quantitative research topic about online games

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• Published: 10 December 2020

Effect of internet use and electronic game-play on academic performance of Australian children

• Md Irteja Islam 1 , 2 ,
• Raaj Kishore Biswas 3 &
• Rasheda Khanam 1  

Scientific Reports volume  10 , Article number:  21727 ( 2020 ) Cite this article

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This study examined the association of internet use, and electronic game-play with academic performance respectively on weekdays and weekends in Australian children. It also assessed whether addiction tendency to internet and game-play is associated with academic performance. Overall, 1704 children of 11–17-year-olds from young minds matter (YMM), a cross-sectional nationwide survey, were analysed. The generalized linear regression models adjusted for survey weights were applied to investigate the association between internet use, and electronic-gaming with academic performance (measured by NAPLAN–National standard score). About 70% of the sample spent > 2 h/day using the internet and nearly 30% played electronic-games for > 2 h/day. Internet users during weekdays (> 4 h/day) were less likely to get higher scores in reading and numeracy, and internet use on weekends (> 2–4 h/day) was positively associated with academic performance. In contrast, 16% of electronic gamers were more likely to get better reading scores on weekdays compared to those who did not. Addiction tendency to internet and electronic-gaming is found to be adversely associated with academic achievement. Further, results indicated the need for parental monitoring and/or self-regulation to limit the timing and duration of internet use/electronic-gaming to overcome the detrimental effects of internet use and electronic game-play on academic achievement.

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Introduction

Over the past two decades, with the proliferation of high-tech devices (e.g. Smartphone, tablets and computers), both the internet and electronic games have become increasingly popular with people of all ages, but particularly with children and adolescents 1 , 2 , 3 . Recent estimates have shown that one in three under-18-year-olds across the world uses the Internet, and 75% of adolescents play electronic games daily in developed countries 4 , 5 , 6 . Studies in the United States reported that adolescents are occupied with over 11 h a day with modern electronic media such as computer/Internet and electronic games, which is more than they spend in school or with friends 7 , 8 . In Australia, it is reported that about 98% of children aged 15–17 years are among Internet users and 98% of adolescents play electronic games, which is significantly higher than the USA and Europe 9 , 10 , 11 , 12 .

In recent times, the Internet and electronic games have been regarded as important, not just for better results at school, but also for self-expression, sociability, creativity and entertainment for children and adolescents 13 , 14 . For instance, 88% of 12–17 year-olds in the USA considered the Internet as a useful mechanism for making progress in school 15 , and similarly, electronic gaming in children and adolescents may assist in developing skills such as decision-making, smart-thinking and coordination 3 , 15 .

On the other hand, evidence points to the fact that the use of the Internet and electronic games is found to have detrimental effects such as reduced sleeping time, behavioural problems (e.g. low self-esteem, anxiety, depression), attention problems and poor academic performance in adolescents 1 , 5 , 12 , 16 . In addition, excessive Internet usage and increased electronic gaming are found to be addictive and may cause serious functional impairment in the daily life of children and adolescents 1 , 12 , 13 , 16 . For example, the AU Kids Online survey 17 reported that 50% of Australian children were more likely to experience behavioural problems associated with Internet use compared to children from 25 European countries (29%) surveyed in the EU Kids Online study 18 , which is alarming 12 . These mixed results require an urgent need of understanding the effect of the Internet use and electronic gaming on the development of children and adolescents, particularly on their academic performance.

Despite many international studies and a smaller number in Australia 12 , several systematic limitations remain in the existing literature, particularly regarding the association of academic performance with the use of Internet and electronic games in children and adolescents 13 , 16 , 19 . First, the majority of the earlier studies have either relied on school grades or children’s self assessments—which contain an innate subjectivity by the assessor; and have not considered the standardized tests of academic performance 16 , 20 , 21 , 22 . Second, most previous studies have tested the hypothesis in the school-based settings instead of canvassing the whole community, and cannot therefore adjust for sociodemographic confounders 9 , 16 . Third, most studies have been typically limited to smaller sample sizes, which might have reduced the reliability of the results 9 , 16 , 23 .

By considering these issues, this study aimed to investigate the association of internet usage and electronic gaming on a standardized test of academic performance—NAPLAN (The National Assessment Program—Literacy and Numeracy) among Australian adolescents aged 11–17 years using nationally representative data from the Second Australian Child and Adolescent Survey of Mental Health and Wellbeing—Young Minds Matter (YMM). It is hypothesized that the findings of this study will provide a population-wide, contextual view of excessive Internet use and electronic games played separately on weekdays and weekends by Australian adolescents, which may be beneficial for evidence-based policies.

Subject demographics

Respondents who attended gave NAPLAN in 2008 (N = 4) and 2009 (N = 29) were removed from the sample due to smaller sample size, as later years (2010–2015) had over 100 samples yearly. The NAPLAN scores from 2008 might not align with a survey conducted in 2013. Further missing cases were deleted with the assumption that data were missing at random for unbiased estimates, which is common for large-scale surveys 24 . From the initial survey of 2967 samples, 1704 adolescents were sampled for this study.

The sample characteristics were displayed in Table 1 . For example, distribution of daily average internet use was checked, showing that over 50% of the sampled adolescents spent 2–4 h on internet (Table 1 ). Although all respondents in the survey used internet, nearly 21% of them did not play any electronic games in a day and almost one in every three (33%) adolescents played electronic games beyond the recommended time of 2 h per day. Girls had more addictive tendency to internet/game-play in compare to boys.

The mean scores for the three NAPLAN tests scores (reading, writing and numeracy) ranged from 520 to 600. A gradual decline in average NAPLAN tests scores (reading, writing and numeracy) scores were observed for internet use over 4 h during weekdays, and over 3 h during weekends (Table 2 ). Table 2 also shows that adolescents who played no electronic games at all have better scores in writing compared to those who play electronic games. Moreover, Table 2 shows no particular pattern between time spent on gaming and NAPLAN reading and numeracy scores. Among the survey samples, 308 adolescents were below the national standard average.

Internet use and academic performance

Our results show that internet (non-academic use) use during weekdays, especially more than 4 h, is negatively associated with academic performance (Table 3 ). For internet use during weekdays, all three models showed a significant negative association between time spent on internet and NAPLAN reading and numeracy scores. For example, in Model 1, adolescents who spent over 4 h on internet during weekdays are 15% and 17% less likely to get higher reading and numeracy scores respectively compared to those who spend less than 2 h. Similar results were found in Model 2 and 3 (Table 3 ), when we adjusted other confounders. The variable addiction tendency to internet was found to be negatively associated with NAPLAN results. The adolescents who had internet addiction were 17% less and 14% less likely to score higher in reading and numeracy respectively than those without such problematic behaviour.

Internet use during weekends showed a positive association with academic performance (Table 4 ). For example, Model 1 in Table 4 shows that internet use during weekends was significant for reading, writing and national standard scores. Youths who spend around 2–4 h and over 4 h on the internet during weekends were 21% and 15% more likely to get a higher reading scores respectively compared to those who spend less than 2 h (Model 1, Table 4 ). Similarly, in model 3, where the internet addiction of adolescents was adjusted, adolescents who spent 2–4 h on internet were 1.59 times more likely to score above the national standard. All three models of Table 4 confirmed that adolescents who spent 2–4 h on the internet during weekends are more likely to achieve better reading and writing scores and be at or above national standard compared to those who used the internet for less than 2 h. Numeracy scores were unlikely to be affected by internet use. The results obtained from Model 3 should be treated as robust, as this is the most comprehensive model that accounts for unobserved characteristics. The addiction tendency to internet/game-play variable showed a negative association with academic performance, but this is only significant for numeracy scores.

Electronic gaming and academic performance

Time spent on electronic gaming during weekdays had no effect on the academic performance of writing and language but had significant association with reading scores (Model 2, Table 5 ). Model 2 of Table 5 shows that adolescents who spent 1–2 h on gaming during weekdays were 13% more likely to get higher reading scores compared to those who did not play at all. It was an interesting result that while electronic gaming during weekdays tended to show a positive effect on reading scores, internet use during weekdays showed a negative effect. Addiction tendency to internet/game-play had a negative effect; the adolescents who were addicted to the internet were 14% less likely to score more highly in reading than those without any such behaviour.

All three models from Table 6 confirm that time spent on electronic gaming over 2 h during weekends had a positive effect on readings scores. For example, the results of Model 3 (Table 6 ) showed that adolescents who spent more than 2 h on electronic gaming during weekdays were 16% more likely to have better reading scores compared to adolescents who did not play games at all. Playing electronic games during weekends was not found to be statistically significant for writing and numeracy scores and national standard scores, although the odds ratios were positive. The results from all tables confirm that addiction tendency to internet/gaming is negatively associated with academic performance, although the variable is not always statistically significant.

Building on past research on the effect of the internet use and electronic gaming in adolescents, this study examined whether Internet use and playing electronic games were associated with academic performance (i.e. reading, writing and numeracy) using a standardized test of academic performance (i.e. NAPLAN) in a nationally representative dataset in Australia. The findings of this study question the conventional belief 9 , 25 that academic performance is negatively associated with internet use and electronic games, particularly when the internet is used for non-academic purpose.

In the current hi-tech world, many developed countries (e.g. the USA, Canada and Australia) have recommended that 5–17 year-olds limit electronic media (e.g. internet, electronic games) to 2 h per day for entertainment purposes, with concerns about the possible negative consequences of excessive use of electronic media 14 , 26 . However, previous research has often reported that children and adolescents spent more than the recommended time 26 . The present study also found similar results, that is, that about 70% of the sampled adolescents aged 11–17 spent more than 2 h per day on the Internet and nearly 30% spent more than 2-h on electronic gaming in a day. This could be attributed to the increased availability of computers/smart-phones and the internet among under-18s 12 . For instance, 97% of Australian households with children aged less than 15 years accessed internet at home in 2016–2017 10 ; as a result, policymakers recommended that parents restrict access to screens (e.g. Internet and electronic games) in children’s bedrooms, monitor children using screens, share screen hours with their children, and to act as role models by reducing their own screen time 14 .

This research has drawn attention to the fact that the average time spent using the internet, which is often more than 4 h during weekdays tends to be negatively associated with academic performance, especially a lower reading and numeracy score, while internet use of more than 2 h during weekends is positively associated with academic performance, particularly having a better reading and writing score and above national standard score. By dividing internet use and gaming by weekdays and weekends, this study find an answer to the mixed evidence found in previous literature 9 . The results of this study clearly show that the non-academic use of internet during weekdays, particularly, spending more than 4 h on internet is harmful for academic performance, whereas, internet use on the weekends is likely to incur a positive effect on academic performance. This result is consistent with a USA study that reported that internet use is positively associated with improved reading skills and higher scores on standardized tests 13 , 27 . It is also reported in the literature that academic performance is better among moderate users of the internet compared to non-users or high level users 13 , 27 , which was in line with the findings of this study. This may be due to the fact that the internet is predominantly a text-based format in which the internet users need to type and read to access most websites effectively 13 . The results of this study indicated that internet use is not harmful to academic performance if it is used moderately, especially, if ensuring very limited use on weekdays. The results of this study further confirmed that timing (weekdays or weekends) of internet use is a factor that needs to be considered.

Regarding electronic gaming, interestingly, the study found that the average time of gaming either in weekdays or weekends is positively associated with academic performance especially for reading scores. These results contradicted previous literatures 1 , 13 , 19 , 27 that have reported negative correlation between electronic games and educational performance in high-school children. The results of this study were consistent with studies conducted in the USA, Europe and other countries that claimed a positive correlation between gaming and academic performance, especially in numeracy and reading skills 28 , 29 . This is may be due to the fact that the instructions for playing most of the electronic games are text-heavy and many electronic games require gamers to solve puzzles 9 , 30 . The literature also found that playing electronic games develops cognitive skills (e.g. mental rotation abilities, dexterity), which can be attributable to better academic achievement 31 , 32 .

Consistent with previous research findings 33 , 34 , 35 , 36 , the study also found that adolescents who had addiction tendency to internet usage and/or electronic gaming were less likely to achieve higher scores in reading and numeracy compared to those who had not problematic behaviour. Addiction tendency to Internet/gaming among adolescents was found to be negatively associated with overall academic performance compared to those who were not having addiction tendency, although the variables were not always statistically significant. This is mainly because adolescents’ skipped school and missed classes and tuitions, and provide less effort to do homework due to addictive internet usage and electronic gaming 19 , 35 . The results of this study indicated that parental monitoring and/ or self-regulation (by the users) regarding the timing and intensity of internet use/gaming are essential to outweigh any negative effect of internet use and gaming on academic performance.

Although the present study uses a large nationally representative sample and advances prior research on the academic performance among adolescents who reported using the internet and playing electronic games, the findings of this study also have some limitations that need to be addressed. Firstly, adolescents who reported on the internet use and electronic games relied on self-reported child data without any screening tests or any external validation and thus, results may be overestimated or underestimated. Second, the study primarily addresses the internet use and electronic games as distinct behaviours, as the YMM survey gathered information only on the amount of time spent on internet use and electronic gaming, and included only a few questions related to addiction due to resources and time constraints and did not provide enough information to medically diagnose internet/gaming addiction. Finally, the cross-sectional research design of the data outlawed evaluation of causality and temporality of the observed association of internet use and electronic gaming with the academic performance in adolescents.

This study found that the average time spent on the internet on weekends and electronic gaming (both in weekdays and weekends) is positively associated with academic performance (measured by NAPLAN) of Australian adolescents. However, it confirmed a negative association between addiction tendency (internet use or electronic gaming) and academic performance; nonetheless, most of the adolescents used the internet and played electronic games more than the recommended 2-h limit per day. The study also revealed that further research is required on the development and implementation of interventions aimed at improving parental monitoring and fostering users’ self-regulation to restrict the daily usage of the internet and/or electronic games.

Data description

Young minds matter (YMM) was an Australian nationwide cross-sectional survey, on children aged 4–17 years conducted in 2013–2014 37 . Out of the initial 76,606 households approached, a total of 6,310 parents/caregivers (eligible household response rate 55%) of 4–17 year-old children completed a structured questionnaire via face to face interview and 2967 children aged 11–17 years (eligible children response rate 89%) completed a computer-based self-reported questionnaire privately at home 37 .

Area based sampling was used for the survey. A total of 225 Statistical Area 1 (defined by Australian Bureau of Statistics) areas were selected based on the 2011 Census of Population and Housing. They were stratified by state/territory and by metropolitan versus non-metropolitan (rural/regional) to ensure proportional representation of geographic areas across Australia 38 . However, a small number of samples were excluded, based on most remote areas, homeless children, institutional care and children living in households where interviews could not be conducted in English. The details of the survey and methodology used in the survey can be found in Lawrence et al. 37 .

Following informed consent (both written and verbal) from the primary carers (parents/caregivers), information on the National Assessment Program—Literacy and Numeracy (NAPLAN) of the children and adolescents were also added to the YMM dataset. The YMM survey is ethically approved by the Human Research Ethics Committee of the University of Western Australia and by the Australian Government Department of Health. In addition, the authors of this study obtained a written approval from Australian Data Archive (ADA) Dataverse to access the YMM dataset. All the researches were done in accordance with relevant ADA Dataverse guidelines and policy/regulations in using YMM datasets.

Outcome variables

The NAPLAN, conducted annually since 2008, is a nationwide standardized test of academic performance for all Australian students in Years 3, 5, 7 and 9 to assess their skills in reading, writing numeracy, grammar and spelling 39 , 40 . NAPLAN scores from 2010 to 2015, reported by YMM, were used as outcome variables in the models; while NAPLAN data of 2008 (N = 4) and 2009 (N = 29) were excluded for this study in order to reduce the time lag between YMM survey and the NAPLAN test. The NAPLAN gives point-in-time standardized scores, which provide the scope to compare children’s academic performance over time 40 , 41 . The NAPLAN tests are one component of the evaluation and grading phase of each school, and do not substitute for the comprehensive, consistent evaluations provided by teachers on the performance of each student 39 , 41 . All four domains—reading, writing, numeracy and language conventions (grammar and spelling) are in continuous scales in the dataset. The scores are given based on a series of tests; details can be found in 42 . The current study uses only reading, writing and numeracy scores to measure academic performance.

In this study, the National standard score is a combination of three variables: whether the student meets the national standard in reading, writing and numeracy. Based on national average score, a binary outcome variable is also generated. One category is ‘below standard’ if a child scores at least one standard deviation (one below scores) from the national standard in reading, writing and numeracy, and the rest is ‘at/above standard’.

Independent variables

Internet use and electronic gaming.

In the YMM survey, owing to the scope of the survey itself, an extensive set of questions about internet usage and electronic gaming could not be included. Internet usage omitted the time spent in academic purposes and/or related activities. Playing electronic games included playing games on a gaming console (e.g. PlayStation, Xbox, or similar console ) online or using a computer, or mobile phone, or a handled device 12 . The primary independent covariates were average internet use per day and average electronic game-play in hours per day. A combination of hours on weekdays and weekends was separately used in the models. These variables were based on a self-assessed questionnaire where the youths were asked questions regarding daily time spent on the Internet and electronic game-play, specifically on either weekends or weekdays. Since, internet use/game-play for a maximum of 2 h/day is recommended for children and adolescents aged between 5 and 17 years in many developed countries including Australia 14 , 26 ; therefore, to be consistent with the recommended time we preferred to categorize both the time variables of internet use and gaming into three groups with an interval of 2 h each. Internet use was categorized into three groups: (a) ≤ 2 h), (b) 2–4 h, and (c) > 4 h. Similar questions were asked for game-play h. The sample distribution for electronic game-play was skewed; therefore, this variable was categorized into three groups: (a) no game-play (0 h), (b) 1–2 h, and (c) > 2 h.

Other covariates

Family structure and several sociodemographic variables were used in the models to adjust for the differences in individual characteristics, parental inputs and tastes, household characteristics and place of residence. Individual characteristics included age (continuous) and sex of the child (boys, girls) and addiction tendency to internet use and/or game-play of the adolescent. Addiction tendency to internet/game-play was a binary independent variable. It was a combination of five behavioural questions relating to: whether the respondent avoided eating/sleeping due to internet use or game-play; feels bothered when s/he cannot access internet or play electronic games; keeps using internet or playing electronic games even when s/he is not really interested; spends less time with family/friends or on school works due to internet use or game-play; and unsuccessfully tries to spend less time on the internet or playing electronic games. There were four options for each question: never/almost never; not very often; fairly often; and very often. A binary covariate was simulated, where if any four out of five behaviours were reported as for example, fairly often or very often, then it was considered that the respondent had addictive tendency.

Household characteristics included household income (low, medium, high), family type (original, step, blended, sole parent/primary carer, other) 43 and remoteness (major cities, inner regional, outer regional, remote/very remote). Parental inputs and taste included education of primary carer (bachelor, diploma, year 10/11), primary carer’s likelihood of serious mental illness (K6 score -likely; not likely); primary carer’s smoking status (no, yes); and risk of alcoholic related harm by the primary carer (risky, none).

Statistical analysis

Descriptive statistics of the sample and distributions of the outcome variables were initially assessed. Based on these distributions, the categorization of outcome variables was conducted, as mentioned above. For formal analysis, generalized linear regression models (GLMs) 44 were used, adjusting for the survey weights, which allowed for generalization of the findings. As NAPLAN scores of three areas—reading, writing and numeracy—were continuous variables, linear models were fitted to daily average internet time and electronic game play time. The scores were standardized (mean = 0, SD = 1) for model fitness. The binary logistic model was fitted for the dichotomized national standard outcome variable. Separate models were estimated for internet and electronic gaming on weekends and weekdays.

We estimated three different models, where models varied based on covariates used to adjust the GLMs. Model 1 was adjusted for common sociodemographic factors including age and sex of the child, household income, education of primary carer’s and family type 43 . However, the results of this model did not account for some unobserved household characteristics (e.g. taste, preferences) that are unobserved to the researcher and are arguably correlated with potential outcomes. The effects of unobserved characteristics were reduced by using a comprehensive set of observable characteristics 45 , 46 that were available in YMM data. The issue of unobserved characteristics was addressed by estimating two additional models that include variables by including household characteristics such as parental taste, preference and inputs, and child characteristics in the model. In addition to the variables in Model 1, Model 2 included remoteness, primary carer’s mental health status, smoking status and risk of alcoholic related harm by the primary carer. Model 3 further included internet/game addiction of the adolescent in addition to all the covariates in Model 2. Model 3 was expected to account for a child’s level of unobserved characteristics as the children who were addicted to internet/games were different from others. The model will further show how academic performance is affected by internet/game addiction. The correlation among the variables ‘internet/game addiction’ and ‘internet use’ and ‘gaming’ (during weekdays and weekends) were also assessed, and they were less than 0.5. Multicollinearity was assessed using the variance inflation factor (VIF), which was under 5 for all models, suggesting no multicollinearity 47 .

p value below the threshold of 0.05 was considered the threshold of significance. All analysis was conducted in R (version 3.6.1). R-package survey (version 3.37) was used for modelling which is suited for complex survey samples 48 .

Data availability

The authors declare that they do not have permission to share dataset. However, the datasets of Young Minds Matter (YMM) survey data is available at the Australian Data Archive (ADA) Dataverse on request ( https://doi.org/10.4225/87/LCVEU3 ).

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Acknowledgements

The authors would like to thank the University of Western Australia, Roy Morgan Research, the Australian Government Department of Health for conducting the survey, and the Australian Data Archive for giving access to the YMM survey dataset. The authors also would like to thank Dr Barbara Harmes for proofreading the manuscript.

This research did not receive any specific Grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Video Game Genres and Advancing Quantitative Video Game Research with the Genre Diversity Score

• Published: 24 October 2020
• Volume 9 , pages 401–420, ( 2020 )

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• Rebecca Sevin   ORCID: orcid.org/0000-0003-4360-4464 1 &
• Whitney DeCamp   ORCID: orcid.org/0000-0002-4044-2220 1  

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Quantitative research on video games often reduces participants’ gaming experience to how much time they spend playing video games. Although appropriate in some instances, it often fails to capture aspects of the video game experience. Studies that only use time as a means of establishing expertise in gaming fail to capture how much a player is involved in different types of video storytelling, game rules and mechanics, social experiences online and/or offline, and many other aspects. Only using time as a measurement also introduces a bias against women, as they typically have less leisure time overall, reducing the time they might spend playing video games. The current study proposes and tests a novel measure for gauging participants’ experience with video games that includes their experience with various game genres in addition to time dedicated to playing games. The “genre diversity score” presented in this paper provides a better understanding of an individual’s experience with gaming as a whole while still providing a metric that can be used in quantitative research. To demonstrate the usefulness of this measure it is compared to measures of time spent playing, the use of a PC for gaming, and casual/non-casual gaming. The analyses indicate that the genre diversity score outperforms other gaming measures in various tests of predictive power making a case for it to be used in future quantitative or mixed methods studies on gaming.

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Data Availability

The data are not publicly available.

Code Availability

The coding used for this study is available upon request.

The difference in free-time is an important consideration here because of the cross-sectional nature of this study. The measures used refer to current video game use, but cumulative lifetime use would also be relevant in how video game exposure might affect various outcomes, and that might not be reflected in current use. Additionally, there may be other purposes for which an enthusiasm-focused measure is more appropriate (i.e., an investigation of the effects of interest or attitudes rather than actual exposure).

A separate set of analyses (not shown, but available upon request) used the same models as in Tables  2 – 4 , but with the ideal scores and ideal hours substituted in for the original measures. In them, ideal genre diversity is consistently the strongest predictor across the models, but the amount of explained variance is slightly lower than in the original models. This suggests that the ideal measure is no better than the actual measure and additionally, ideal hours per week is a significant predictor even after controlling for other measures in these models, but remains relatively weak in comparison to genre diversity and using a PC, suggesting again that it is not a suitable measure by itself.

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Questions used for genre diversity score construct.

(Q1) In the past 6 months, how often have you played the following types of games?

(1) Casual games such as Solitaire, Angry Birds, mobile games, Facebook games, etc.

(2) Arcade / Retro games such as Pac Man, early Super Mario or Sonic games, Galaga, etc.

(3) Group Entertainment such as Kinect Sports, Rock Band, Wii sports, etc.

(4) Role playing style games (RPGs) such as Final Fantasy, Skyrim, Fallout, Mass Effect, etc.

(5) Simulation style games such as Flight Simulator, The Sims, Sim City, etc.

(6) Action games, games which focus on game play, such as God of War, Grand Theft Auto, etc.

(7) Adventure games, games with a focus on story, such as Myst, Assassin’s Creed, Uncharted, Silent Hill etc.

(8) Strategy games such as Civilizations, Warhammer, StarCraft etc.

(9) Racing games such as Forza, Mario Kart, Burnout, Need for Speed, etc.

(10) Independent games such as Bastion, Super Meat Boy, Limbo, Braid, etc.

(11) Shooter style games such as Halo, Modern Warfare, Call of Duty, Left 4 Dead, etc.

(12) Massively Multiplayer Online (MMO) style games such as World of Warcraft, Guild Wars, etc.

(13) Realistic Sports games, such as Madden, FIFA, etc.

(14) Fighting games such as Tekken, Soul Caliber, Super Smash Brothers, etc.

(15) Other genre not listed: __________________.

(16) Other genre not listed: __________________.

Responses for each genre: (a) Never / have not heard of this genre, (b) Rarely ever, (c) A few times a month, (d) Several times a month, (e) A few times a week, (f) Roughly every other day, (g) Daily or almost every day.

(Q2) On a scale of 1 to 5, where 1 is “no interest” and 5 is “very strong interest,” how interested are you playing the following types of games if time and money were not an issue?

[Same list of 16 genres as above].

Responses for each genre: (a) No interest (1), (b) Very little interest (2), (c) Some interest (3), (d) Strong interest (4), (e) Very strong interest (5).

About this article

Sevin, R., DeCamp, W. Video Game Genres and Advancing Quantitative Video Game Research with the Genre Diversity Score. Comput Game J 9 , 401–420 (2020). https://doi.org/10.1007/s40869-020-00115-3

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Accepted : 14 October 2020

Published : 24 October 2020

Issue Date : December 2020

DOI : https://doi.org/10.1007/s40869-020-00115-3

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Quantitative Research for new games user researchers

Essential quantitative research skills for games user researchers, including how to compare two data-sets, and common stats errors.

Last updated: July 19, 2021

Let’s start with my confession. I have been a user researcher working with games for over ten years. I have run hundreds of studies, and overseen thousands of hours of playtests (over 25,000 player hours at last count!). And yet, I know very little about stats.

There are two quantitative research things I know how to do. Today, I will explain both.

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Comparing two sets of numbers

The first thing is how to compare two sets of numbers. I was taught this by Cyril and Mirweis at PlayStation, and I am grateful to them both for teaching me the only stats I know – how to compare two sets of numbers. This is useful when comparing things such as ‘how many times did the player fail’ or ‘how long did it take people to complete this level’

This method is appropriate for when the data is numerical rather than categorical (or ordinal) . Here’s a short explanation of what that means.  

When you have some numerical data, it’s quite common to want to compare it. This allows you to learn “is there a difference between these two things”, and then inspire conversations such as “do we want players to fail more times on this level than on the next one?”.

To do this, you want to find the average, and then work out some confidence intervals to anticipate whether the difference between them is real or whether it was potentially caused by not measuring enough people. 

So, after counting how many times people died on level 3, you can take an average – which looks like this. 

We can see that on average, players died around 2.5 times on level 3.  We can then do the same thing for the next level.

(This is probably a good moment to mention there is a template that does the maths for you later in this post…)

Looking at the average for Level 4 shows us that people died on average more often on Level 4 than they did on Level 3. 

But we don’t know if this is because Level 4 causes more deaths, or just random chance that it occurred in this study.

To identify that bit, we calculate confidence intervals. Which looks like this…

And we can see that the confidence intervals (the uppey-downy bits) overlap. The top of Level 3 overlaps with the bottom of Level 4.

Level 5’s confidence intervals do not overlap with any of the other levels. If the confidence intervals don’t overlap, there is a real difference between them. It’s true that more people died, and will die, on level 5 than level 4.

This hopefully means that Level 5 harder – although you should watch people play to understand actually why the difference in deaths occurred.

If the confidence intervals don’t overlap, we can’t tell if there is a difference. This is the case for Level 3 + 4. This either means that the number of times people die are the same, or that we haven’t seen enough players to draw an appropriate conclusion.

(There are probably errors in the terminology above, but as I said, I know little about stats – I just know how to compare two sets of numbers).

I use this all of the time – to count and compare deaths, completion time, etc. I made a template that you can duplicate to see the formulas required, and to have a go at doing it yourself.

Go deeper on quantitative research

Beyond this one technique, I’ve found two other tools very helpful.

Adjusted wald calculators like this allow you to state your completion rate (e.g. 3 out of 10 people encountered this issue), and from that anticipate how many people in the real world would encounter the same issue (between 10% and 60% apparently).

And the book ‘Quantifying the User Experience’ which has lots of nice decision maps like these, which tells me what tools I should (and shouldn’t) be using … and includes a crash course in stats to explain how to do them! 

Avoid common quantitative research errors

The second thing I’ve learned is a collection of things not to do. By recognising some stats errors, it helps me know when I should seek out someone better with stats than me to help out. 

Avoiding common errors include:

• Don’t do the kind of maths I described above on ordinal data (such as likert scales). People often do, and get away with it, but it’s somewhat inaccurate as you’re treating categories like they are numbers.
• Think about the sampling bias you have created in your study, and don’t over-emphasise how representative your conclusions are
• Don’t assume that because you are measuring what players say they think or do, you are actually measuring what they think or do.
• Recognise that when you are limiting the options you allow people to select from, you are limiting the range of results you will get back, potentially distorting the truth.
• Avoid dogmatic rules about sample sizes. There’s lots of rules out there that have become dogmatic (‘quant studies need 30 users’, ‘qual studies need 5 users’), and many people repeat them without understanding the reason behind them. Understand why those guidelines exist, think about what you are trying to learn, and make conscious decisions rather than following ‘rules’.

The job is not just qualitative research

I sometimes encounter the idea that user researchers are synonymous with qualitative research. I don’t think that is appropriate or correct. Even if you are more comfortable with qualitative research, you shouldn’t allow your skillset to determine the method you apply for answering research questions. 

Instead always lead with ‘what does the team want to know’, and then ‘what is the most appropriate way of discovering that’. If that method isn’t one you are comfortable with, use it as an opportunity to learn how to do a new thing, ask for help from the community, or bring in some help from someone who is comfortable with it. Our job is to “help the team make evidence-based decisions”, regardless of the methods we are most comfortable with.

What quantitative research skills should I be ready for in the job interview?

If you can answer the following questions, I would say you would be a stand-out candidate…

• What is p-value?
• How would you compare the difficulty between two levels? What would you measure, and how should that be interpreted?
• How would you measure if players are enjoying a game?
• How would you handle being asked ‘I think this study should have a larger sample size’?

You will notice that these questions are often not about ‘how do I do the stats’, but much more interested in ‘when is quantitative research appropriate, how should it be applied, how should I explain things to my colleagues, and what are the caveats for this kind of work’. Which I think is where the real challenge lies! 

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How to Invest in OpenAI's ChatGPT (Updated 2024)

April 08, 2024 — 04:30 pm EDT

Written by Melissa Pistilli for Investing News Network  ->

OpenAI’s ChatGPT is one of the latest technological breakthroughs in the artificial intelligence (AI) space. But is there a good investment case for a technology that has become so controversial?

This emerging technology is representative of a niche subsector of the AI industry known as generative AI — systems that can generate text, images or sounds in response to prompts given by users.

Precedence Research expects the global AI market to grow at a compound annual growth rate (CAGR) of 19 percent to reach US$2.57 trillion by 2032. Just how much of an impact OpenAI’s ChatGPT will have on this space is hard to predict, but S&P Global suggested in December 2023 that the total market revenue of generative AI as a whole will see a CAGR of 57.9 percent through 2028, increasing from US$3.7 billion last year to US$36.36 billion in 2028.

“The key trend last year was the rise of generative AI, and 2023 will go down as one of the most exciting years for AI yet! With the launch of ChatGPT late in 2022, the true scale of its disruptive potential was more realized across the world in 2023,” said Naseem Husain , senior vice president and exchange-traded fund (ETF) strategist at Horizons ETFs. “Its success has sparked a wave of generative and chat AI models, from Midjourney to Grok.”

Of course, OpenAi has also generated a lot of controversy, such as fears over job destruction and targeted disinformation campaigns . And let’s not forget the odd and abrupt, however brief, ousting of OpenAI CEO Sam Altman .

Many lawsuits have emerged as well. Multiple news outlets , including the the New York Times , have launched copyright lawsuits against OpenAI, and some of the plaintiffs are also seeking damages from the private tech firm’s very public partner Microsoft (NASDAQ: MSFT ). Additionally, the Authors Guild, which represents a group of prominent authors , launched a class-action lawsuit against OpenAI that is calling for a licensing system that would allow authors to opt out of having their books used to train AI, and would require AI companies to pay for the material they do use.

With all of that said, there's still a lot of excitement surrounding generative AI technology. Many investors are wondering if it's possible to invest in OpenAI's ChatGPT, and if there are other ways to invest in generative AI. Here the Investing News Network (INN) answers those questions and more, shedding light on this new landscape.

​What is OpenAI's ChatGPT?

Created by San Francisco-based tech lab OpenAI, ChatGPT is a generative AI software application that uses a machine learning technique called reinforcement learning from human feedback (RLHF) to emulate human-written conversations based on a large range of user prompts. This kind of software is better known as an AI chatbot.

ChatGPT learns language by training on texts gleaned from across the internet, including online encyclopedias, books, academic journals, news sites and blogs. Based on this training, the AI chatbot generates text by making predictions about which words (or tokens) can be strung together to produce the most suitable response.

More than a million people engaged with ChatGPT within the first week of its launch for free public testing on November 30, 2022. Many were in awe of the chatbot’s seemingly natural language capabilities, not only in terms of understanding questions, but also because of its human-like responses. Users felt as if they were having a conversation with a human.

Besides being an excellent conversation partner, ChatGPT can write engaging short stories , develop catchy marketing materials, solve complicated math problems and even create code in various programming languages.

Based on this success, OpenAI has created a more powerful version of the ChatGPT system called GPT-4 , which was released in March 2023. It is currently only available to paid ChatGPT subscribers and Microsoft Copilot users .

This iteration of ChatGPT can accept visual inputs , is much more precise and can display a higher level of expertise in various subjects. Because of this, GPT-4 can describe images in vivid detail and ace standardized tests.

Unlike its predecessor, GPT-4 doesn't have any time limits on what information it can access; however, AI researcher and professor Dr. Oren Etzioni has said that the chatbot is still terrible at discussing the future and generating new ideas. It also hasn't lost its tendency to deliver incorrect information with too high a degree of confidence.

​What is Elon Musk's relationship to OpenAI?

DIA TV / Shutterstock

OpenAI was founded in 2015 by Altman, its current CEO, as well as Tesla's (NASDAQ: TSLA ) Elon Musk and other big-name investors, such as venture capitalist Peter Thiel and LinkedIn co-founder Reid Hoffman. Musk left his position on OpenAI's board of directors in 2018 to focus on Tesla and its pursuit of autonomous vehicle technology.

A few days after ChatGPT became available for public testing, Musk took to X , formerly known as Twitter, to say, “ChatGPT is scary good. We are not far from dangerously strong AI.” That same day, he announced that X had shut the door on OpenAI’s access to its database so it could no longer use it for RLHF training.

His reason: “OpenAI was started as open-source & non-profit. Neither are still true.”

Furthering his feud with OpenAI, Musk filed a lawsuit against the company in March 2024 for an alleged breach of contract. The crux of his complaint was that OpenAI has broken the "founding agreement" made between the founders (Altman, Greg Brockman and himself) that the company would remain a non-profit. Altman and OpenAI have denied there was such an agreement and that Musk was keen on an eventual for-profit structure.

​Is ChatGPT revolutionary or hype?

Is ChatGPT a revolutionary technology or just another hyped-up tech fad that will flop, much in the way of Google Glass or the Segway ? It may be too early to tell, but as with any new technology, there are plenty of wrinkles to iron out.

One of the most challenging bugs to fix before ChatGPT can be deployed more widely is the chatbot’s propensity to respond with “plausible-sounding but incorrect or nonsensical answers," admits OpenAI .

Remember, its selection of which words to string together are actually predictions — not as fallible as mere guesses, but still fallible. Even the latest 4.0 version is “ still is not fully reliable (it “hallucinates” facts and makes reasoning errors),” says the company, which emphasizes that users should exercise caution when employing the technology.

Indeed, ChatGPT's failings can have dangerous real-life consequences. Among other negative applications, the tech can be used to spread misinformation, carry out phishing email scams or write malicious code. What’s more, the AI-based technology is prone to racial and gender-based biases. Not only has this language learning model contributed to the human-like quality of its responses, but it has also picked up on some of humanity’s shortcomings.

“ChatGPT was trained on the collective writing of humans across the world, past and present. This means that the same biases that exist in the data, can also appear in the model,” explains Garling Wu , staff writer for online technology publication MUO, in a September 2023 article. “In fact, users have shown how ChatGPT can give produce some terrible answers, some, for example, that discriminate against women. But that's just the tip of the iceberg; it can produce answers that are extremely harmful to a range of minority groups.”

On the flip side, an August 2023 study by the University of East Anglia identified a left-wing bias in ChatGPT. Researchers at the school said their work shows that ChatGPT "favors Democrats in the U.S., the Labour Party in the U.K., and president Lula da Silva of the Workers’ Party in Brazil," according to Forbes.

There’s also the fear among teachers that the technology is leading to an unwelcome rise in academic dishonesty, with students using ChatGPT to write essays or complete their science homework.

“Teachers and school administrators have been scrambling to catch students using the tool to cheat, and they are fretting about the havoc ChatGPT could wreak on their lesson plans,” writes New York Times tech columnist Kevin Roose .

Despite these concerns, we’re likely to see new iterations of ChatGPT — hopefully without the aforementioned bugs — as OpenAI has the backing of tech giant Microsoft.

Why is Microsoft investing in OpenAI? ​

Ascannio / Shutterstock

Since 2019, Microsoft has invested at least US$3 billion in OpenAI to help the small tech firm create its ultra-powerful AI chatbot, as reported by New York Times technology correspondents Cade Metz and Karen Weise.

Microsoft announced in mid-January 2023 that as part of the third phase of its partnership with OpenAI, it will make "a multiyear, multibillion dollar investment." Although the company hasn't disclosed the total amount of its latest spend, reports at the time indicated that US$10 billion is on the table . According to a February article from Reuters , OpenAI was recently valued at US$80 billion, meaning Microsoft's US$10 billion move would be huge. However, as of late 2023 there were rumors that OpenAI has only received a fraction of that purported investment.

How could Microsoft benefit from its investment? It seems the tech giant is hopeful advancements in generative AI may increase revenues for its Azure cloud computing business, as OpenAI officially licensed its technologies to Microsoft in 2020. Indeed, Pitchbook has described the deal as an “ unprecedented milestone ” for generative AI technology.

The strength of Microsoft’s confidence in OpenAI’s Altman was definitely on display in late November, when it quickly moved him to the payroll of its advanced AI research team after he was fired from OpenAI . Barely a week passed before Altman was back at the helm of OpenAI with major board changes, including the addition of Dee Templeton , Microsoft's vice president of technology and research partnerships and operations, as a non-voting observer.

​What's the future of OpenAI and ChatGPT?

The ChatGPT 3.5 platform is free to use, and can be accessed via the web. Those with an iPhone or iPad can also use ChatGPT through an app , and an Android version launched in July 2023 . OpenAI also launched a paid subscription, ChatGPT Plus for business use , in August 2023. ChatGPT Plus gives users access to the newest iteration, GPT-4.

In addition to Microsoft's use of the ChatGPT technology as part of Copilot, other companies are working with OpenAI to incorporate the technology into their platforms, including Canva, Duolingo (NASDAQ: DUOL ), Intercom, Salesforce (NYSE: CRM ), Scale, Stripe, and Upwork (NASDAQ: UPWK ).

As uptake increases, generative AI technology is replacing humans in the workplace, and will likely continue doing so in a number of fields, from content creation and customer service to transcription and translation services, and even in graphic design and paralegal fields. However, humans are hitting back, as evidenced by recent lawsuits launched against OpenAI and Microsoft. As mentioned, a growing group of prominent authors is suing the creator of ChatGPT and its financial backer for infringing on their copyright by using their books without permission to train the language models behind ChatGPT and other AI-based software.

The New York Times has also taken a stand by taking OpenAI and Microsoft to Manhattan Federal Court.

"Defendants seek to free-ride on the Times's massive investment in its journalism by using it to build substitutive products without permission or payment," states the complaint . "There is nothing 'transformative' about using the Times's content without payment to create products that substitute for the Times and steal audiences away from it."

What about the long-term goals for OpenAI and ChatGPT? Metz of the New York Times believes the end game is “artificial general intelligence, or AGI — a machine that can do anything the human brain can do.”

In keeping with this end goal, OpenAI made a major move by acquiring an AI creative firm with a deep talent bench, Global Illumination , in mid-August 2023. "Global Illumination is a company that has been leveraging AI to build creative tools, infrastructure, and digital experiences," states OpenAI on its website .

"The team previously designed and built products early on at Instagram and Facebook and have also made significant contributions at YouTube, Google, Pixar, Riot Games, and other notable companies."

In November 2023, OpenAI decided to give customers without coding skills the ability to create customized versions of its chatbot and access to large data sets for training. “OpenAI wants people to start innovating using the chatbots and creating special chatbots,” Hod Lipson, an engineering and data science professor at Columbia University, told CNBC .

Chatbot creators will eventually have the ability to share their custom chatbots through OpenAI’s GPT Store. “They’re really trying to create a marketplace, which will allow companies and people to innovate and play around with this incredible form of AI that they’ve just unleashed,” Lipson added.

What is Google's Bard AI?

Carl DMaster / Shutterstock

While ChatGPT has been generating major buzz, it's definitely not the only chatbot out there.

Notably, Alphabet (NASDAQ: GOOGL ) subsidiary Google launched its answer to ChatGPT in March 2023. Known as Bard AI , the chatbot is built on Google’s Language Model for Dialogue Applications (or LaMDA). Google CEO Sundar Pichai has described Bard as an “experimental conversational AI service … (that) seeks to combine the breadth of the world’s knowledge with the power, intelligence and creativity of our large language models.”

As with ChatGPT, users can key in a query, request or prompt and Bard will provide a human-like response. One way in which Bard may have had a leg up on the original ChatGPT is that the latter could only use data up to 2021, while the former can access up-to-date information online; this is less relevant now that GPT-4 no longer has this limitation.

However, Bard's ability to access current data hasn’t spared it from ChatGPT’s biggest folly: confidently stating misinformation as fact. The Verge reported that when asked about new discoveries from the James Webb Space Telescope, Google’s Bard “made a factual error in its very first demo.”

In early in 2024, Google launched the latest iteration of its Bard Advanced AI chatbot with a new name, Gemini AI. The new version is powered by Google's Gemini Ultra large language model.

​Which stocks will benefit the most from AI chatbot technology?

Other than companies directly tied to generative AI technology, which stocks are likely to get a boost from advances?

There are several verticals in the tech industry with indirect exposure to AI chatbot technology, such as semiconductors, network equipment providers, cloud providers, central processing unit manufacturers and internet of things.

Some of the publicly traded companies in these verticals include:

• Graphics processing unit leader Nvidia (NASDAQ: NVDA )
• The world's largest semiconductor chip manufacturer by revenue, Taiwan Semiconductor Manufacturing Company (NYSE: TSM )
• Computer memory and data storage producer Micron Technology (NASDAQ: MU )
• Digital communications firm Cisco Systems (NASDAQ: CSCO )
• Networking products provider Juniper Networks (NYSE: JNPR )
• Semiconductor producer Marvell Technology Group (NASDAQ: MRVL )
• Cloud-computing Amazon Web Services' parent company Amazon (NASDAQ: AMZN )
• Bluechip multinational technology company IBM (NYSE: IBM )
• Major semiconducter chip manufacturer Intel (NASDAQ: INTC )

While most companies specializing in generative AI remain in the venture capital stage, there are plenty of AI stocks for those interested in the space. INN's article 5 Canadian Artificial Intelligence Stocks , ASX AI Stocks: 5 Biggest Companies , and 12 Generative AI Stocks to Watch as ChatGPT Soars includes some examples.

Investors who don’t like to put all their eggs in one basket can check out these 5 Artificial Intelligence ETFs . And if you’re looking for a more general overview of the market, INN has you covered with How to Invest in Artificial Intelligence . You can also take a look back at the market in 2023 with our AI Market 2023 Year-End Review , or read projections for AI this year in our AI Market Forecast: 3 Top Trends that will Affect AI in 2024 .

​FAQs for investing in OpenAI and ChatGPT

When will openai go public.

So, can you invest in OpenAI itself? The company is not currently a publicly traded stock; however, if Microsoft does take a large position in the company, investors will be able to gain indirect exposure to OpenAI by purchasing Microsoft shares.

For those seeking direct exposure, be on the lookout for news of an initial public offering (IPO). As of late-March 2024, there are no plans for an OpenAI IPO on the horizon.

How is OpenAI funded?​

OpenAI raised US$11.3 billion over six funding rounds from 2016 to January 2024.

The three top investors are technology investment firm Thrive Capital, venture capital firm Andreessen Horowitz and revolutionary technology investment firm Founders Fund.

​What is the market value of ChatGPT/OpenAI?

OpenAI has a market valuation of US$80 billion as of February 2024. The company’s 2023 revenue had reached US$2 billion mark in December 2023 to join the ranks of Google and Meta (NASDAQ: META ).

Does ChatGPT use Nvidia chips?

ChatGPT’s distributed computing infrastructure depends upon powerful servers with multiple graphics processing units (GPUs). High-performance Nvidia GPU chips are preferred for this application as they also provide excellent Compute Unified Device Architecture support.

​Will ChatGPT cause another GPU shortage?

Most likely not. The type of GPUs used for machine learning models like ChatGPT are different from other types of GPUs, including those used to power gaming systems or crypto mining.

​Can ChatGPT make stock predictions?

A University of Florida study recently highlighted the potential for advanced language models such as ChatGPT to accurately predict movements in the stock market using sentiment analysis. During the course of the study, ChatGPT outperformed traditional sentiment analysis methods, and the finance professors conducting the research concluded that “incorporating advanced language models into the investment decision-making process can yield more accurate predictions and enhance the performance of quantitative trading strategies.”

When to expect ChatGPT 5?

OpenAI filed a trademark application for ChatGPT-5 in mid-July 2023, which hinted that the next iteration of the generative AI technology is currently under development. There were rumours the company planned to complete training for ChatGPT-5 by the end of 2023, which did not materialize. Its anyone's guess when we may see its launch, but most likely not before Q3 2024 .

While PC Guide notes that OpenAI did release GPT-4V and GPT-4 Turbo in Q4 2023, there is little sign that ChatGPT-5 is close to market. However, the publication did share that, "In a March 2024 interview on the Lex Fridman podcast, Sam Altman teased an “ amazing new model this year ” but wouldn’t commit to it being called GPT 5 (or anything else)".

This is an updated version of an article first published by the Investing News Network in 2023.

Don't forget to follow us @INN_Technology for real-time news updates!

Securities Disclosure: I, Melissa Pistilli, hold no direct investment interest in any company mentioned in this article.

Editorial Disclosure: The Investing News Network does not guarantee the accuracy or thoroughness of the information reported in the interviews it conducts. The opinions expressed in these interviews do not reflect the opinions of the Investing News Network and do not constitute investment advice. All readers are encouraged to perform their own due diligence.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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