and interesting manners.

2. Learning by Doing:

Practice makes a man perfect. Repetition and practice is important for learning. The pupils must be encouraged to learn through activity. Theoretical teaching should be replaced by practical application of knowledge, experimentation and personal application. Children learn better through personal experience. Verbalisation should be reduced to minimum.

3. Special Methods of Learning:

It has been found that some special methods give better results. In learning a piece of poetry, learning by the whole method, and by the part method have been advocated. Sometimes it is helpful to recall what is learnt and to recite by memory. Gestalt psychologists do not approve of ‘trial and error learning’. They advocate learning by insight. They discourage mechanical repetitions without understanding.

4. Timely Testing:

Through tests, the learner knows his exact achievement, and there is no scope for over-estimation or underestimation. Occasional and periodical testing motivates the pupil to be regular in his studies.

Related Articles:

• 2 Factors That Influence Learning
• Process of Learning: 4 Factors
• Top 3 Factors Influencing Personality Development
• Factors Influencing the Developmental Process in Individuals

Educational Psychology , Learning , Factors Influencing Learning

How People Learn II: Learners, Contexts, and Cultures (2018)

Chapter: 6 motivation to learn, 6 motivation to learn.

Motivation is a condition that activates and sustains behavior toward a goal. It is critical to learning and achievement across the life span in both informal settings and formal learning environments. For example, children who are motivated tend to be engaged, persist longer, have better learning outcomes, and perform better than other children on standardized achievement tests ( Pintrich, 2003 ). Motivation is distinguishable from general cognitive functioning and helps to explain gains in achievement independent of scores on intelligence tests ( Murayama et al., 2013 ). It is also distinguishable from states related to it, such as engagement, interest, goal orientation, grit, and tenacity, all of which have different antecedents and different implications for learning and achievement ( Järvelä and Renninger, 2014 ).

HPL I 1 emphasized some key findings from decades of research on motivation to learn:

• People are motivated to develop competence and solve problems by rewards and punishments but often have intrinsic reasons for learning that may be more powerful.
• Learners tend to persist in learning when they face a manageable challenge (neither too easy nor too frustrating) and when they see the value and utility of what they are learning.
• Children and adults who focus mainly on their own performance (such as on gaining recognition or avoiding negative judgments) are

___________________

1 As noted in Chapter 1 , this report uses the abbreviation “ HPL I ” for How People Learn: Brain, Mind, Experience, and School: Expanded Edition ( National Research Council, 2000 ).

less likely to seek challenges and persist than those who focus on learning itself.

• Learners who focus on learning rather than performance or who have intrinsic motivation to learn tend to set goals for themselves and regard increasing their competence to be a goal.
• Teachers can be effective in encouraging students to focus on learning instead of performance, helping them to develop a learning orientation.

In this chapter, we provide updates and additional elaboration on research in this area. We begin by describing some of the primary theoretical perspectives that have shaped this research, but our focus is on four primary influences on people’s motivation to learn. We explore research on people’s own beliefs and values, intrinsic motivation, the role of learning goals, and social and cultural factors that affect motivation to learn. We then examine research on interventions and approaches to instructional design that may influence motivation to learn, and we close with our conclusions about the implications of this research.

The research we discuss includes both laboratory and field research from multiple disciplines, such as developmental psychology, social psychology, education, and cognitive psychology.

THEORETICAL PERSPECTIVES

Research on motivation has been strongly driven by theories that overlap and contain similar concepts. A comprehensive review of this literature is beyond the scope of this report, but we highlight a few key points. Behavior-based theories of learning , which conceptualized motivation in terms of habits, drives, incentives, and reinforcement schedules, were popular through the mid-20th century. In these approaches, learners were assumed to be passive in the learning process and research focused mainly on individual differences between people (e.g., cognitive abilities, drive for achievement). These differences were presumed to be fixed and to dictate learners’ responses to features in the learning environment (method of instruction, incentives, and so on) and their motivation and performance.

Current researchers regard many of these factors as important but have also come to focus on learners as active participants in learning and to pay greater attention to how learners make sense of and choose to engage with their learning environments. Cognitive theories, for example, have focused on how learners set goals for learning and achievement and how they maintain and monitor their progress toward those goals. They also consider how physical aspects of the learning environment, such as classroom structures ( Ames, 1986 ) and social interactions (e.g., Gehlbach et al., 2016 ), affect learning through their impacts on students’ goals, beliefs, affect, and actions.

Motivation is also increasingly viewed as an emergent phenomenon , meaning it can develop over time and change as a result of one’s experiences with learning and other circumstances. Research suggests, for example, that aspects of the learning environment can both trigger and sustain a student’s curiosity and interest in ways that support motivation and learning ( Hidi and Renninger, 2006 ).

A key factor in motivation is an individual’s mindset : the set of assumptions, values, and beliefs about oneself and the world that influence how one perceives, interprets, and acts upon one’s environment ( Dweck, 1999 ). For example, a person’s view as to whether intelligence is fixed or malleable is likely to link to his views of the malleability of his own abilities ( Hong and Lin-Siegler, 2012 ). As we discuss below, learners who have a fixed view of intelligence tend to set demonstrating competence as a learning goal, whereas learners who have an incremental theory of intelligence tend to set mastery as a goal and to place greater value on effort. Mindsets develop over time as a function of learning experiences and cultural influences. Research related to mindsets has focused on patterns in how learners construe goals and make choices about how to direct attention and effort. Some evidence suggests that it is possible to change students’ self-attributions so that they adopt a growth mindset, which in turn improves their academic performance ( Blackwell et al., 2007 ).

Researchers have also tried to integrate the many concepts that have been introduced to explain this complex aspect of learning in order to formulate a more comprehensive understanding of motivational processes and their effects on learning. For example, researchers who study psychological aspects of motivation take a motivational systems perspective , viewing motivation as a set of psychological mechanisms and processes, such as those related to setting goals, engagement in learning, and use of self-regulatory strategies ( Kanfer, 2015 ; Linnenbrink-Garcia and Patall, 2016 ; Yeager and Walton, 2011 ).

LEARNERS’ BELIEFS AND VALUES

Learners’ ideas about their own competence, their values, and the preexisting interests they bring to a particular learning situation all influence motivation.

Self-Efficacy

When learners expect to succeed, they are more likely to put forth the effort and persistence needed to perform well. Self-efficacy theory ( Bandura, 1977 ), which is incorporated into several models of motivation and learning, posits that the perceptions learners have about their competency or capabilities are critical to accomplishing a task or attaining other goals ( Bandura, 1977 ).

According to self-efficacy theory, learning develops from multiple sources, including perceptions of one’s past performance, vicarious experiences, performance feedback, affective/physiological states, and social influences. Research on how to improve self-efficacy for learning has shown the benefits of several strategies for strengthening students’ sense of their competence for learning, including setting appropriate goals and breaking down difficult goals into subgoals ( Bandura and Schunk, 1981 ) and providing students with information about their progress, which allows them to attribute success to their own effort ( Schunk and Cox, 1986 ). A sense of competence may also foster interest and motivation, particularly when students are given the opportunity to make choices about their learning activities ( Patall et al., 2014 ).

Another important aspect of self-attribution involves beliefs about whether one belongs in a particular learning situation. People who come from backgrounds where college attendance is not the norm may question whether they belong in college despite having been admitted. Students may misinterpret short-term failure as reflecting that they do not belong, when in fact short-term failure is common among all college students. These students experience a form of stereotype threat, where prevailing cultural stereotypes about their position in the world cause them to doubt themselves and perform more poorly ( Steele and Aronson, 1995 ).

A recent study examined interventions designed to boost the sense of belonging among African American college freshmen ( Walton and Cohen, 2011 ). The researchers compared students who did and did not encounter survey results ostensibly collected from more senior college students, which indicated that most senior students had worried about whether they belonged during their first year of college but had become more confident over time. The students who completed the activity made significant academic gains, and the researchers concluded that even brief interventions can help people overcome the bias of prior knowledge by challenging that knowledge and supporting a new perspective.

Another approach to overcoming the bias of knowledge is to use strategies that can prevent some of the undesirable consequences of holding negative perspectives. One such strategy is to support learners in trying out multiple ideas before settling on the final idea. In one study, for example, researchers asked college students either to design a Web page advertisement for an online journal and then refine it several times or to create several separate ones ( Dow et al., 2010 ). The researchers posted the advertisements and assessed their effectiveness both by counting how many clicks each generated and by asking experts in Web graphics to rate them. The authors found that the designs developed separately were more effective and concluded that when students refined their initial designs, they were trapped by their initial decisions. The students who developed separate advertisements explored the possibilities more thoroughly and had more ideas to choose from.

Learners may not engage in a task or persist with learning long enough to achieve their goals unless they value the learning activities and goals. Expectancy-value theories have drawn attention to how learners choose goals depending on their beliefs about both their ability to accomplish a task and the value of that task. The concept of value encompasses learners’ judgments about (1) whether a topic or task is useful for achieving learning or life goals, (2) the importance of a topic or task to the learner’s identity or sense of self, (3) whether a task is enjoyable or interesting, and (4) whether a task is worth pursuing ( Eccles et al., 1983 ; Wigfield and Eccles, 2000 ).

Research with learners of various ages supports the idea that those who expect to succeed at a task exert more effort and have higher levels of performance ( Eccles and Wigfield, 2002 ). However, some studies have suggested that task valuation seems to be the strongest predictor of behaviors associated with motivation, such as choosing topics and making decisions about participation in training ( Linnenbrink-Garcia et al., 2008 ). Such research illustrates one of the keys to expectancy-value theory: the idea that expectancy and value dimensions work together. For example, a less-than-skilled reader may nevertheless approach a difficult reading task with strong motivation to persist in the task if it is interesting, useful, or important to the reader’s identity ( National Research Council, 2012c ). As learners experience success at a task or in a domain of learning, such as reading or math, the value they attribute to those activities can increase over time ( Eccles and Wigfield, 2002 ).

Learners’ interest is an important consideration for educators because they can accommodate those interests as they design curricula and select learning resources. Interest is also important in adult learning in part because students and trainees with little interest in a topic may show higher rates of absenteeism and lower levels of performance ( Ackerman et al., 2001 ).

Two forms of learner interest have been identified. Individual or personal interest is viewed as a relatively stable attribute of the individual. It is characterized by a learner’s enduring connection to a domain and willingness to re-engage in learning in that domain over time ( Schiefele, 2009 ). In contrast, situational interest refers to a psychological state that arises spontaneously in response to specific features of the task or learning environment ( Hidi and Renninger, 2006 ). Situational interest is malleable, can affect student engagement and learning, and is influenced by the tasks and materials educators use or encourage ( Hunsu et al., 2017 ). Practices that engage students and influence their attitudes may increase their personal interest and intrinsic motivation over time ( Guthrie et al., 2006 ).

Sometimes the spark of motivation begins with a meaningful alignment of student interest with an assignment or other learning opportunity. At other times, features of the learning environment energize a state of wanting to know more, which activates motivational processes. In both cases, it is a change in mindset and goal construction brought about by interest that explains improved learning outcomes ( Barron, 2006 ; Bricker and Bell, 2014 ; Goldman and Booker, 2009 ). For instance, when learner interest is low, students may be less engaged and more likely to attend to the learning goals that require minimal attention and effort.

Many studies of how interest affects learning have included measures of reading comprehension and text recall. This approach has allowed researchers to assess the separate effects of topic interest and interest in a specific text on how readers interact with text, by measuring the amount of time learners spend reading and what they learn from it. Findings from studies of this sort suggest that educators can foster students’ interest by selecting resources that promote interest, by providing feedback that supports attention ( Renninger and Hidi, 2002 ), by demonstrating their own interest in a topic, and by generating positive affect in learning contexts (see review by Hidi and Renninger, 2006 ).

This line of research has also suggested particular characteristics of texts that are associated with learner interest. For example, in one study of college students, five characteristics of informational texts were associated with both interest and better recall: (1) the information was important, new, and valued; (2) the information was unexpected; (3) the text supported readers in making connections with prior knowledge or experience; (4) the text contained imagery and descriptive language; and (5) the author attempted to relate information to readers’ background knowledge using, for example, comparisons and analogies ( Wade et al., 1999 ). The texts that students viewed as less interesting interfered with comprehension in that they, for example, offered incomplete or shallow explanations, contained difficult vocabulary, or lacked coherence.

A number of studies suggest that situational interest can be a strong predictor of engagement, positive attitudes, and performance, including a study of students’ essay writing ( Flowerday et al., 2004 ) and other research (e.g., Alexander and Jetton, 1996 ; Schraw and Lehman, 2001 ). These studies suggest the power of situational interest for engaging students in learning, which has implications for the design of project-based or problem-based learning. For example, Hoffman and Haussler (1998) found that high school girls displayed significantly more interest in the physics related to the working of a pump when the mechanism was put into a real-world context: the use of a pump in heart surgery.

The perception of having a choice may also influence situational interest and engagement, as suggested by a study that examined the effects of classroom practices on adolescents enrolled in a summer school science course

( Linnenbrink-Garcia et al., 2013 ). The positive effect learners experience as part of interest also appears to play a role in their persistence and ultimately their performance (see, e.g., Ainley et al., 2002 ).

Intrinsic Motivation

Self-determination theory posits that behavior is strongly influenced by three universal, innate, psychological needs—autonomy (the urge to control one’s own life), competence (the urge to experience mastery), and psychological relatedness (the urge to interact with, be connected to, and care for others). Researchers have linked this theory to people’s intrinsic motivation to learn ( Deci and Ryan, 1985 , 2000 ; Ryan and Deci, 2000 ). Intrinsic motivation is the experience of wanting to engage in an activity for its own sake because the activity is interesting and enjoyable or helps to achieve goals one has chosen. From the perspective of self-determination theory ( Deci and Ryan, 1985 , 2000 ; Ryan and Deci, 2000 ), learners are intrinsically motivated to learn when they perceive that they have a high degree of autonomy and engage in an activity willingly, rather than because they are being externally controlled. Learners who are intrinsically motivated also perceive that the challenges of a problem or task are within their abilities.

External Rewards

The effect of external rewards on intrinsic motivation is a topic of much debate. External rewards can be an important tool for motivating learning behaviors, but some argue that such rewards are harmful to intrinsic motivation in ways that affect persistence and achievement.

For example, some research suggests that intrinsic motivation to persist at a task may decrease if a learner receives extrinsic rewards contingent on performance. The idea that extrinsic rewards harm intrinsic motivation has been supported in a meta-analysis of 128 experiments ( Deci et al., 1999 , 2001 ). One reason proposed for such findings is that learners’ initial interest in the task and desire for success are replaced by their desire for the extrinsic reward ( Deci and Ryan, 1985 ). External rewards, it is argued, may also undermine the learner’s perceptions of autonomy and control.

Other research points to potential benefits. A recent field study, for example, suggests that incentives do not always lead to reduced engagement after the incentive ends ( Goswami and Urminsky, 2017 ). Moreover, in some circumstances external rewards such as praise or prizes can help to encourage engagement and persistence, and they may not harm intrinsic motivation over the long term, provided that the extrinsic reward does not undermine the individual’s sense of autonomy and control over her behavior (see National Research Council, 2012c , pp. 143–145; also see Cerasoli et al.,

2016 ; Vansteenkiste et al., 2009 ). Thus, teaching strategies that use rewards to capture and stimulate interest in a topic (rather than to drive compliance), that provide the student with encouragement (rather than reprimands), and that are perceived to guide student progress (rather than just monitor student progress) can foster feelings of autonomy, competence, and academic achievement (e.g., Vansteenkist et al., 2004 ). Praise is important, but what is praised makes a difference (see Box 6-1 ).

Other work ( Cameron et al., 2005 ) suggests that when rewards are inherent in the achievement itself—that is, when rewards for successful completion of a task include real privileges, pride, or respect—they can spur intrinsic motivation. This may be the case, for example, with videogames in which individuals are highly motivated to play well in order to move to the next higher level. This may also be the case when learners feel valued and respected for their demonstrations of expertise, as when a teacher asks a student who correctly completed a challenging homework math problem to explain his solution to the class. Extrinsic rewards support engagement sufficient for learning, as shown in one study in which rewards were associated with enhanced memory consolidation but only when students perceived the material to be boring ( Murayama and Kuhbandner, 2011 ). Given the prevalence

BOX 6-1 What You Praise Makes a Difference

of different performance-based incentives in classrooms (e.g., grades, prizes), a better, more integrated understanding is needed of how external rewards may harm or benefit learners’ motivation in ways that matter to achievement and performance in a range of real-world conditions across the life span.

Effects of Choice

When learners believe they have control over their learning environment, they are more likely to take on challenges and persist with difficult tasks, compared with those who perceive that they have little control ( National Research Council, 2012c ). Evidence suggests that the opportunity to make meaningful choices during instruction, even if they are small, can support autonomy, motivation, and ultimately, learning and achievement ( Moller et al., 2006 ; Patall et al., 2008 , 2010 ). 2

Choice may be particularly effective for individuals with high initial interest in the domain, and it may also generate increased interest ( Patall, 2013 ). One possible reason why exercising choice seems to increase motivation is that the act of making a choice induces cognitive dissonance: a feeling of being uncomfortable and unsure about one’s decision. To reduce this feeling, individuals tend to change their preferences to especially value and become interested in the thing they chose ( Izuma et al., 2010 ). Knowing that one has made a choice (“owning the choice”) can protect against the discouraging effects of negative feedback during the learning process, an effect that has been observed at the neurophysiological level ( Murayama et al., 2015 ). The perception of choice also may affect learning by fostering situational interest and engagement ( Linnenbrink-Garcia et al., 2013 ).

THE IMPORTANCE OF GOALS

Goals—the learner’s desired outcomes—are important for learning because they guide decisions about whether to expend effort and how to direct attention, foster planning, influence responses to failure, and promote other behaviors important for learning ( Albaili, 1998 ; Dweck and Elliot, 1983 ; Hastings and West, 2011 ).

Learners may not always be conscious of their goals or of the motivation processes that relate to their goals. For example, activities that learners perceive as enjoyable or interesting can foster engagement without the learner’s

2 The 2008 study was a meta-analysis, so the study populations are not described. The 2010 study included a total of 207 (54% female) high school students from ninth through twelfth grade. A majority (55.5%) of the students in these classes were Caucasian, 28 percent were African American, 7 percent were Asian, 3 percent were Hispanic, 1.5 percent were Native American, and 5 percent were of other ethnicities.

conscious awareness. Similarly, activities that learners perceive as threatening to their sense of competence or self-esteem (e.g., conditions that invoke stereotype threat, discussed below 3 ) may reduce learners’ motivation and performance even (and sometimes especially) when they intend to perform well.

HPL I made the point that having clear and specific goals that are challenging but manageable has a positive effect on performance, and researchers have proposed explanations. Some have focused on goals as motives or reasons to learn ( Ames and Ames, 1984 ; Dweck and Elliott, 1983 ; Locke et al., 1981 ; Maehr, 1984 ; Nicholls, 1984 ). Others have noted that different types of goals, such as mastery and performance goals, have different effects on the cognitive, affective, and behavioral processes that underlie learning as well as on learners’ outcomes ( Ames and Archer, 1988 ; Covington, 2000 ; Dweck, 1986 ). Research has also linked learners’ beliefs about learning and achievement, or mindsets, with students’ pursuit of specific types of learning goals ( Maehr and Zusho, 2009 ). The next section examines types of goals and research on their influence.

Types of Goals

Researchers distinguish between two main types of goals: mastery goals , in which learners focus on increasing competence or understanding, and performance goals , in which learners are driven by a desire to appear competent or outperform others (see Table 6-1 ). They further distinguish between performance-approach and performance-avoidance goals ( Senko et al., 2011 ). Learners who embrace performance-avoidance goals work to avoid looking incompetent or being embarrassed or judged as a failure, whereas those who adopt performance-approach goals seek to appear more competent than others and to be judged socially in a favorable light. Within the category of performance-approach goals, researchers have identified both self-presentation goals (“wanting others to think you are smart”) and normative goals (“wanting to outperform others”) ( Hulleman et al., 2010 ).

Learners may simultaneously pursue multiple goals ( Harackiewicz et al., 2002 ; Hulleman et al., 2008 ) and, depending on the subject area or skill domain, may adopt different achievement goals ( Anderman and Midgley, 1997 ). Although students’ achievement goals are relatively stable across the school years, they are sensitive to changes in the learning environment, such as moving from one classroom to another or changing schools ( Friedel et al., 2007 ). Learning environments differ in the learning expectations, rules, and

3 When an individual encounters negative stereotypes about his social identity group in the context of a cognitive task, he may underperform on that task; this outcome is attributed to stereotype threat ( Steele, 1997 ).

TABLE 6-1 Mindsets, Goals, and Their Implications for Learning

structure that apply, and as a result, students may shift their goal orientation to succeed in the new context ( Anderman and Midgley, 1997 ).

Dweck (1986) argued that achievement goals reflect learners’ underlying theories of the nature of intelligence or ability: whether it is fixed (something with which one is born) or malleable. Learners who believe intelligence is malleable, she suggested, are predisposed toward adopting mastery goals, whereas learners who believe intelligence is fixed tend to orient toward displaying competence and adopting performance goals ( Burns and Isbell, 2007 ; Dweck, 1986 ; Dweck and Master, 2009 ; Mangels et al., 2006 ). Table 6-1 shows how learners’ mindsets can relate to their learning goals and behaviors.

Research in this area suggests that learners who strongly endorse mastery goals tend to enjoy novel and challenging tasks ( Pintrich, 2000 ; Shim et al., 2008 ; Witkow and Fuligni, 2007 ; Wolters, 2004 ), demonstrate a greater willingness to expend effort, and engage higher-order cognitive skills during learning ( Ames, 1992 ; Dweck and Leggett, 1988 ; Kahraman and Sungur, 2011 ; Middleton and Midgley, 1997 ). Mastery students are also persistent—even in the face of failure—and frequently use failure as an opportunity to seek feedback and improve subsequent performance ( Dweck and Leggett, 1988 ).

Learners’ mastery and performance goals may also influence learning and achievement through indirect effects on cognition. Specifically, learners with mastery goals tend to focus on relating new information to existing knowledge as they learn, which supports deep learning and long-term memory for the

information. By contrast, learners with performance goals tend to focus on learning individual bits of information separately, which improves speed of learning and immediate recall but may undermine conceptual learning and long-term recall. In this way, performance goals tend to support better immediate retrieval of information, while mastery goals tend to support better long-term retention ( Crouzevialle and Butera, 2013 ). Performance goals may in fact undermine conceptual learning and long-term recall. When learners with mastery goals work to recall a previously learned piece of information, they also activate and strengthen memory for the other, related information they learned. When learners with performance goals try to recall what they learned, they do not get the benefit of this retrieval-induced strengthening of their memory for other information ( Ikeda et al., 2015 ).

Two studies with undergraduate students illustrate this point. Study participants who adopted performance goals were found to be concerned with communicating competence, prioritizing areas of high ability, and avoiding challenging tasks or areas in which they perceived themselves to be weaker than others ( Darnon et al., 2007 ; Elliot and Murayama, 2008 ). These students perceived failure as a reflection of their inability and typically responded to failure with frustration, shame, and anxiety. These kinds of performance-avoidance goals have been associated with maladaptive learning behaviors including task avoidance ( Middleton and Midgley, 1997 ; sixth-grade students), reduced effort ( Elliot, 1999 ), and self-handicapping ( Covington, 2000 ; Midgley et al., 1996 ).

The adoption of a mastery goal orientation to learning is likely to be beneficial for learning, while pursuit of performance goals is associated with poor learning-related outcomes. However, research regarding the impact of performance goals on academic outcomes has yielded mixed findings ( Elliot and McGregor, 2001 ; Midgley et al., 2001 ). Some researchers have found positive outcomes when learners have endorsed normative goals (a type of performance goal) ( Covington, 2000 ; Linnenbrink, 2005 ). Others have found that achievement goals do not have a direct effect on academic achievement but operate instead through the intermediary learning behaviors described above and through self-efficacy ( Hulleman et al., 2010 ).

Influence of Teachers on Learners’ Goals

Classrooms can be structured to make particular goals more or less salient and can shift or reinforce learners’ goal orientations ( Maehr and Midgley, 1996 ). Learners’ goals may reflect the classroom’s goal structure or the values teachers communicate about learning through their teaching practices (e.g., how the chairs are set up or whether the teacher uses cooperative learning groups) (see Kaplan and Midgley, 1999 ; Urdan et al., 1998 ). When learners perceive mastery goals are valued in the classsroom, they are more likely

TABLE 6-2 Achievement Goals and Classroom Climate

SOURCE: Adapted from Ames and Archer (1988 , Tbl. 1, p. 261).

to use information-processing strategies, self-planning, and self-monitoring strategies ( Ames and Archer, 1988 ; Schraw et al., 1995 ). A mastery-oriented structure in the classroom is positively correlated with high academic competency and negatively related to disruptive behaviors. Further, congruence in learners’ perceptions of their own and their school’s mastery orientation is associated with positive academic achievement and school well-being ( Kaplan and Maehr, 1999 ).

Teachers can influence the goals learners adopt during learning, and learners’ perceptions of classroom goal structures are better predictors of learners’ goal orientations than are their perceptions of their parents’ goals. Perceived classroom goals are also strongly linked to learners’ academic efficacy in the transition to middle school. Hence, classroom goal structures are a particularly important target for intervention ( Friedel et al., 2007 ; Kim et al., 2010 ). Table 6-2 summarizes a longstanding view of how the prevailing classroom goal structure—oriented toward either mastery goals or performance goals—affects the classroom climate for learning. However, more experimental research is needed to determine whether interventions designed to influence such mindsets benefit learners.

Learning Goals and Other Goals

Academic goals are shaped not only by the immediate learning context but also by the learners’ goals and challenges, which develop and change

throughout the life course. Enhancing a person’s learning and achievement requires an understanding of what the person is trying to achieve: what goals the individual seeks to accomplish and why. However, it is not always easy to determine what goals an individual is trying to achieve because learners have multiple goals and their goals may shift in response to events and experiences. For example, children may adopt an academic goal as a means of pleasing parents or because they enjoy learning about a topic, or both. Teachers may participate in an online statistics course in order to satisfy job requirements for continuing education or because they view mastery of the topic as relevant to their identity as a teacher, or both.

At any given time, an individual holds multiple goals related to achievement, belongingness, identity, autonomy, and sense of competence that are deeply personal, cultural, and subjective. Which of these goals becomes salient in directing behavior at what times depends on the way the individual construes the situation. During adolescence, for example, social belongingness goals may take precedence over academic achievement goals: young people may experience greater motivation and improved learning in a group context that fosters relationships that serve and support achievement. Over the life span, academic achievement goals also become linked to career goals, and these may need to be adapted over time. For example, an adolescent who aspires to become a physician but who continually fails her basic science courses may need to protect her sense of competence by either building new strategies for learning science or revising her occupational goals.

A person’s motivation to persist in learning in spite of obstacles and setbacks is facilitated when goals for learning and achievement are made explicit, are congruent with the learners’ desired outcomes and motives, and are supported by the learning environment, as judged by the learner; this perspective is illustrated in Box 6-2 .

Future Identities and Long-Term Persistence

Long-term learning and achievement tend to require not only the learner’s interest, but also prolonged motivation and persistence. Motivation to persevere may be strengthened when students can perceive connections between their current action choices (present self) and their future self or possible future identities ( Gollwitzer et al., 2011 ; Oyserman et al., 2015 ). The practice of displaying the names and accomplishments of past successful students is one way educators try to help current students see the connection.

Researchers have explored the mechanisms through which such experiences affect learning. Some neurobiological evidence, for example, suggests that compelling narratives that trigger emotions (such as admiration elicited by a story about a young person who becomes a civil rights leader for his community) may activate a mindset focused on a “possible future” or values

BOX 6-2 Learners’ Perceptions of the Learning Environment Can Inadvertently Undermine Motivation

( Immordino-Yang et al., 2009 ). Similar research also points to an apparent shifting between two distinct neural networks that researchers have associated with an “action now” mindset (with respect to the choices and behaviors for executing a task during learning) and a “possible future/values oriented”

mindset (with respect to whether difficult tasks are ones that “people like me” do) ( Immordino-Yang et al., 2012 ). Students who shift between these two mindsets may take a reflective stance that enables them to inspire themselves and to persist and perform well on difficult tasks to attain future goals ( Immordino-Yang and Sylvan, 2010 ).

Practices that help learners recognize the motivational demands required and obstacles to overcome for achieving desired future outcomes also may support goal attainment, as suggested in one study of children’s attempts to learn foreign-language vocabulary words ( Gollwitzer et al., 2011 ). Research is needed, however, to better establish the efficacy of practices designed to shape learners’ thinking about future identities and persistence

SOCIAL AND CULTURAL INFLUENCES ON MOTIVATION

All learners’ goals emerge in a particular cultural context. As discussed in Chapter 2 , the way individuals perceive and interpret the world and their own role in it, and their expectations about how people function socially, reflect the unique set of influences they have experienced. The procedures people use to complete tasks and solve problems, as well as the social emotional dispositions people bring to such tasks, are similarly shaped by context and experience ( Elliott et al., 2001 ; Oyserman, 2011 ). In this section, the committee discusses three specific lines of research that illustrate the importance of culturally mediated views of the self and social identities to learners’ perceptions of learning environments, goals, and performance.

Cross-Cultural Differences in Learners’ Self-Construals

Over the past several decades, researchers have attempted to discern the influence of culture on a person’s self-construal, or definition of herself in reference to others. In an influential paper, Markus and Kitayama (1991) distinguished between independent and interdependent self-construals and proposed that these may be associated with individualistic or collectivistic goals. For example, they argued that East Asian cultures tend to emphasize collectivistic goals, which promote a comparatively interdependent self-construal in which the self is experienced as socially embedded and one’s accomplishments are tied to the community. In contrast, they argued, the prevailing North American culture tends to emphasize individualistic goals and an individualistic self-construal that prioritizes unique traits, abilities, and accomplishments tied to the self rather than to the community.

Although assigning cultural groups to either a collectivist or individualistic category oversimplifies very complex phenomena, several large-sample

survey studies have offered insights about the ways learners who fit these two categories tend to vary in their assessment of goals, the goals they see as relevant or salient, and the ways in which their goals relate to other phenomena such as school achievement ( King and McInerney, 2016 ). For example, in cross-cultural studies of academic goals, Dekker and Fischer (2008) found that gaining social approval in achievement contexts was particularly important for students who had a collectivist perspective. This cultural value may predispose students to adopt goals that help them to avoid the appearance of incompetence or negative judgments (i.e., performance-avoidance goals) ( Elliot, 1997 , 1999 ; Kitayama, Matsumoto, and Norasakkunkit, 1997 ).

More recent work has also explored the relationships between such differences and cultural context. For example, several studies have compared students’ indications of endorsement for performance-avoidance goals and found that Asian students endorsed these goals to a greater degree than European American students did ( Elliot et al., 2001 ; Zusho and Njoku, 2007 ; Zusho et al., 2005 ). This body of work seems to suggest that though there were differences, the performance avoidance may also have different outcomes in societies in which individualism is prioritized than in more collectivistic ones. These researchers found that performance-avoidance goals can be adaptive and associated with such positive academic outcomes as higher levels of engagement, deeper cognitive processing, and higher achievement. (See also the work of Chan and Lai [2006] on students in Hong Kong; Hulleman et al. [2010] ; and the work of King [2015] on students in the Philippines.)

Although cultures may vary on average in their emphasis on individualism and collectivism, learners may think in either individualistic and collectivistic terms if primed to do so ( Oyserman et al., 2009 ). For example, priming interventions such as those that encourage participants to call up personal memories of cross-cultural experiences ( Tadmor et al., 2013 ) have been used successfully to shift students from their tendency to take one cultural perspective or the other. Work on such interventions is based on the assumption that one cultural perspective is not inherently better than the other: the most effective approaches would depend on what the person is trying to achieve in the moment and the context in which he is operating. Problem solving is facilitated when the salient mindset is well matched to the task at hand, suggesting that flexibility in cultural mindset also may promote flexible cognitive functioning and adaptability to circumstances ( Vezzali et al., 2016 ).

This perspective also suggests the potential benefits of encouraging learners to think about problems and goals from different cultural perspectives. Some evidence suggests that these and other multicultural priming interventions improve creativity and persistence because they cue individuals to think of problems as having multiple possible solutions. For instance, priming learners to adopt a multicultural mindset may support more-divergent thinking about multiple possible goals related to achievement, family, identity, and

friendships and more flexible action plans for achieving those goals. Teachers may be able to structure learning opportunities that incorporate diverse perspectives related to cultural self-construals in order to engage students more effectively ( Morris et al., 2015 ).

However, a consideration for both research and practice moving forward is that there may be much more variation within cultural models of the self than has been assumed. In a large study of students across several nations that examined seven different dimensions related to self-construal ( Vignoles et al., 2016 ), researchers found neither a consistent contrast between Western and non-Western cultures nor one between collectivistic and individualistic cultures. To better explain cultural variation, the authors suggested an ecocultural perspective that takes into account racial/ethnic identity.

Social Identity and Motivation Processes

Identity is a person’s sense of who she is. It is the lens through which an individual makes sense of experiences and positions herself in the social world. Identity has both personal and social dimensions that play an important role in shaping an individual’s goals and motivation. The personal dimensions of identity tend to be traits (e.g., being athletic or smart) and values (e.g., being strongly committed to a set of religious or political beliefs). Social dimensions of identity are linked to social roles or characteristics that make one recognizable as a member of a group, such as being a woman or a Christian ( Tajfel and Turner, 1979 ). They can operate separately (e.g., “an African American”) or in combination (“an African American male student”) ( Oyserman, 2009 ).

Individuals tend to engage in activities that connect them to their social identities because doing so can support their sense of belonging and esteem and help them integrate into a social group. This integration often means taking on the particular knowledge, goals, and practices valued by that group ( Nasir, 2002 ). The dimensions of identity are dynamic, malleable, and very sensitive to the situations in which people find themselves ( Oyserman, 2009 ; Steele, 1997 ). This means the identity a person takes on at any moment is contingent on the circumstances

A number of studies indicate that a positive identification with one’s racial or ethnic identity supports a sense of school belonging, as well as greater interest, engagement, and success in academic pursuits. For example, African American adolescents with positive attitudes toward their racial/ethnic group express higher efficacy beliefs and report more interest and engagement in school ( Chavous et al., 2003 ). The value of culturally connected racial/ethnic identity is also evident for Mexican and Chinese adolescents ( Fuligni et al., 2005 ). In middle school, this culturally connected identity is linked to higher grade-point averages among African American ( Altschul et al., 2006 ; Eccles et al., 2006 ), Latino ( Oyserman, 2009 ), and Native American students in North

BOX 6-3 Basketball, Mathematics, and Identity

America ( Fryberg et al., 2013 ). The research described in Box 6-3 illustrates the potential and powerful influence of social identity on learners’ engagement with a task.

Stereotype Threat

The experience of being evaluated in academic settings can heighten self-awareness, including awareness of the stereotypes linked to the social group to which one belongs and that are associated with one’s ability ( Steele, 1997 ). The effects of social identity on motivation and performance may be positive, as illustrated in the previous section, but negative stereotypes can lead people to underperform on cognitive tasks (see Steele et al., 2002 ; Walton and Spencer, 2009 ). This phenomenon is known as stereotype threat , an unconscious worry that a stereotype about one’s social group could be applied to oneself or that one might do something to confirm the stereotype ( Steele, 1997 ). Steele has noted that stereotype threat is most likely in areas of performance in which individuals are particularly motivated.

In a prototypical experiment to test stereotype threat, a difficult achievement test is given to individuals who belong to a group for whom a negative stereotype about ability in that achievement domain exists. For example, women are given a test in math. The test is portrayed as either gender-neutral

(women and men do equally well on it) or—in the threat condition—as one at which women do less well. In the threat condition, members of the stereotyped group perform at lower levels than they do in the gender-neutral condition. In the case of women and math, for instance, women perform more poorly on the math test than would be expected given their actual ability (as demonstrated in other contexts) ( Steele and Aronson, 1995 ). Several studies have replicated this finding ( Beilock et al., 2008 ; Dar-Nimrod and Heine, 2006 ; Good et al., 2008 ; Spencer et al., 1999 ), and the finding is considered to be robust, especially on high-stakes tests such as the SAT ( Danaher and Crandall, 2008 ) and GRE.

The effects of negative stereotypes about African American and Latino students are among the most studied in this literature because these stereotypes have been persistent in the United States ( Oyserman et al., 1995 ). Sensitivity to these learning-related stereotypes appears as early as second grade ( Cvencek et al., 2011 ) and grows as children enter adolescence ( McKown and Strambler, 2009 ). Among college-age African Americans, underperformance occurs in contexts in which students believe they are being academically evaluated ( Steele and Aronson, 1995 ). African American school-age children perform worse on achievement tests when they are reminded of stereotypes associated with their social group ( Schmader et al., 2008 ; Wasserberg, 2014 ). Similar negative effects of stereotype threat manifest among Latino youth ( Aronson and Salinas, 1997 ; Gonzales et al., 2002 ; Schmader and Johns, 2003 ).

Stereotype threat is believed to undermine performance by lowering executive functioning and heightening anxiety and worry about what others will think if the individual fails, which robs the person of working memory resources. Thus, the negative effects of stereotype threat may not be as apparent on easy tasks but arise in the context of difficult and challenging tasks that require mental effort ( Beilock et al., 2007 ).

Neurophysiological evidence supports this understanding of the mechanisms underlying stereotype threat. Under threatening conditions, individuals show lower levels of activation in the brain’s prefrontal cortex, reflecting impaired executive functioning and working memory ( Beilock et al., 2007 ; Cadinu et al., 2005 ; Johns et al., 2008 ; Lyons and Beilock, 2012 ; Schmader and Jones, 2003 ) and higher levels of activation in fear circuits, including, for example, in the amygdala ( Spencer et al., 1999 ; Steele and Aronson, 1995 ).

In the short term, stereotype threat can result in upset, distraction, anxiety, and other conditions that interfere with learning and performance ( Pennington et al., 2016 ). Stereotype threat also may have long-term deleterious effects because it can lead people to conclude that they are not likely to be successful in a domain of performance ( Aronson, 2004 ; Steele, 1997 ). It has been suggested that the longer-term effects of stereotype threat may be one cause of longstanding achievement gaps ( Walton and Spencer, 2009 ). For example, women for whom the poor-at-math stereotype was primed reported

more negative thoughts about math ( Cadinu et al., 2005 ). Such threats can be subtly induced. In one classroom study, cues in the form of gendered objects in the room led high school girls to report less interest in taking computer science courses ( Master et al., 2015 ).

Students can maintain positive academic self-concepts in spite of negative stereotypes when supported in doing so ( Anderman and Maehr, 1994 ; Graham, 1994 ; Yeager and Walton, 2011 ). For example, a study by Walton and Spencer (2009) illustrates that under conditions that reduce psychological threat, students for whom a stereotype about their social group exists perform better than nonstereotyped students at the same level of past performance (see Figure 6-1 ).

These findings highlight an important feature of stereotype threat: it is not a characteristic solely of a person or of a context but rather a condition that results from an interaction between the two. To be negatively affected, a person must be exposed to and perceive a potential cue in the environment and be aware of a stereotype about the social group with which he identifies ( Aronson et al., 1999 ). For example, in a study of African American children in an urban elementary school, introduction of a reading test as an index of ability hampered performance only among students who reported being aware of racial stereotypes about intelligence ( Walton and Spencer, 2009 ).

It also appears that the learner must tie her identity to the domain of skills

being tested. For example, students who have a strong academic identity and value academic achievement highly are more vulnerable to academic stereotype threat than are other students ( Aronson et al., 1999 ; Keller, 2007 ; Lawrence et al., 2010 ; Leyens et al., 2000 ; Steele, 1997 ).

Researchers have identified several actions educators can take that may help to manage stereotype threat. One is to remove the social identity characteristic (e.g., race or gender) as an evaluating factor, thereby reducing the possibility of confirming a stereotype ( Steele, 1997 ). This requires bolstering or repositioning dimensions of social identity. Interventions of this sort are likely to work not because they reduce the perception of, or eliminate, stereotype threat, but because they change students responses to the threatening situation ( Aronson et al., 2001 ; Good et al., 2003 ). For example, learners can be repositioned as the bearers of knowledge or expertise, which can facilitate identity shifts that enable learners to open up to opportunities for learning ( Lee, 2012 ). In research that confronted women with negative gender-based stereotypes about their performance in mathematics but prompted them to think of other aspects of their identity, the women performed on par with men and appeared to be buffered against the deleterious effects of gender-based stereotypes. Women who did not receive the encouragement performed worse than their male counterparts ( Gresky et al., 2005 ). Such findings suggest that having opportunities to be reminded of the full range of dimensions of one’s identity may promote resilience against stereotype threats. Notably, interventions that have addressed stereotype threat tend to target and support identity rather than self-esteem. However, clear feedback that sets high expectations and assures a student that he can reach those expectations are also important ( Cohen and Steele, 2002 ; Cohen et al., 1999 ).

Values-affirmation interventions are designed to reduce self-handicapping behavior and increase motivation to perform. Enabling threatened individuals to affirm their talents in other domains through self-affirmations has in some situations strengthened students’ sense of self ( McQueen and Klein, 2006 ). Values-affirmation exercises in which students write about their personal values (e.g., art, sports, music) have bolstered personal identity, reduced threat, and improved academic performance among students experiencing threat ( Cohen et al., 2006 , 2009 ; Martens et al., 2006 ). In randomized field experiments, self-affirmation tasks were associated with better grades for middle school students ( Cohen et al., 2006 , 2009 ) 4 and college students ( Miyake et al., 2010 ). However, other studies have not replicated these findings (e.g., Dee, 2015 ; Hanselman et al., 2017 ), so research is needed to determine for whom and under which conditions values-affirmation approaches may be effective.

Although research suggests steps that educators can take that may help to

4 The 2006 study included 119 African American and 119 European American students; the 2009 study was a 2-year follow-up with the same sample.

eliminate stereotype threat, much of this research has been in highly controlled settings. The full range of factors that may be operating and interacting with one another has yet to be fully examined in real-world environments. However, educators can take into account the influences that research has identified as potentially causing, exacerbating, or ameliorating the effects of stereotype threat on their own students’ motivation, learning, and performance.

INTERVENTIONS TO IMPROVE MOTIVATION

Many students experience a decline in motivation from the primary grades through high school ( Gallup, Inc., 2014 ; Jacobs et al., 2002 ; Lepper et al., 2005 ). Researchers are beginning to develop interventions motivated by theories of motivation to improve student motivation and learning.

Some interventions focus on the psychological mechanisms that affect students’ construal of the learning environment and the goals they develop to adapt to that environment. For example, a brief intervention was designed to enhance student motivation by helping learners to overcome the negative impact of stereotype threat on social belongingness and sense of self ( Yeager et al., 2016 ). In a randomized controlled study, African American and European American college students were asked to write a speech that attributed adversity in learning to a common aspect of the college-adjustment process rather than to personal deficits or their ethnic group ( Walton and Cohen, 2011 ). After 3 years, African American students who had participated in the intervention reported less uncertainty about belonging and showed greater improvement in their grade point averages compared to the European American students.

One group of interventions to address performance setbacks has focused on exercises to help students shift from a fixed view of intelligence to a growth theory of intelligence. For example, in 1-year-long study, middle school students attended an eight-session workshop in which they either learned about study skills alone (control condition) or both study skills and research on how the brain improves and grows by working on challenging tasks (the growth mindset condition). At the end of the year, students in the growth mindset condition had significantly improved their math grades compared to students who only learned about study skills. However, the effect size was small and limited to a small subset of underachieving students ( Blackwell et al., 2007 ).

The subjective and personal nature of the learner’s experiences and the dynamic nature of the learning environment require that motivational interventions be flexible enough to take account of changes in the individual and in the learning environment. Over the past decade, a number of studies have suggested that interventions that enhance both short- and long-term motivation and achievement using brief interventions or exercises can be effective (e.g., Yeager and Walton, 2011 ). The interventions that have shown sustained effects on aspects of motivation and learning are based on relatively brief activities

and exercises that directly target how students interpret their experiences, particularly their challenges in school and during learning.

The effectiveness of brief interventions appears to stem from their impact on the individual’s construal of the situation and the motivational processes they set in motion, which in turn support longer-term achievement. Brief interventions to enhance motivation and achievement appear to share several important characteristics. First, the interventions directly target the psychological mechanisms that affect student motivation rather than academic content. Second, the interventions adopt a student-centric perspective that takes into account the student’s subjective experience in and out of school. Third, the brief interventions are designed to indirectly affect how students think or feel about school or about themselves in school through experience, rather than attempting to persuade them to change their thinking, which is likely to be interpreted as controlling. Fourth, these brief interventions focus on reducing barriers to student motivation rather than directly increasing student motivation. Such interventions appear particularly promising for African American students and other cultural groups who are subjected to negative stereotypes about learning and ability. However, as Yeager and Walton (2011) note, the effectiveness of these interventions appears to depend on both context and implementation.

Studies such as these are grounded in different theories of motivation related to the learners’ cognition, affect, or behavior and are intended to affect different aspects of motivation. Lazowski and Hulleman (2016) conducted a meta-analysis of research on such interventions to identify their effects on outcomes in education settings. The studies included using measures of authentic education outcomes (e.g., standardized test scores, persistence at a task, course choices, or engagement) and showed consistent, small effects across intervention type.

However, this meta-analysis was small: only 74 published and unpublished papers met criteria for inclusion, and the included studies involved a wide range of theoretical perspectives, learner populations, types of interventions, and measured outcomes. These results are not a sufficient basis for conclusions about practice, but further research may help identify which interventions work best for whom and under which conditions, as well as factors that affect implementation (such as dosage, frequency, and timing). Improvements in the ability to clearly define, distinguish among, and measure motivational constructs could improve the validity and usefulness of intervention research.

CONCLUSIONS

When learners want and expect to succeed, they are more likely to value learning, persist at challenging tasks, and perform well. A broad constellation of factors and circumstances may either trigger or undermine students’ desire

to learn and their decisions to expend effort on learning, whether in the moment or over time. These factors include learners’ beliefs and values, personal goals, and social and cultural context. Advances since the publication of HPL I provide robust evidence for the importance of both an individual’s goals in motivation related to learning and the active role of the learner in shaping these goals, based on how that learner conceives the learning context and the experiences that occur during learning. There is also strong evidence for the view that engagement and intrinsic motivation develop and change over time—these are not properties of the individual or the environment alone.

While empirical and theoretical work in this area continues to develop, recent research does strongly support the following conclusion:

CONCLUSION 6-1: Motivation to learn is influenced by the multiple goals that individuals construct for themselves as a result of their life and school experiences and the sociocultural context in which learning takes place. Motivation to learn is fostered for learners of all ages when they perceive the school or learning environment is a place where they “belong” and when the environment promotes their sense of agency and purpose.

More research is needed on instructional methods and how the structure of formal schooling can influence motivational processes. What is already known does support the following general guidance for educators:

CONCLUSION 6-2: Educators may support learners’ motivation by attending to their engagement, persistence, and performance by:

• helping them to set desired learning goals and appropriately challenging goals for performance;
• creating learning experiences that they value;
• supporting their sense of control and autonomy;
• developing their sense of competency by helping them to recognize, monitor, and strategize about their learning progress; and
• creating an emotionally supportive and nonthreatening learning environment where learners feel safe and valued.

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There are many reasons to be curious about the way people learn, and the past several decades have seen an explosion of research that has important implications for individual learning, schooling, workforce training, and policy.

In 2000, How People Learn: Brain, Mind, Experience, and School: Expanded Edition was published and its influence has been wide and deep. The report summarized insights on the nature of learning in school-aged children; described principles for the design of effective learning environments; and provided examples of how that could be implemented in the classroom.

Since then, researchers have continued to investigate the nature of learning and have generated new findings related to the neurological processes involved in learning, individual and cultural variability related to learning, and educational technologies. In addition to expanding scientific understanding of the mechanisms of learning and how the brain adapts throughout the lifespan, there have been important discoveries about influences on learning, particularly sociocultural factors and the structure of learning environments.

How People Learn II: Learners, Contexts, and Cultures provides a much-needed update incorporating insights gained from this research over the past decade. The book expands on the foundation laid out in the 2000 report and takes an in-depth look at the constellation of influences that affect individual learning. How People Learn II will become an indispensable resource to understand learning throughout the lifespan for educators of students and adults.

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What are the factors that affect learning at your school?

Subscribe to the economic studies bulletin, lauren bauer lauren bauer fellow - economic studies , associate director - the hamilton project @laurenlbauer.

September 10, 2019

Reducing chronic absence and developing conditions for learning are instrumental to improving outcomes for students and can be improved through policy reform and leadership. Schools and educators have the power to improve both student attendance and conditions for learning.

Chronic Absence: School and Community Factors

This Hamilton Project at Brookings interactive map shows rates of chronic absence along with relevant school and community factors for every school in the country. You can search by zip code or school name; click on schools to discover more information. By gradespan, schools with lower rates of chronic absence are shown in yellow and schools with the highest rates of chronic absence are shown in red.

Community Factors

Student chronic absence, placeholder.

Source: The Hamilton Project

Chronic absence, which is typically defined as a student missing 10 percent or more of school for any reason, signifies that a student is missing so much school that they are academically at risk. In addition, the four school conditions for learning include physical and emotional health and safety; sense of belonging, connectedness, and support; academic challenge and engagement; and social and emotional competence for students and adults.

A new Hamilton Project data interactive, “Chronic Absence: School and Community Factors,” examines the factors that affect learning at local elementary, middle, and high schools across the United States. The interactive provides a range of information for every school in the nation, including:

• Student Chronic Absence (2015-16): the share of students at a school who missed more than 15 days; • Exclusionary Discipline/Week (2015-16): the frequency of in- and out-of-school suspensions; • English and Math Proficient (2016-17; 2017-18): the share of students who were proficient in English/Language Arts or Math by state-determined standards; • Student:Teacher Ratio (2015-16): the ratio of students to full-time equivalent classroom teachers; • Student:Support Staff Ratio (2015-16): the ratio of students to nurses, psychologists, social workers, and counselors; and, • Teacher Attendance (2015-16): the share of full-time equivalent classroom teachers who were absent more than 10 days.

The interactive allows users to search by zip code or school name and click on schools to discover more information. The interactive also shows whether a school is in the top or bottom quintile of a given measure, so users can see how a school compares to others throughout the nation.

“Chronic Absence: School and Community Factors” accompanies a new paper titled, “ Using Chronic Absence Data to Improve Conditions for Learning ” that I co-authored with Hedy Chang and Jane Sundius of Attendance Works and David Osher and Mara Schanfield of the American Institutes for Research. In this paper, we describe how education leaders, community partners, and policymakers can address inequities and improve student outcomes by creating conducive learning environments that encourage students to come to school. The paper also details the complex relationships between community, school, and public policy factors that affect both student outcomes and the set of policies and practices to improve attendance and conditions for learning.

While national chronic absence data are increasingly accurate and widely available, the same is not true of national data on the level of physical, social, and emotional health and safety in schools or the other three conditions for learning that we have described in the report. In response, the interactive provides data points for each school that proxy these concepts, such as suspension rates and student–teacher ratios.

The data interactive also goes outside the school building, displaying an index of community factors that affect learning by zip code. The variables that compose the index include: the share of the adult population who are high school dropouts, the adult employment-to-population ratio, the share of children living in poverty, the share of children without health insurance, the share of children living in the same home as the previous year, household median income in the zip code, the extent of residential racial segregation, life expectancy, and average daily air quality in the county. In the interactive, light blue shading reflects more supportive community conditions (i.e. higher values of the index) and darker blue shading reflects less-supportive community conditions (i.e. lower values of the index). While the zip code in which a school is located may not fully represent a particular school’s attendance zone, where a school is situated provides context for school-level conditions for learning.

The map also shows school rates of chronic absence by grade level, from lower levels of chronic absence (yellow) to higher (red). By clicking on a school, users will see a set of proxies that reflect school conditions for learning. These proxies include the frequency of exclusionary disciplinary incidents (suspensions), student achievement as measured by proficiency in English/language arts and math, student–teacher ratio, student–support staff ratio, and the share of teachers who missed 10 or more days of school. The data for community factors are from 2013–17; for chronic absence, discipline, and teacher factors from 2015–16; and student achievement from 2016–17 or 2017–18 where available. Student achievement data from 2017-18 is italicized.

The Every Student Succeeds Act’s enactment and the decision of 36 states and Washington DC to hold schools accountable for reducing rates of chronic absence signifies the priority given to improving school attendance. The Hamilton Project has many resources for tackling chronic absence:

• Those interested in looking more closely at how rates of chronic absence vary across different student characteristics and schools can find those details in two interactives: “ Chronic Absence across the United States ” and “ Chronic School Absenteeism in the United States .” These Hamilton Project interactive maps allow anyone to explore rates of chronic absence at the school, district, state, and national levels by student and school characteristics.

• Those interested in the relationship between chronic absence and school accountability policy can find two Hamilton Project strategy papers, “ Lessons for Broadening School Accountability under the Every Student Succeeds Act ” and “ Reducing Chronic Absenteeism under the Every Student Succeeds Act .” These reports lay out a rationale for holding schools accountable for improving attendance and a framework for states as they oversee ESSA implementation. These papers describe the incidence of chronic absence, present analyses of factors that relate to chronic absence, and describe evidence-based strategies for schools as they work to reduce rates of chronic absence among students.

These resources jointly shine a light on the problem of chronic absence and illuminate actionable paths forward for school leaders and policymakers working toward improving school conditions for learning.

You can read a technical appendix here.

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Factors Affecting Learning Satisfaction in Face-to-Face and Non-Face-to-Face Flipped Learning among Nursing Students

Mi-kyoung cho.

1 Department of Nursing Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju 28644, Korea; moc.liamg@ohckmaic

Mi Young Kim

2 College of Nursing, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea

Factors influencing students’ learning satisfaction may differ between face-to-face and non-face-to-face flipped learning. For non-face-to-face flipped learning, which was widely employed during the COVID-19 pandemic, it is necessary to examine the impacts on learning satisfaction, which may vary depending on professor–student interaction rather than individual competencies, such as SDL readiness. This descriptive study, conducted 2 March 2019 to 24 June 2020, included 89 s-year, flipped-learning nursing students (28 face-to-face, 61 non-face-to-face). Students completed questionnaires about learning satisfaction, SDL readiness, and professor–student interaction. The data, collected using e-surveys, were analyzed using descriptive statistics, t -test, ANOVA, Pearson’s correlation, and multiple stepwise regression with IBM’s SPSS Statistics 25.0 program. The total average score of learning satisfaction (38.19 ± 6.04) was positively correlated with SDL readiness ( r = 0.56, p < 0.001) and professor–student interaction ( r = 0.36, p = 0.001), although total learning satisfaction was significantly different between the face-to-face and the non-face-to-face groups ( t = 5.28, p = 0.024). They were also significant influencing factors, along with face-to-face flipped learning, for total learning satisfaction ( F = 18.00, p < 0.001, explanatory power = 36.7%), suggesting flipped learners in non-face-to-face contexts must increase engagement beyond professor–student interaction.

1. Introduction

1.1. study rationale.

In the era of the COVID-19 pandemic, the role of nurses with hands-on experience has become important, and it has become critical for nursing education institutions to train nurses with the competency to perform duties in an actual clinical setting. Accordingly, there is a call for the transformation of university education, including the education of nurses who can demonstrate competency in complex clinical settings [ 1 ]. In a situation where improving the quality of university-level education is required, face-to-face classes were impossible due to the COVID-19 pandemic, and non-face-to-face classes became inevitable [ 2 ]. As classes transitioned to online models, lack of university resources or confusion, such as system server instability and learning-management system failure, became problematic issues, and improvement in this regard has become necessary [ 3 ]. Such a sudden shift in circumstances revealed concerns about aspects of the educational system, such as deterioration in the quality of university-level education [ 4 ] However, in addition to the systematic problems that occurred in the process of transitioning to a non-face-to-face class format in recent years, the sudden complete transition to non-face-to-face classes brought difficulty and confusion, which was a sudden change in learning method without a preparation period, due to unpredictable situations for students.

Learners who experienced a sudden transition to online classes due to the COVID-19 pandemic experienced low levels of class satisfaction and class efficacy [ 5 ] as well as difficulties, such as using efficient time management, decreased concentration, maladaptation to online learning, and the absence of communication between the learner and the instructors [ 6 ]. Further, the fact that there is no physical place for the class and that the time could be adjusted flexibly meant that the students’ autonomy increased, but self-directed learning in this environment became even more important.

The importance of self-directed learning ability has been emphasized even before the COVID-19 pandemic. Universities introduced and applied flipped learning [ 7 ], emphasizing learner-led learning instead of unilateral teaching and learning. Flipped learning is a teaching and learning method in which students listen to lectures individually using online and digital contents outside the classroom and perform various learning activities, including assignments, in the classroom. It is a method of participating in learner-centered, interactive lessons, such as problem-solving activities or discussions in the classroom [ 8 ]. As effects of flipped learning, improvements have been reported for academic achievement and satisfaction [ 9 , 10 ], class participation and interest [ 11 ], and self-efficacy [ 12 ].

Currently, all university-level education is being conducted non-face-to-face due to the COVID-19 pandemic. However, even after the COVID-19 pandemic is over, non-face-to-face classes may still need to be conducted due to similar circumstances. Therefore, various face-to-face teaching and learning methods should also be available to be implemented non-face-to-face. A typical form of flipped learning is a combination of non-face-to-face pre-learning and face-to-face learning activities. Therefore, it is necessary to examine the effects of implementing flipped learning completely non-face-to-face, that is, when the in-class course has been flexibly transformed as non-face-to-face and when various types of flipped learning are implemented. One essential aspect of transitioning to a new method of teaching is learning satisfaction, which is a state of mind obtained when the learner has achieved the purpose of learning or the individual learner’s expectations are met [ 13 ]; it has factors that could potentially affect learning performance. Owing to the fact that learning satisfaction with the class of college students is based on the evaluation of the quality of college education and improvement of academic ability [ 14 ], it can be seen that learning satisfaction has a comprehensive meaning, including achievement. Therefore, it is necessary to check the level of satisfaction when a new learning method is introduced and identify the factors that affect this satisfaction.

One such factor is the self-directed learning readiness emphasized in flipped learning. Self-directed learning readiness is a key factor in flipped learning; thus, it is necessary to identify whether there is a difference in self-directed learning readiness when the in-class content is delivered face-to-face or non-face-to-face and whether this could affect learning satisfaction. Another factor that could affect satisfaction is the interaction between the instructor and the learner, which is emphasized in flipped learning [ 7 ]. However, what each learner actually feels and their perceived level of intensity of the interaction may vary [ 15 ]. The most representative characteristic of online education is that, unlike face-to-face educational activities, all interactions must rely on the medium used [ 16 ]. Furthermore, it is necessary to analyze the differences in interactions between face-to-face and non-face-to-face learning environments and online interactions [ 17 ].

Arguably, advancements in IT technology demand the shift toward dismantling schools’ boundaries, removing division between subjects, doing away with traditional educational culture, changing the role of instructors and learners, and moving toward space-oriented rather than location-oriented education [ 18 ], and the pandemic has accelerated these changes. Although the COVID-19 pandemic may be over someday, similar scenarios may require non-face-to-face implementation. Flipped learning is flexible, as its form can be changed, and classes can be operated in various ways. In this study, we sought to examine the effects of face-to-face flipped and non-face-to-face flipped learning satisfaction, self-directed learning readiness, and professor–student interaction and identify the factors that affect learner satisfaction when flipped learning is used as a teaching method. In addition, we intended to investigate the flexible adaptability of flipped learning to provide foundational data for future use in configuring various teaching methods.

1.2. Objectives

This study was conducted with nursing students to examine the effects of face-to-face flipped learning and non-face-to-face flipped learning on learning satisfaction, self-directed learning readiness, and professor–student interaction. We also sought to identify the factors that affect learner satisfaction. Specific objectives are as follows:

• Identify the differences in learner satisfaction, self-directed learning readiness, and professor–student interaction between face-to-face flipped learning and non-face-to-face flipped learning;
• Identify learner satisfaction when flipped learning is used as a teaching and learning method;
• Identify the correlation among learner satisfaction, self-directed learning readiness, and professor–student interaction when flipped learning is used as a teaching and learning method; and
• Identify the factors that affect learner satisfaction when flipped learning is used as a teaching and learning method.

2. Materials and Methods

2.1. study design.

This study was conducted with undergraduate nursing students as a descriptive survey to identify the differences in self-directed learning readiness, professor–student interaction, and learner satisfaction after conducting face-to-face flipped learning and non-face-to-face flipped learning and identify the factors that affect the learning satisfaction of nursing students. The flow of the study process is shown in Figure 1 .

Flow diagram of the study (based on the CONSORT statement).

2.2. Study Participants

The study participants included 89 s-year undergraduate students at a university who were taking course in a Nursing and English; those who understood the objectives of the study and provided voluntary consent were permitted to participate. Using the G*power version 3.1.2 [ 19 ] with a two-sided significance level (α) of 0.05, median effect size ( d ) of 0.15 for Cohen’s multiple regression, power (1-β) of 0.80 with four predictors, and a 10% dropout rate for the calculated number of participants, the target number of participants was 95. Participants were recruited at the orientation for a Nursing and English course; 28 students consented to participation in the study in 2019, and 61 students consented in 2020 for a total of 89 nursing students who consented to the participation in the study. The questionnaires were collected without any dropouts to be used in the final analysis. The final power was 0.97.

As the study was conducted as a questionnaire survey, there was no harm for the participants. However, as the participants were students enrolled in courses, best efforts were made to protect participants’ voluntary participation and personal information. At the orientation of the courses, the researcher first explained the objectives of the study, study method, data collection time points, and the students’ right to withdraw at any time without any academic penalty for refusing. Next, the students were informed that the data obtained from the questionnaire survey would not be used for purposes other than research.

2.3. Class Delivery Method

The study was conducted while ensuring that the key factors of flipped learning, such as student-directed approach, asynchronous content delivery, and the learning method combining information technology [ 8 ], were faithfully followed for face-to-face and non-face-to-face groups. The methods were identical with the exception of the in-class part in flipped learning; the specifics of the procedures are as follows.

2.3.1. Pre-Class

Provision of class materials.

In both face-to-face and non-face-to-face flipped learning, the instructor provided the materials to be learned during class through eCampus Blackboard. The eCampus Blackboard is a web-based learning system and electronic community center for students and faculty. The instructor provided videos of medical terminology to be learned for each week, with the native English speakers repeating each word twice along with the instruction materials on a Nursing and English for communication on the job as the foundation for learning required courses for the major as a way of providing various types of materials to enable a more efficient pre-class learning. This course is a sophomore subject regarding some basics of nursing—learning medical terminology for communication; thus, it has little direct relevance to learning direct nursing skills. Therefore, communication-related videos and terms that can be used in nursing practice were used as online content.

Preliminary Learning

In both face-to-face and non-face-to-face flipped learning, the instruction video was assigned as a required learning material among various types of pre-class learning materials provided. The learners were allowed to adjust and manage the quantity, pace, and hours of studying for themselves. The instruction materials and the terminology videos were provided to help the learners use their free time to learn according to their own circumstances and learning schedule. This pre-class learning method allowed for a student-directed approach and asynchronous content delivery.

Practice Exercises

In both groups, students were given practice exercises from the textbook to complete after the pre-class. The practice exercises in the textbook were given to ensure that the students completed the required learning [ 20 ] as online learning is performed by the students at home.

2.3.2. In-Class

Quizzes were completed in class. For the face-to-face flipped learning group, the instructor administered weekly 10-min online quizzes consisting of 10 questions (five multiple-choice, five on accurate spelling of medical terminology) in eCampus Blackboard to check that the students completed the pre-learning. For the non-face-to-face group, students were allowed to go online at their convenience to take the quiz within 10 min before and 30 min after the start of the class. A timer was set to 10 min; at that point, the online quiz was automatically terminated, and the answers had to be submitted. Additionally, the questions shown to the students were only displayed on the screen once in random order, and the students were not allowed to go back to the previous screen so that the previously submitted answers could not be revised, and the answers could not be shared among students.

Case Building

Case building was done in the class; for the face-to-face flipped learning group, the instructor put six students in each group and gave 30 min to create a patient case using the medical terminology learned for that week on patients’ symptoms, diagnosis, treatment, and nursing, while going around the classroom to provide instructions for each group. Once the students prepared the PPT presentation and uploaded it on the discussion board in eCampus Blackboard, 10 groups each gave a three-minute presentation on various cases and received feedback. For the non-face-to-face flipped learning group, once the instructor assigned groups and group activities, the students were allowed to discuss them in groups without supervision; feedback was provided as comments from the instructor and students on the PPT presentation uploaded to the discussion board in eCampus Blackboard.

Q&A

Q&A was a class activity; for the face-to-face flipped learning group, the instructor provided immediate feedback on the various scenarios and situations presented by the students, issues discovered during discussions and applications through case analysis, answers to practice exercises, and questions on online quizzes. Instead of answering each question directly on the online bulletin board, the gist of each question was summarized and provided as an additional explanation during face-to-face class. For the non-face-to-face flipped learning group, the questions uploaded on the weekly Q&A in eCampus Blackboard were answered by the instructor or learners in a comment. Questions posted on the online bulletin board were each given an answer.

In both face-to-face and non-face-to-face flipped learning, the students uploaded the materials they wanted to share from the contents related to the weekly learning that they learned in addition after the pre-class learning using the weekly Q&A session. In addition, continuous discussion and Q&A were held online in weekly Q&A sessions on the materials to be tested until the end of the quiz. Learners responded first to the questions uploaded by the students, and the instructor provided an additional response before class activities or before quizzes in case the response needed correction or additional explanation.

2.3.3. After Class

Follow-up learning was commonly applied in both face-to-face and non-face-to-face flipped learning. Students who scored less than 70 points in the online quizzes completed additional weekly learning and were asked to submit homework on additionally learned material in at least one sheet of A4 paper.

2.4. Study Tools

2.4.1. learner satisfaction.

Learner satisfaction was measured based on questionnaires developed by Jung and Lim [ 21 ] and revised to fit the purpose of the present study. The questionnaires consisted of ten items, with the two subcategories of general learner satisfaction and academic learner satisfaction that were scored on a five-point Likert-type scale from “highly satisfied” (score of 5) to “never satisfied” (score of 1), with mean scores ranging from 1 to 5; a higher score indicated higher learner satisfaction. Cronbach’s for this questionnaire was 0.870 in Jung and Lim [ 21 ], and the reliability of overall questionnaire was 0.904, the general learner satisfaction 0.877, and the academic learner satisfaction 0.830, respectively, in the present study.

2.4.2. Self-Directed Learning Readiness

Self-directed learning (SDL) readiness was measured using the 32-item Korean version of the Self-Directed Learning Readiness Scale revised by Kim et al. [ 22 ] and originally developed by Guglielmino [ 23 ]. SDL readiness consists of 32 questions in six sub-domains, including attachment to learning, self-confidence as a learner, openness to challenges, curiosity for learning, self-understanding, and acceptance of responsibility for learning. Scores were measured on a five-point Likert-type scale from “highly satisfied” (score of 5) to “never satisfied” (score of 1), with the mean score ranging from 1 to 5; a higher score indicated higher SDL readiness. In Kim et al.’s [ 22 ] study, the reliability of the scale in Cronbach’s α was 0.930; in the current study, the reliability of the six sub-domains were 0.756, 0.830, 0.761, 0.793, 0.745, and 0.704, respectively, and the reliability of the overall tool was 0.861.

2.4.3. Professor–Student Interaction

For professor–student interaction scale, the Questionnaire on Teacher–Student Interaction developed by Fisher [ 24 ] was modified and supplemented by Hyun et al. [ 25 ] for use. The professor–student interaction scale consists of 18 questions in total, with two sub-factors: intimacy and reliability. Scores were measured on a five-point Likert-type scale from “highly satisfied” (score of 5) to “never satisfied” (score of 1), with the total scores ranging from 18 to 90 and a higher score indicating more satisfying professor–student interaction. In Hyun et al.’s [ 25 ] study, the reliability of the scale in Cronbach’s α was 0.920; in the current study, it was 0.942, and the reliability of the two sub-factors were 0.878 and 0.942, respectively.

2.5. Data Collection

Data collection was conducted in a time difference design from 4 March 2019 to 14 June 2019 and from 16 March 2020 to 24 June 2021 with second-year students taking Nursing and English courses. In preliminary data collection, the researcher explained the objectives and method of the study as well as the timing of questionnaires during the orientation of the course. The questionnaires were completed by accessing the URL for preliminary questionnaire on the e-campus notice. The start screen of the questionnaire URL provided the information sheet and the informed consent form on the objectives of the study, class method, rights of the participants, and personal information protection. Accessing the questions on the questionnaire on the following page was allowed only if the students read the information sheet prior to starting the questionnaire and provided voluntary consent to study participation. Follow-up data collection was conducted by uploading the URL for the follow-up questionnaire on the e-campus notice after the final examination and sending the same URL to the students taking the courses via e-mail. To complete data collection in a short period, the student president of each department put up a notice on SNS requesting all those who responded to the preliminary questionnaire to participate; it was conducted through voluntary connection.

2.6. Data Analysis

Data analysis was conducted using IBM SPSS Statistics 25.0 program (IBM, Armonk, NY, USA). The characteristics of nursing students, learner satisfaction, self-directed learning readiness, and professor–student interaction were analyzed using means, standard deviations, frequency, and percentages. The preliminary test of homogeneity for characteristics of nursing students, learner satisfaction, self-directed learning readiness, and professor–student interaction was tested using the chi-square test before implementing flipped learning. After applying flipped learning, the differences in the learner satisfaction, self-directed learning readiness, and professor–student interaction between the face-to-face and non-face-to-face groups were analyzed using the independent t -test. The difference obtained by subtracting the pre-values from the post-values to correct for the pre-values in the post-value comparison for self-directed learning readiness was tested; professor–student interactions were found to be non-homogeneous in the preliminary test. The difference in learner satisfaction according to characteristics of nursing students was analyzed using the independent t -test, whereas the correlations among learner satisfaction, self-directed learning readiness, and professor–student interaction were analyzed using Pearson’s coefficient correlation. The characteristics of nursing students, self-directed learning readiness, professor–student interaction, and the effects of face-to-face flipped learning and non-face-to-face flipped learning on learner satisfaction were analyzed using the stepwise multiple regression analysis. The significance level of each statistic was selected from p < 0.05.

3.1. Participant Characteristics

Overall, 77 (86.5%) of the participants were less than 22 years old, and 72 (80.9%) were women. In the last semester, 49 participants (55.1%) had a mean GPA of 3.52. Prior to the flipped learning class, there was no difference in the participant characteristics between the face-to-face and non-face-to-face flipped learning groups ( Table 1 ).

Characteristics of the participants and homogeneity of the variables between the two participant groups ( n = 89).

* Fisher’s exact test.

3.2. Differences between Face-to-Face and Non-Face-to-Face Flipped Learning Groups after Flipped Learning

Before the flipped learning class, self-directed learning readiness, and professor–student interaction were not homogeneous between the face-to-face and non-face-to-face flipped learning groups. To correct for this non-homogeneity in self-directed learning readiness and professor–student interaction in advance, the difference in post-values and pre-values was compared. As a result, there was no difference in self-directed learning readiness between the two groups after flipped learning ( t = −1.15, p = 0.258). After flipped learning, the professor–student interaction was higher for the non-face-to-face flipped learning group than for the face-to-face flipped learning group, with statistical significance ( t = −4.31, p < 0.001). After flipped learning, the learner satisfaction in nursing students was higher in the face-to-face flipped learning group than in the non-face-to-face flipped learning group with statistical significance ( t = 5.28, p = 0.024; Table 2 ).

Comparison of variables in the two groups after flipped learning ( n = 89).

Abbreviations: SDL, self-directed learning, PSI, professor–student interaction.

3.3. Difference in Learner Satisfaction According to Participant Characteristics

After flipped learning, the learner satisfaction in nursing students was higher for students with a mean SDL readiness score of 87.24 or higher compared to those with a score of less than 87.24 for total scores ( t = −3.04, p = 0.003), general learner satisfaction ( t = −2.84, p = 0.006), and academic learner satisfaction ( t = −2.82, p = 0.006). There was no difference in learner satisfaction depending on other participant characteristics or variables ( Table 3 ).

Differences in learner satisfaction according to participant characteristics ( n = 89).

3.4. Correlations among Learner Satisfaction, SDL Readiness, and PSI after Flipped Learning Class

After flipped learning, the learner satisfaction in nursing students revealed that total scores, general learner satisfaction, and academic learner satisfaction were positively correlated with SDL readiness and PSI ( Table 4 ).

Correlations among the variables ( n = 89).

Abbreviation: SDL, self-directed learning, PSI, professor–student interaction.

3.5. Factors Affecting Learner Satisfaction after Flipped Learning Class

Table 5 shows the regression model for identifying the factors that affect learner satisfaction of nursing students after the flipped learning class. Gender, a categorical variable, was treated as a dummy variable, and age, last semester’s grades, SDL readiness, and PSI were entered as continuous variables and analyzed by a stepwise multiple regression method. When constructing the model, variables were selected based on a significance probability of 0.05, and variables were removed based on a significance probability of 0.10. In the learner satisfaction model, the tolerance limits between independent variables were all above 0.1, and the variance inflation index (VIF) also satisfied the criteria of less than 10, indicating that there is no problem of multicollinearity.

Factors influencing learner satisfaction ( n = 89).

* Reference: non-face-to-face flipped learning. Abbreviations: SDL, self-directed learning; PSI, professor–student interaction; VIF, variance inflating factor.

In the regression model for learner satisfaction of nursing students after flipped learning class, face-to-face flipped learning method ( t = 2.39, p = 0.019), SDL readiness ( t = 4.97, p < 0.001), and PSI ( t = 2.44, p = 0.017) were significant influencing factors, and the explanatory power of the regression model constructed with these three variables was 36.7% ( F = 18.00, p < 0.001). In the regression model for general learner satisfaction, a subdomain of learner satisfaction, SDL readiness ( t = 4.76, p < 0.001), and PSI ( t = 2.40, p = 0.019) were significant influencing factors, and the explanatory power of the regression model constructed with these two variables was 22.5% for general learner satisfaction ( F = 16.90, p < 0.001). In the regression model for academic learner satisfaction, SDL readiness ( t = 5.25, p < 0.001) and PSI ( t = 2.47, p = 0.015) were significant influencing factors, and the explanatory power of the regression model constructed with these two variables was 33.3% for academic learner satisfaction ( F = 22.97, p < 0.001).

4. Discussion

This study was conducted to examine the difference in self-directed learning readiness, professor–student interaction, and learning satisfaction for nursing students who had face-to-face classes with flipped learning and others who had non-face-to-face classes with flipped learning to identify the factors that affect learner satisfaction. First, self-directed learning readiness was improved for both the face-to-face and non-face-to-face flipped learning groups compared to before the class, but there was no significant intergroup difference. The lack of intergroup difference seems to be because the pre-class process was identical for both groups, as the students had to conduct self-directed learning in advance. Second, flipped learning allows for both student-directed approach and asynchronous content delivery through the learner’s preliminary learning [ 8 ], and these two factors were the common factors for both the face-to-face flipped learning and non-face-to-face flipped learning. Therefore, it seems that self-directed learning readiness did not show a significant difference between the two groups.

Results of this study revealed that professor–student interaction was significantly higher for non-face-to-face flipped learning than face-to-face flipped learning. In contrast, previous studies reported that it is difficult to ask questions of the instructor or obtain answers to questions during an online class [ 26 ] and that there is a lack of instructor–learner interaction online [ 27 ]. In general, it is believed that professor–student interaction is difficult in non-face-to-face classes due to not being in the same physical space. In learning, professor–student interaction is perceived as important by both the instructor and the learner [ 28 ], but the interaction can be perceived differently by each, and the learner’s perception of interactions is particularly important. In fact, a previous study [ 29 ] reported that the professor–student interaction as perceived by the learner encourages active participation of students and frequent interaction of communication, activities, and mutual interest. The method of interaction achieved in this study in relation is described below.

Both groups had the online bulletin board for mutual communication, but they were implemented differently to suit the class format. For the face-to-face flipped learning group, responses were provided during face-to-face class activities by pooling the questions together rather than answering each question. For the non-face-to-face flipped learning group, responses to questions were individually provided on the bulletin board. In the case of non-face-to-face flipped learning, there was a high degree of individual interactions, which could have been perceived as higher interaction by the learners, as reported by Kwon [ 30 ]. The reported results of that research regarding the interaction with online learners were that the quantitative increase in the interactive behavior through the Q&A bulletin board and posting contents directly related to the class were perceived as an increase in interactions. Owing to the nature of the class format, online interaction is dependent on the medium in a non-face-to-face class. The characteristics of online interaction include non-linearity, impracticality, many-to-many, recordability, multi-content-based, and information-rich communication [ 31 ].

Additionally, online interactions do not require immediate and linear interactions like face-to-face situations, and they allow sufficient time to organize thoughts for interaction [ 16 ], which could be perceived as more comfortable for some. From this perspective, it would be necessary to measure whether the learner feels that the mutual interaction is present rather than focusing on the methodology of face-to-face or non-face-to-face. Rather than assuming that there would be limitations in professor–student interaction for non-face-to-face learning, it would be necessary to maximize active professor–student interaction by optimizing the advantage of the communication using online media. Demonstrations of active professor–student interactions achieved in the non-face-to-face method suggest that such interactions can be promoted if the online system and the means for communication are open.

In this study, learning satisfaction was higher for face-to-face than non-face-to-face flipped learning. When the factors affecting the learning satisfaction were examined, learning satisfaction was high for learning format, that is, for face-to-face flipped learning, when the self-directed learning readiness and the professor–student interaction were high. When examined individually, the details are as follows:

First, class format, such as face-to-face format, had a positive effect on learning satisfaction. In flipped learning, online content learning occurred in the pre-class step, whereas the various learner-oriented activities occurred during the in-class step, and the role of the instructor became that of an advisor and facilitator providing feedback rather than that of a traditional instructor [ 8 ]. This instructor role was more efficiently achieved in the face-to-face class. Additionally, there may be factors, such as immersion, that could improve learning satisfaction for face-to-face compared with the non-face-to-face classes, as difficulties with concentration and immersion were reported in non-face-to-face learning [ 4 ]. When university students listen to online lectures for a long time, concentration decreases, and immersion in learning becomes difficult due to distractions in the surroundings, such as internet searches, games, YouTube videos, and webtoons that pop up as the computer is turned on [ 4 ].

Moreover, the difference in immersion to learning between face-to-face and non-face-to-face flipped learning could cause a difference in learning satisfaction. Previous studies reported that higher ability for immersion was associated with higher academic self-efficacy, which is the belief that one can perform well in learning [ 32 ]; immersion in learning leads to a better understanding of learning materials and, thereby, leads students to challenge themselves with higher-level tasks due to their confidence [ 33 ]. Furthermore, online lectures have the advantage of being repeated at the student’s convenience. In fact, it was shown that learners prefer video classes that they can watch repeatedly [ 34 ]. However, it could also suggest that the learners’ convenience does not increase learning satisfaction, per se. Additionally, non-face-to-face learning activities have the limitation of eliminating some kinds of nonverbal communication [ 35 ]. In text-based interactions, which comprise most online interactions, facial expressions, eye movement, and physical movements cannot be communicated; even if they could be delivered through the screen, they cannot be delivered in the same way as when in the same physical space [ 15 ]. These aspects result in the differences between the face-to-face and non-face-to-face learning environments, eventually leading to the difference in the level of satisfaction.

The results of this study also show that students with high self-directed learning readiness had higher learning satisfaction. This finding supports the result of the previous studies [ 36 ] that reported the effects of self-directed learning readiness on learning satisfaction. Unlike traditional learning, online learning occurs while the instructor and the learner are in different times and spaces; thus, it is difficult for the instructor to manage the learning for the learners. Therefore, self-directed learning, which is the ability to plan and learn without the help of others in any type of circumstances and context, can be regarded as an essential parameter for improving performance in online classes [ 4 ].

In addition, learning satisfaction was higher when there was higher professor–student interaction. This finding is consistent with the reports [ 36 ] that learning satisfaction increases with increased professor–student interactions and the study that reported that communication with the professor and Q&A with the professor affected the online class satisfaction in terms of interactions during an online class [ 37 ]. As professor–student interactions are emphasized in flipped learning [ 7 , 8 , 18 , 19 ], it is necessary to promote such interactions. By maximizing the flexibility and the efficiency of flipped learning characterized by professor–student interactions and individualized learning, students’ academic achievement can be improved, and the flexibility and efficiency of learning can be increased, as prior online learning can be adjusted to suit the individual’s situation [ 8 ]. Considering these advantages, flipped learning can be applied flexibly according to circumstances, as it is believed that such learning can be transformed and applied according to the characteristics of the subject [ 38 ].

With the recent increase in the quantity of knowledge and information and in a situation that demands the ability to resolve complex issues, the importance of self-directed learning ability, which is the ability to efficiently use the various types of information, knowledge, and techniques obtained, is being emphasized. Based on the finding that self-directed learning readiness with increasing importance is associated with learning satisfaction, the flipped learning instruction method that could improve self-directed learning readiness needs to be applied flexibly depending on the time and situation.

The results of this study could help in the design and implementation of interactions for effective online education in universities where the demand for online learning is increasing rapidly. Further, it may provide foundational knowledge for establishing a class strategy using face-to-face and online learning optionally or combining both. This study was applied with classes on theory-related subjects. However, the research can be sufficiently expanded to apply to clinical learning-related fields. Although there is a difference between theory and clinical practice, non-face-to-face practical subjects can also be conducted; thus, both factors that affect learning satisfaction and the importance of immersion and student interaction can be similarly applied to clinical field education.

5. Conclusions

This study was conducted to understand how flipped learning can be modified and to examine strategies for making it equally effective in face-to-face and non-face-to-face class activities. Various online instruction methods can be applied that utilize the advancements in technology to overcome barriers and better address crises, such as the COVID-19 pandemic. Results revealed that there was no difference in self-directed learning readiness between face-to-face flipped learning and non-face-to-face flipped learning; the professor–student interaction was higher for non-face-to-face learning, whereas learning satisfaction was higher for face-to-face learning. Furthermore, when the factors affecting learning satisfaction were examined, learning satisfaction was high for learning type, specifically face-to-face classes, when the self-directed learning readiness and the professor–student interaction were high.

These results suggest that enhancing self-directed learning ability should be raised as an essential piece of the educational agenda and that professor–student interaction can actively occur in non-face-to-face environments. Therefore, further research is needed to develop methods to (1) include immersion in non-face-to-face education, as that is the most significant advantage of face-to-face classes; (2) include nonverbal communications; and (3) increase interactions among students in non-face-to-face classes. In addition, this study may provide the foundational knowledge for establishing class strategy while flexibly implementing flipped learning to suit the situations and the environment.

In this study, we found that it is necessary for both professors and learners to be flexible in coping with face-to-face and non-face-to-face teaching methods. Considering that convenience is not necessarily connected with learning satisfaction, we recommend creating an immersive learning environment regardless of the teaching method. Additionally, the findings support a recommendation for creating a learning environment wherein non-face-to-face activities are configured according to the characteristics of learners familiar with non-face-to-face learning and in which learners can actively participate.

Author Contributions

Conceptualization, M.-K.C. and M.Y.K.; data curation, M.-K.C.; formal analysis, M.-K.C.; investigation, M.Y.K.; methodology, M.-K.C. and M.Y.K.; writing, M.-K.C. and M.Y.K. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study due to the fact that it was conducted as a survey analyzing the results of routine questionnaires administered to evaluate and improve the effects after applying a new educational method rather than an interventional study.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study. Students were informed of what was being evaluated prior to the assessments, and only those who provided voluntary consent participated. Measures were taken so that there were not any ethical issues, and best attempts were made to prevent any harm to the students.

Conflicts of Interest

The authors declare no conflict of interest.

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