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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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Islam, M.I., Biswas, R.K. & Khanam, R. Effect of internet use and electronic game-play on academic performance of Australian children. Sci Rep 10 , 21727 (2020). https://doi.org/10.1038/s41598-020-78916-9

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A large scale test of the gaming-enhancement hypothesis

Andrew k. przybylski.

1 Oxford Internet Institute, University of Oxford, Oxford, United Kingdom

John C. Wang

2 Nanyang Technological University, Singapore

Associated Data

The following information was supplied regarding data availability:

All study materials and data are available for download using the Open Science Framework: https://osf.io/je786/ .

A growing research literature suggests that regular electronic game play and game-based training programs may confer practically significant benefits to cognitive functioning. Most evidence supporting this idea, the gaming-enhancement hypothesis , has been collected in small-scale studies of university students and older adults. This research investigated the hypothesis in a general way with a large sample of 1,847 school-aged children. Our aim was to examine the relations between young people’s gaming experiences and an objective test of reasoning performance. Using a Bayesian hypothesis testing approach, evidence for the gaming-enhancement and null hypotheses were compared. Results provided no substantive evidence supporting the idea that having preference for or regularly playing commercially available games was positively associated with reasoning ability. Evidence ranged from equivocal to very strong in support for the null hypothesis over what was predicted. The discussion focuses on the value of Bayesian hypothesis testing for investigating electronic gaming effects, the importance of open science practices, and pre-registered designs to improve the quality of future work.

Introduction

Electronic games are now a ubiquitous form of entertainment and it is popularly believed that the time spent playing games might have positive benefits that extend outside of gaming contexts ( Lenhart et al., 2008 ; McGonigal, 2012 ). This general idea, the gaming-enhancement hypothesis , posits that electronic gaming contexts influence a range of perceptual and cognitive abilities because they present complex, dynamic, and demanding challenges. A recent representative study of British adults and young people suggests this idea is widely held; Nine in ten think brain-training games provide an effective way to improve focus, memory, and concentration, and as many as two thirds have tried using these games to improve their own cognitive abilities ( Clemence et al., 2013 ). This view is increasingly profitable and controversial ( Nuechterlein et al., 2016 ). Given the intense public and private interest and investment in games as a way of improving cognition and reasoning, is noteworthy that the scientific literature investigating the gaming-enhancement hypothesis, though promising, is still at an early stage.

A growing body of research suggests that common varieties of electronic gaming experience might enhance general cognitive skills and abilities. These studies show that those who opt to, or are assigned to, play a range of commercially available games show measurable differences in terms of their visual and spatial abilities ( Quaiser-Pohl, Geiser & Lehmann, 2006 ; Green & Bavelier, 2006 ), executive functioning, information processing ( Maillot, Perrot & Hartley, 2012 ), and performance at specialist skills such as laparoscopic surgery ( Rosser et al., 2007 ). In particular, action games ( Green & Bavelier, 2006 ), strategy games ( Basak et al., 2008 ), and multiplayer online games ( Whitlock, McLaughlin & Allaire, 2012 ), have been identified as having enhancing effects because they provide complex multi-tasking environments that require players to integrate a range of sensory inputs. Researchers argue these environments lead players to implement adaptive strategies to meet the varied demands of these virtual contexts ( Bavelier et al., 2012 ).

There is good reason to think that predispositions to engage specific kinds of games may relate to cognitive performance and reasoning more broadly. For example, in studying skill acquisition among strategy game players, researchers have reported evidence that differences in brain volume are correlated with speed and performance in gaming contexts ( Basak et al., 2011 ). Exploration and learning in online gaming contexts closely mirror their offline analogues. Those with pre-existing strengths tend to thrive at online gaming challenges initially, but those with low levels of starting ability quickly close the performance gap ( Stafford & Dewar, 2013 ). It is possible that such inclinations guide players to games that suit them. Findings from experimental and quasi-experimental studies suggest both preference and experience matter in terms of small to moderate effects across a wide range of cognitive performance indicators ( Powers et al., 2013 ).

Unfortunately, many of these studies have pronounced shortcomings that temper the broad promise of the gaming-enhancement hypothesis ( Van Ravenzwaaij et al., 2014 ). For example, most of the evidence supporting this view has been derived from small-scale surveys, intervention studies in university settings, or from samples of older adults. A recent review of this literature indicates a small yet consistent link between gaming and general reasoning ability (Cohen’s d  = 0.24), but the average sample size of studies examining the gaming-enhancement hypothesis is only 32 participants ( Powers et al., 2013 ). Perhaps as a consequence of such small samples, the effects sizes reportedly linking games to cognitive abilities vary widely as a function of the methods used and the research groups investigating them. For example, studies published in top-tier journals, while typically using very small samples, report substantially larger effects compared to the rest of the literature (e.g., Cohen’s d  = 0.85; Powers et al., 2013 ). Moreover, despite the fact that games are played by the overwhelming majority of young people ( Lenhart et al., 2015 ), fewer than one in ten studies of the gaming enhancement study have included participants under the age of 18. Childhood and adolescence see profound development in cognitive abilities, and though negative effects of games are fiercely debated ( Mills, 2014 ; Bell, Bishop & Przybylski, 2015 ), there is very little evidence concerning their possible positive effects in this cohort. Further, nearly all studies examining gaming effects do so by studying individual games or game types in isolation. So although there is reason to think that action ( Green & Bavelier, 2006 ), strategy ( Basak et al., 2011 ), and online game play ( Whitlock, McLaughlin & Allaire, 2012 ) could have positive effects it is not possible to know if attitudes, preferences, or engagement with games in general, or specific subtypes in particular, are driving the effects reported. Finally, there are a number of larger-scale intervention studies that show evidence that does not support or directly contradicts the gaming-enhancement hypothesis ( Chisholm et al., 2010 ; Kennedy et al., 2011 ). Given the nature of the existing evidence, research that systematically addresses these gaps in our knowledge is needed.

Present research

The aim of the present research was to evaluate the extent to which everyday electronic game engagement by young people relates to their general reasoning abilities. In particular, we were interested to test how personal preferences for specific types of games related to reasoning ability. In line with previous research, we hypothesized that those who gravitate towards action, online, and strategy games would show higher levels of cognitive performance on a test of deductive reasoning ability ( Basak et al., 2011 ). Our second goal was to test whether regular active engagement with action, online, and strategy games was linked to cognitive ability. In line with highly cited work in the area, we hypothesized that those playing these challenging games for more than five hours each week ( Green & Bavelier, 2006 ) would show higher levels of reasoning ability.

Data source

The present study utilized data collected in the first year of the Effects of Digital Gaming on Children and Teenagers in Singapore (EDGCTS) project. This dataset has been used in numerous previous publications focusing on the effects of gaming on motivation, dysregulated behavior, and interpersonal aggression in young people ( Wan & Chiou, 2006 ; Gentile et al., 2009 ; Wang, Liu & Khoo, 2009 ; Chen et al., 2009 ; Choo et al., 2010 ; Gentile et al., 2011 ; Wang et al., 2011 ; Li, Liau & Khoo, 2011 ; Chng et al., 2014 ; Prot et al., 2014 ; Gentile et al., 2014 ; Busching et al., 2015 ; Eichenbaum & Kattner, 2015 ; Chng et al., 2015 ; Liau et al., 2015b ; Choo et al., 2015 ; Liau et al., 2015a ). A subsample of data from the EDGCTS project was used for the present study because it included self-reports of game play and an objective test of participants’ reasoning abilities. Neither of these variables has been the focus of previously published studies, and a complete list of publications based on the EDGCTS dataset can be found on the Open Science Framework (osf.io/je786).

Participants, ethics, and data

Ethical clearance for data collection was granted through the Institutional Review Board of Nanyang Technological University in Singapore. Because of the combined length of the EDGCTS testing protocol, data collection was conducted over a four-day period to reduce participant burden and minimize sequence effects. The research was deemed low risk and consent was obtained from parents through liaison teachers. Participants were informed that involvement in the project was voluntary and that they could withdraw at any time. Ethical review for this secondary data analysis was conducted by the research ethics committee at the University of Oxford (SSH/OII/C1A-16-063).

In the first wave of the EDGCTS, quantitative data were collected from a total of 3,034 respondents. Gender information was present for 3,012 participants (99.3% of all cases), age data were available for 2,813 participants (92.7%), 2,135 participants provided the name of at least one electronic game they played (70.4%), and a total of 2,647 participants completed the reasoning test (87.2%). In sum, a total of 1,847 participants (60.9% of all cases), provided valid data and were included in subsequent analyses. These 1,847 participants ranged in age from 8 to 16 years ( M  = 10.97, SD = 1.99); 430 of these identified as female and 1,417 identified as male. The self-report materials, datasets, source code, and analysis code used in this study are available from the Open Science Framework (osf.io/je786).

Outcome variable

Reasoning ability.

Participants’ deductive reasoning ability was assessed using the 60-item Raven’s Standard Progressive Matrices Plus (RPM) task ( Raven, Raven & Court, 2003 ). The RPM, a widely used non-verbal test of reasoning ability, measures deductive intelligence by prompting takers to identify the key missing visual element that completes patterns shown in a series of increasingly complex 2 × 2, 2 × 3, 3 × 3, 4 × 4, and 6 × 6, matrices. This assessment was used because it has been well validated across a range of demographic and cross-cultural cohorts ( Raven, 2000 ). Because our participants were school-aged children, they completed the version of this multiple-choice test designed for group administration in educational settings for students between the ages of 8 and 16 years. Participants got a median of 29 of 60 matrices ( SD = 4.44) correct and age-adjusted reasoning scores were created for each participant in line with best-practices ( Savage-McGlynn, 2012 ). To this end, participants were segregated by age and their raw performance scores were transformed into z -scores such that their performance was standardized with respect to other children their age.

Explanatory variables

Participants’ electronic gaming was assessed through a series of questions asking about the games they frequently played. Participants were requested to provide the names of up to three games they played as well as an estimate of the amount of time they spend playing each. A total of 446 (24.1%) participants named a single game, 485 (26.3%) participants named two games, and 916 (49.6%) named three games. The titles of named games were content coded to mark if they belonged to one of the three game categories of interest: action games (e.g., Call of Duty, Halo), multiplayer online games (e.g., Maple Story, World of Warcraft), or strategy games (e.g., SimCity, StarCraft).

Game preference

Preference scores were created for each participant using the game names provided through self-report. If one or more of a participant’s named games belonged to the action, online, or strategy types, they were marked as expressing a preference for this kind of game (coded 1); if not, they were counted as not having a preference for this game type (coded 0). This meant three preference scores, one for each game type, were computed for each participant.

Regular play

Data from game preference scores and participants’ self-reported play behavior were used to determine if participants were regular players of specific game types. Scores were created for participants by combing information about the kinds of games they expressed preferences for and their self-reported amounts of weekly play. Amounts of weekly play were computed summing participant estimates of weekday engagement, multiplied by five, and estimates of weekend day engagement multiplied by two. Codes for game types were then used to create one game engagement score for each type of game. In line with the approach prescribed in previous research ( Green & Bavelier, 2006 ), participants were considered regular active players of a game type if they invested five or more hours in a given game type in a week (coded 1), and were coded 0 if they did not spend any time playing this game type in a typical week. Table 1 presents summary statistics for participant game preferences and proportions of active players of each game type.

Preliminary analyses

Zero-order bivariate correlations between observed variables are presented in Table 2 . Because 36 correlations were conducted, we adjusted our p value threshold for rejecting the null hypothesis from 0.05 to 0.0014 ( Holm, 1979 ). Analyses indicated that older participants were more likely to report higher levels of engagement with games as compared to younger ones ( r s = .130–.278). Similarly, female participants were less likely to engage action and strategy games ( r s = .128–.217). Gender was not significantly related to online game play nor was it related to age-adjusted reasoning ability (all p s > 0.0014). Because gender was not associated with our target outcome, which was centered on age, neither age nor gender were considered as covariates in hypothesis testing.

Game preference and reasoning ability

In line with meta-analytic evidence a series of one-sided Bayesian independent samples t -tests were used to quantify evidence for the game-enhancement hypothesis ( Rouder et al., 2012 ; Powers et al., 2013 ; Morey, Romeijn & Rouder, 2016 ), that specified preference for action, online, and strategy games would be related to better deductive reasoning ability. Table 3 presents a summary of these results and observed means using a Cauchy prior of 0.24, effect size for quasi-experiments on measures of reasoning and intelligence, and a second prior effect size for the enhancement hypothesis as reported in top-tier journals (Cohen’s d of 0.85; Powers et al., 2013 ). Results provided very strong support for the null hypothesis over the alternative for action games (BF 10 = 0.06; M 0  = 0.02, SD 0  = 1.00, M 1  =  − 0.12, SD 1 = 0.97) and equivocal support for alternative hypothesis for those who preferred strategy games (BF 10 = 1.45; M 0  =  − 0.02, SD 0  = 1.02, M 1  = 0.08, SD 1 = 0.93), or online multiplayer games (BF 10 = 2.815; M 0  =  − 0.5, SD 0  = 1.04, M 1  = 0.05, SD 1 = 0.95). In examining the robustness of these Bayes factors it is clear that evidence for effects larger than the average in the literature are also not supported. Results derived using the larger effect sizes reported in top-tier journals for the enhancement effect, d  = 0.85, appeared less likely as evidence was 1.25 and 57.01 times more likely to have been observed under the null hypothesis than under the gaming-enhancement hypothesis (see Table 3 ). Figure 1A – 1C , present relative evidence for the gaming enhancement hypothesis for effect sizes ranging from a 0.0 to 1.5. Taken together with the focused hypothesis tests, these results indicated participants’ preferences for these specific types of games were not reliably linked to their deductive reasoning ability in these data.

BF 01 denotes evidence favoring the Null hypothesis. BF 10 denotes evidence favoring the alternative hypothesis.

Note. Equal variances are assumed. (A) through (C) represent game preference, and (D) through (F) represent regular game play. (A) Action games. (B) Strategy games. (C) Online games (D) Action games. (E) Strategy games. (F) Online Games.

Regular game play and reasoning ability

To examine the relations between regular action, strategy, or online game play and reasoning ability, three additional Bayesian hypothesis tests were conducted following the procedure used for game preferences. These models evaluated the relative evidence for the null hypothesis as well as the gaming-enhancement hypothesis that postulates that playing these games for more than five hours each week would be associated with players’ reasoning abilities. Results provided moderate support for the null over the alternative hypothesis for action games (BF 10 = 0.12; M 0  = 0.02, SD 0  = 1.00, M 1  =  − 0.07, SD 1  = 0.99), equivocal evidence for strategy games (BF 10 = 1.120; M 0  =  − 0.023, SD 0  = 1.02, M 1  = 0.08, SD 1 = 0.90), and equivocal to moderate evidence for the null over the alternative hypothesis for multiplayer online games (BF 10 = 0.34; M 0  =  − 0.4, SD 0  = 1.04, M 1  = 0.01, SD 1 = 0.97). Robustness checks using effect size for the gaming enhancement hypothesis as reported in top-tier journals ( d  = 0.85; Powers et al., 2013 ) indicated evidence against the this hypothesis. Data were between 2.45 and 29.15 times more likely to be observed under the null hypothesis. Figure 1D – 1F present relative evidence for the gaming enhancement hypothesis for effect sizes ranging from of 0.0 to 1.5. Taken together with the previous results, these findings suggest it is unlikely that regular active play of these games is systematically related to higher levels of general reasoning abilities.

The promise that electronic games might positively influence human cognition is one that generates intense public, corporate, and scientific interest. The present research drew on a large sample of school-aged children and considered both their electronic gaming and cognitive performance to test of the gaming-enhancement hypothesis. Of central interest was the nature of the potential relations between children’s self-reported preferences and gaming habits, and performance on a widely used test of deductive reasoning ability. Contrary to our expectations, the results did not provide substantial evidence in support of the idea that the complex and interactive experiences provided by commercially available games generalize to functioning outside of gaming contexts. In most cases, the evidence was in favor of the null hypothesis over this account.

We hypothesized that those who express preferences for action, strategy, and multiplayer online games, modes of play would show modestly higher levels of cognitive performance as seen in previous smaller-scale studies ( Basak et al., 2011 ). In contrast to what was expected, we found equivocal to very strong evidence favoring the null hypothesis over this prediction. Second, we hypothesized that regular engagement with games—playing five or more hours a week of action, strategy, and multiplayer online games—would be linked with better reasoning ability. Results from our analyses did not support this prediction. Evidence for regular strategy game players were equivocal, but ranged from moderately to strongly in favor of the null for these types of games. Taken together, our findings disconfirmed the gaming-enhancement hypothesis, especially in terms of the larger effects reported in top-tier journals.

Our approach carries a number of strengths that should inform future studies of gaming effects. First, evidence from this study relied on data provided by more than 1,800 young people, a sample over 50 times larger than the average for studies examining gaming and cognition ( Powers et al., 2013 ). If research is to sort out the cognitive dynamics of play, larger and more robust sampling is needed. Second, the Bayesian hypothesis testing approach we adopted used open source software (JASP; JASP Team, 2016 ) and allowed our study to quantify evidence for the both the null hypothesis as well as a plausible alternative based on the existing literature. Although the analysis plan for this study was not registered before the data were known, the framework provides valuable empirical data that researchers can use as the basis, or prior, to inform their own pre-registered designs. Finally, if indeed scholars are increasingly skeptical of corporate attempts to sell games based on their purported upsides (e.g.,  Allaire et al., 2014 ), this vigor should be extended to all scientific inquiry in this area, for example by making the materials, data, source and analysis code openly available. Future research making both positive and negative claims regarding the effects of gaming on young people should do likewise.

Limitations and future directions

The present study presents a number of limitations that suggest promising avenues for future work. First, because the data under study were cross-sectional they capture an easy to interpret pattern of results that represent a snapshot in time. The data structure did so at the expense of being able to draw causal inferences, and a complementary approach would examine long-term salutary effects on cognition, ideally as a function of experimental manipulations of game exposure. Second, data regarding participants’ game preferences and regular play were collected through self-report. Research indicates that some participants, and young people in particular, provide exaggerated data when it comes to taboo activities such as sexual habits and drug use ( Robinson-Cimpian, 2014 ). It is possible that the average levels of engagement reported by our participants disguised interesting patterns of engagement which merit inquiry. For example, infrequent periods of high engagement (e.g., binge-playing) might have its own special relations with reasoning abilities. If so, an experience-sampling based approach would be needed to assess both between- and within-person variability with respect to the relation between gaming and reasoning ability. Finally, the present study only used a single assessment of general cognitive abilities, the Ravens Progressive Matrices task ( Raven, 2000 ). There are many other facets to intelligence and executive control that might be more sensitive to influence by regular electronic gaming. Measures of naïve reasoning ( Masson, Bub & Lalonde, 2011 ), short and long-term memory ( Melby-Lervåg & Hulme, 2013 ), audio processing ( Liu & Holt, 2011 ) might be more liable to be influenced by gaming. If the gaming-enhancement hypothesis is not broadly accurate, it may find empirical support under conditions where these alternative aspects of intelligence and reasoning abilities are under study.

Closing remarks

The promise that popular games can enhance cognitive skills is an alluring one. Our findings suggest there is no relation between interest in, or regular play of, electronic games and general reasoning ability. As such, we advise that future research examining the potential influences of gaming contexts on players should pre-register their analysis plans or follow the registered reports process (e.g.,  Chambers, 2013 ; Elson, Przybylski & Krämer, 2015 ). Such steps would go a long way to reduce researcher degrees of freedom which might, along with publication bias, affect conclusions drawn about the effects of gaming and cognitive enhancement ( Feynman, 1974 ; Gelman & Loken, 2013 ; Nissen et al., 2016 ). While the research presented here might be further informed by additional work conducted to these standards, our findings above offer an early exploration of the gaming-enhancement hypothesis which is well-powered and guided by open-science.

Funding Statement

This research was partially funded by a John Fell Fund Grant (CZD08320) through the University of Oxford to Dr. Przbylski, and a joint grant awarded by the Ministry of Education, Singapore and the Media Development Authority f (EPI/06AK) to Professor Wang. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Additional Information and Declarations

The authors declare there are no competing interests.

Andrew K. Przybylski conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

John C. Wang conceived and designed the experiments, performed the experiments, contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper.

The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):

Ethical clearance for the Effects of Digital Gaming on Children and Teenagers in Singapore project (EDGCTS) was sought and granted through the Institutional Review Board of Nanyang Technological University in Singapore. The Research and Ethics Committee at the Oxford Internet Institute conducted ethical review for secondary data analysis on the EDGCTS dataset (SSH/OII/C1A-16-063).

The Impact of Online Games on Student Academic Performance

21 Pages Posted: 1 Jun 2023

Beimbet Beibit

Nazarbayev Intellectual School

Date Written: May 12, 2023

Online video gaming has become a popular leisure activity among students, but concerns have been raised about its potential impact on academic performance. While some argue that video games can enhance cognitive skills, others claim that excessive gaming can lead to poor academic performance and even addiction. This research aims to investigate the influence of online video gaming on the academic performance of students. The study will examine the relationship between online gaming and academic performance, as well as factors that may moderate this relationship, such as the days of gaming sessions, gender, and academic performance. A survey was conducted among a sample of students from NIS school(73 participants), to collect data on their gaming habits and academic performance. The data collected will be analyzed using statistical methods to determine whether there is a significant correlation between online gaming and academic performance. The findings of this study can be used to inform educational policy and practice, and to promote healthy gaming habits among students.

Keywords: video games, academic performance, addiction, influence

JEL Classification: I

Suggested Citation: Suggested Citation

Beimbet Beibit (Contact Author)

Nazarbayev intellectual school ( email ), do you have a job opening that you would like to promote on ssrn, paper statistics.

ORIGINAL RESEARCH article

The effects of online game addiction on reduced academic achievement motivation among chinese college students: the mediating role of learning engagement.

• 1 BinZhou College of Science and Technology, Binzhou, China
• 2 Binzhou Polytechnic, Binzhou, China
• 3 Faculty of Education, Beijing Normal University, Beijing, China
• 4 National Institute of Vocational Education, Beijing Normal University, Beijing, China

Introduction: The present study aimed to examine the effects of online game addiction on reduced academic achievement motivation, and the mediating role of learning engagement among Chinese college students to investigate the relationships between the three variables.

Methods: The study used convenience sampling to recruit Chinese university students to participate voluntarily. A total of 443 valid questionnaires were collected through the Questionnaire Star application. The average age of the participants was 18.77 years old, with 157 males and 286 females. Statistical analysis was conducted using SPSS and AMOS.

Results: (1) Chinese college students’ online game addiction negatively affected their behavioral, emotional, and cognitive engagement (the three dimensions of learning engagement); (2) behavioral, emotional, and cognitive engagement negatively affected their reduced academic achievement motivation; (3) learning engagement mediated the relationship between online game addiction and reduced academic achievement motivation.

1. Introduction

Online games, along with improvements in technology, have entered the daily life of college students through the popularity of computers, smartphones, PSPs (PlayStation Portable), and other gaming devices. Online game addiction has recently become a critical problem affecting college students’ studies and lives. As early as 2018, online game addiction was officially included in the category of “addictive mental disorders” by the World Health Organization (WHO), and the International Classification of Diseases (ICD) was updated specifically to include the category of “Internet Gaming Disorder” (IGD). Prior research investigating Chinese college students’ online game addiction status mostly comprised regional small-scale studies. For example, a study on 394 college students in Chengde City, Hebei province, China showed that the rate of online game addiction was about 9% ( Cui et al., 2021 ). According to the results of an online game survey conducted by China Youth Network (2019) on 682 Chinese college students who played online games, nearly 60% of participants played games for more than 1 h a day, over 30% stayed up late because of playing games, over 40% thought that playing games had affected their physical health, over 70% claimed that games did not affect their studies, and over 60% had spent money on online games. This phenomenon has been exacerbated by the fact that smartphones and various portable gaming devices have become new vehicles for gaming with the development of technology. The increase in the frequency or time spent on daily gaming among adolescents implies a growth in the probability of gaming addiction, while an increase in the level of education decreases the probability of gaming addiction ( Esposito et al., 2020 ; Kesici, 2020 ). Moreover, during the COVID-19 pandemic, adolescents’ video game use and the severity of online gaming disorders increased significantly ( Teng et al., 2021 ).

A large body of literature on the relationship between problematic smartphone use and academic performance has illustrated the varying adverse effects of excessive smartphone obsession ( Durak, 2018 ; Mendoza et al., 2018 ; Rozgonjuk et al., 2018 ). These effects are manifested in three critical ways: first, the more frequently cell phones are used during study, the greater the negative impact on academic performance and achievement; second, students are required to master the basic skills and cognitive abilities to succeed academically, which are negatively affected by excessive cell phone use and addiction ( Sunday et al., 2021 ); third, online game addiction negatively affects students’ learning motivation ( Demir and Kutlu, 2018 ; Eliyani and Sari, 2021 ). However, there is currently a lack of scientifically objective means of effective data collection regarding online game addiction among college students in China, such as big data. Hong R. Z. et al. (2021) and Nong et al. (2023) suggested that the impact of addiction on students’ learning should be explored more deeply.

Since the 1990s, learning engagement has been regarded as a positive behavioral practice in learning in Europe and the United States, and plays an important role in the field of higher education research ( Axelson and Flick, 2010 ). Recently, studies on learning engagement among college students have also been a hot topic in various countries ( Guo et al., 2021 ). According to Fredricks et al. (2004) , learning engagement includes three dimensions: behavioral, emotional, and cognitive.

The concept of behavioral engagement encompasses three aspects: first, positive behavior in the classroom, such as following school rules and regulations and classroom norms; second, engagement in learning; and third, active participation in school activities ( Finn et al., 1995 ). Emotional engagement refers to students’ responses to their academic content and learning environment. The emotional responses to academic content include students’ emotional responses such as interest or disinterest in learning during academic activities ( Kahu and Nelson, 2018 ), while the emotional responses to the learning environment refer to students’ identification with their peers, teachers, and the school environment ( Stipek, 2002 ). Cognitive engagement is often associated with internal processes such as deep processing, using cognitive strategies, self-regulation, investment in learning, the ability to think reflectively, and making connections in daily life ( Khan et al., 2017 ). Cognitive engagement emphasizes the student’s investment in learning and self-regulation or strategies.

According to Yang X. et al. (2021) , learning engagement refers to students’ socialization, behavioral intensity, affective qualities, and use of cognitive strategies in performing learning activities. Besides, Kuh et al. (2007) argued that learning engagement was “the amount of time and effort students devote to instructional goals and meaningful educational practices.” Learning engagement is not only an important indicator of students’ learning process, but also a significant predictor of students’ academic achievement ( Zhang, 2012 ). It is also an essential factor in promoting college students’ academic success and improving education quality.

As one of the crucial components of students’ learning motivation ( Han and Lu, 2018 ), achievement motivation is the driving force behind an individual’s efforts to put energy into what he or she perceives to be valuable and meaningful to achieve a desired outcome ( Story et al., 2009 ). It can be considered as achievement motivation when an individual’s behavior involves “competing at a standard of excellence” ( Brunstein and Heckhausen, 2018 ). Students’ achievement motivation ensures the continuity of learning activities, achieving academic excellence and desired goals ( Sopiah, 2021 ). Based on the concept of achievement motivation, academic achievement motivation refers to the mental perceptions or intentions that students carry out regarding their academic achievement, a cognitive structure by which students perceive success or failure and determine their behavior ( Elliot and Church, 1997 ). Related research also suggests that motivation is one variable that significantly predicts learning engagement ( Xiong et al., 2015 ).

Therefore, it is worthwhile to investigate the internal influence mechanism of college students’ online game addiction on their reduced academic achievement motivation and the role of learning engagement, which is also an issue that cannot be ignored in higher education research. The present study explored the relationship between online game addiction, learning engagement, and reduced academic achievement motivation among college students by establishing a structural equation model (SEM) to shed light on the problem of online game addiction among college students.

2. Research model and hypotheses

2.1. research model.

Previous research usually regarded learning engagement as a variable of one or two dimensions, and scholars tend to favor the dimension of behavioral engagement. However, other ignored dimensions are inseparable parts of learning engagement ( Dincer et al., 2019 ). In a multi-dimensional model, the mutual terms of each dimension form a single composite structure. Therefore, the present study took the structure proposed by Fredricks et al. (2004) as a reference, divided learning engagement into behavioral, emotional, and cognitive dimensions as mediating variables, and explored the relationship between online game addiction, learning engagement, and reduced academic achievement motivation. The research frame diagram is shown in Figure 1 .

Figure 1 . The research model.

2.2. Research questions

2.2.1. the relationship between online game addiction and learning engagement.

Learning engagement has been viewed as a multidimensional concept in previous studies. Finn (1989) proposed the participation-identification model to make pioneering progress in learning engagement study. Schaufeli et al. (2002) suggested that learning engagement was an active, fulfilling mental state associated with learning. Chapman (2002) pointed out affective, behavioral, and cognitive criteria for assessing students’ learning engagement based on previous research. Fredricks et al. (2004) systematically outlined learning engagement as an integration of behavioral, emotional, and cognitive engagement. The updated International Classification of Diseases [ World Health Organization (WHO), 2018a , b ] specifies several diagnostic criteria for gaming addiction, including the abandonment of other activities, the loss of interest in other previous hobbies, and the loss or potential loss of work and social interaction because of gaming. Past studies have shown the adverse effects of excessive Internet usage on students’ learning. Short video addiction negatively affects intrinsic and extrinsic learning motivation ( Ye et al., 2022 ). Students’ cell phone addiction negatively affects academic commitment, academic performance, and relationship facilitation, all of which negatively affect their academic achievement ( Tian et al., 2021 ). The amount of time spent surfing the Internet and playing games has been identified to negatively affect students’ cognitive ability ( Pan et al., 2022 ). College students’ cell phone addiction, mainly reflected in cell phone social addiction and game entertainment addiction, has also been noted to impact learning engagement; specifically, the higher the level of addiction, the lower the learning engagement ( Qi et al., 2020 ). Gao et al. (2021) also showed that cell phone addiction among college students could negatively affect their learning engagement. Choi (2019) showed that excessive use of cell phones might contribute to smartphone addiction, which also affects students’ learning engagement. Accordingly, the following three research hypotheses were proposed.

H1 : Online game addiction negatively affects behavioral engagement.

H2 : Online game addiction negatively affects emotional engagement.

H3 : Online game addiction negatively affects cognitive engagement.

2.2.2. The relationship between learning engagement and reduced academic achievement motivation

Achievement motivation is people’s pursuit of maximizing individual value, which embodies an innate drive, including the need for achievement, and can be divided into two parts: the intention to succeed and the intention to avoid failure ( McClelland et al., 1976 ). On this basis, Weiner (1985) proposed the attributional theory of achievement motivation, suggesting that individuals’ personality differences, as well as the experience of success and failure, could influence their achievement attributions and that an individual’s previous achievement attributions would affect his or her expectations and emotions for the subsequent achievement behavior while expectations and emotions could guide motivated behavior. Birch and Ladd (1997) indicated that behavioral engagement involved positive behavioral attitudes such as hard work, persistence, concentration, willingness to ask questions, and active participation in class discussions to complete class assignments. Students’ attitudes toward learning are positively related to achievement motivation ( Bakar et al., 2010 ). Emotional engagement involves students’ sense of identity with their peers, teachers, and the school environment ( Stipek, 2002 ). Students’ perceptions of the school environment influence their achievement motivation ( Wang and Eccles, 2013 ). Cognitive engagement encompasses the ability to use cognitive strategies, self-regulation, investment in learning, and reflective thinking ( Khan et al., 2017 ). Learning independence and problem-solving abilities predict student motivation ( Saeid and Eslaminejad, 2017 ). Hu et al. (2021) indicated that cognitive engagement had the most significant effect on students’ academic achievement among the learning engagement dimensions, and that emotional engagement was also an important factor influencing students’ academic achievement. Therefore, the following three research hypotheses were proposed:

H4 : Behavioral engagement significantly and negatively affects the reduced academic achievement motivation.

H5 : Emotional engagement significantly and negatively affects the reduced academic achievement motivation.

H6 : Cognitive engagement significantly and negatively affects the reduced academic achievement motivation.

2.2.3. The relationship between online game addiction, learning engagement, and reduced academic achievement motivation

Past studies have demonstrated the relationship between online game addiction and students’ achievement motivation. For example, a significant negative correlation between social network addiction and students’ motivation to progress has been reported ( Haji Anzehai, 2020 ), and a significant negative correlation between Internet addiction and students’ achievement motivation has been reported ( Cao et al., 2008 ). Students addicted to online games generally have lower academic achievement motivation because they lack precise academic planning and motivation ( Chen and Gu, 2019 ). Yayman and Bilgin (2020) pointed out a correlation between social media addiction and online game addiction. Accordingly, there might be a negative correlation between online game addiction and academic achievement motivation among college students.

Students addicted to online games generally have lower motivation for academic achievement because they lack precise academic planning and learning motivation ( Chen and Gu, 2019 ). Similarly, Haji Anzehai (2020) reported a significant negative correlation between social network addiction and students’ motivation to progress.

Learning engagement is often explored as a mediating variable in education research. Zhang et al. (2018) found that learning engagement was an essential mediator of the negative effect of internet addiction on academic achievement in late adolescence and is a key factor in the decline in academic achievement due to students’ internet addiction. Li et al. (2019) noted that college students’ social networking site addiction significantly negatively affected their learning engagement, and learning engagement mediated the relationship between social networking addiction and academic achievement. Accordingly, the following research hypothesis was proposed.

H7 : Learning engagement mediates the relationship between online game addiction and reduced academic achievement motivation.

3. Research methodology and design

3.1. survey implementation.

The present study employed the Questionnaire Star application for online questionnaire distribution. Convenience sampling was adopted to recruit Chinese college students to participate voluntarily. The data were collected from October 2021 to January 2022 from a higher vocational college in Shandong province, China. Participants were first-and second-year students. According to Shumacker and Lomax (2016) , the number of participants in SEM studies should be approximately between 100 and 500 or more. In the present study, 500 questionnaires were returned, and 443 were valid after excluding invalid responses. The mean age of the participants was 18.77 years. There were 157 male students, accounting for 35.4% of the total sample, and 286 female students, accounting for 64.6%.

3.2. Measurement instruments

The present empirical study employed quantitative research methods by collecting questionnaires for data analysis. The items of questionnaires were adapted from research findings based on corresponding theories and were reviewed by experts to confirm the content validity of the instruments. The distributed questionnaire was a Likert 5-point scale (1 for strongly disagree , 2 for disagree , 3 for average , 4 for agree , and 5 for strongly agree ). After the questionnaire was collected, item analysis was conducted first, followed by reliability and validity analysis of the questionnaire constructs using SPSS23 to test whether the scale met the criteria. Finally, research model validation was conducted.

3.2.1. Online game addiction

In the present study, online game addiction referred to the addictive behavior of college students in online games, including mobile games and online games. The present study adopted a game addiction scale compiled by Wu et al. (2021) and adapted the items based on the definition of online game addiction. The adapted scale had 10 items. Two examples of the adapted items in the scale were: “I will put down what should be done and spend my time playing online games” and “My excitement or expectation of playing an online game is far better than other interpersonal interactions.”

3.2.2. Learning engagement

In the present study, learning engagement included students’ academic engagement in three dimensions: behavioral, emotional, and cognitive. The learning engagement scale compiled by Luan et al. (2020) was adapted based on its definition. The adapted scale had 26 questions in three dimensions: behavioral, emotional, and cognitive engagement. Two examples of the adapted items in the scale are: “I like to actively explore unfamiliar things when I am doing my homework” and “I will remind myself to double-check the places where I tend to make mistakes in my homework.”

3.2.3. Reduced academic achievement motivation

Reduced academic achievement motivation in the present study refers to the reduction in college students’ intrinsic tendency to enjoy challenges and achieve academic goals and academic success. The achievement motivation scale developed by Ye et al. (2020) was adapted to measure reduced academic achievement motivation. The adapted scale had 10 items. Two examples of the adapted items in the scale are: “Since playing online games, I do not believe that the effectiveness of learning is up to me, but that it depends on other people or the environment” and “Since I often play online games, I am satisfied with my current academic performance or achievement and do not seek higher academic challenges.”

4. Results and discussion

4.1. internal validity analysis of the measurement instruments.

In the present study, item analysis was conducted using first-order confirmatory factor analysis (CFA), which can reflect the degree of measured variables’ performance within a smaller construct ( Hafiz and Shaari, 2013 ). The first-order CFA is based on the streamlined model and the principle of independence of residuals. According to Hair et al. (2010) and Kenny et al. (2015) , it is recommended that the value of χ 2 / df in the model fitness indices should be less than 5; the root mean square error of approximation (RMSEA) value should be greater than 0.100; the values of the goodness of fit index (GFI) and adjusted goodness of fit index (AGFI) should not be lower than 0.800; the factor loading (FL) values of the constructs should also be greater than 0.500. Based on the criteria above, the items measuring the online game addiction construct were reduced from 10 to seven; the items measuring the behavioral engagement construct were reduced from nine to six; the items measuring the emotional engagement construct were reduced from nine to six; the items measuring the cognitive engagement construct were reduced from eight to six; and the items measuring the reduced academic achievement motivation construct was reduced from 10 to six, as shown in Table 1 .

Table 1 . First-order confirmatory factor analysis.

4.2. Construct reliability and validity analysis

In order to determine the internal consistency of the constructs, the reliability of the questionnaire was tested using Cronbach’ s α value. According to Hair et al. (2010) , a Cronbach’ s α value greater than 0.700 indicates an excellent internal consistency among the items, and the constructs’ composite reliability (CR) values should exceed 0.700 to meet the criteria. In the present study, the Cronbach’ s α values for the constructs ranged from 0.911 to 0.960, and the CR values ranged from 0.913 to 0.916, which met the criteria, as shown in Table 2 .

Table 2 . Construct reliability and validity of constructs.

In the present study, convergent validity was confirmed by two types of indicators, FL and average variance extracted (AVE). According to Hair et al. (2011) , an FL value should be greater than 0.500, and items with an FL value less than 0.500 should be removed; and AVE values should be greater than 0.500. In the present study, the FL values of the constructs ranged from 0.526 to 0.932, and the AVE values ranged from 0.600 to 0.805; all dimensions met the recommended criteria, as shown in Table 2 .

According to Awang (2015) and Hair et al. (2011) , the square root of the AVE of each construct (latent variable) should be greater than its correlation coefficient values with other constructs to indicate the ideal discriminant validity. The results of the present study showed that the three constructs of online game addiction, learning engagement, and reduced academic achievement motivation had good discriminant validity in the present study, as shown in Table 3 .

Table 3 . Discriminant validity analysis.

4.3. Correlation analysis

Pearson’s correlation coefficient is usually used to determine the closeness of the relationship between variables. A correlation coefficient greater than 0.8 indicates a high correlation between variables; a correlation coefficient between 0.3 and 0.8 indicates a moderate correlation between variables; while a correlation of less than 0.3 indicates a low correlation. Table 4 shows the Correlation Analysis results. Online game addiction was moderately negatively correlated with behavioral engagement ( r  = −0.402, p  < 0.001), moderately negatively correlated with emotional engagement ( r  = −0.352, p  < 0.001), slightly negatively correlated with cognitive engagement ( r  = −0.288, p  < 0.001), and slightly positively correlated with reduced academic achievement motivation ( r  = 0.295, p  < 0.001). Behavioral engagement was moderately positively correlated with emotional engagement ( r  = 0.696, p  < 0.001), moderately positively correlated with cognitive engagement ( r  = 0.601, p  < 0.001), and moderately negatively correlated with reduced academic achievement motivation ( r  = −0.497, p  < 0.001). Emotional engagement was moderately positively correlated with cognitive engagement ( r  = 0.787, p  < 0.001) and moderately negatively correlated with reduced academic achievement motivation ( r  = −0.528, p  < 0.001). Cognitive engagement was moderately negatively correlated with reduced motivation for academic achievement ( r  = −0.528, p  < 0.001).

Table 4 . Correlation analysis.

4.4. Analysis of fitness of the measurement model

According to Hair et al. (2010) and Abedi et al. (2015) , the following criteria should be met in the analysis for measurement model fitness: the ratio of chi-squared and degree of freedom ( χ 2 / df ) should be less than 5; the root mean square error of approximation (RMSEA) should not exceed 0.100; the goodness of fit index (GFI), adjusted goodness of fit index (AGFI), normed fit index (NFI), non-normed fit index (NNFI), comparative fit index (CFI), incremental fit index (IFI) and relative fit index (RFI) should be higher than 0.800; and the parsimonious normed fit index (PNFI) and the parsimonious fitness of fit index (PGFI) should be higher than 0.500. The model fitness indices in the present study were χ 2  = 1434.8, df  = 428, χ 2 / df  = 3.352, RMSEA = 0.073, GFI = 0.837, AGFI = 0.811, NFI = 0.899, NNFI = 0.920, CFI = 0.927, IFI = 0.927, RFI = 0.890, PNFI = 0.827, and PGFI = 0.722. The results were in accordance with the criteria, indicating a good fitness of the model in the present study ( Table 5 ).

Table 5 . Direct effects analysis.

4.5. Validation of the research model

Online game addiction had a negative effect on behavioral engagement ( β  = −0.486; t  = −9.143; p < 0.001). Online game addiction had a negative effect on emotional engagement ( β  = −0.430; t  = −8.054; p < 0.001). Online game addiction had a negative effect on cognitive engagement ( β  = −0.370; t  = −7.180; p < 0.001). Online game addiction had a positive effect on reduced academic achievement motivation ( β  = 0.19; t = −2.776; p < 0.01). Behavioral engagement had a negative effect on reduced academic achievement motivation ( β  = −0.238; t  = −3.759; p < 0.001). Emotional engagement had a negative effect on reduced academic achievement motivation ( β  = −0.221; t  = −2.687; p < 0.01), and cognitive engagement had a negative effect on reduced academic achievement motivation ( β  = −0.265; t  = −3.581; p < 0.01), as shown in Figure 2 Table 6 .

Figure 2 . Validation of the research model. *** p  < 0.001.

Table 6 . Indirect effects analysis.

Cohen’ s f 2 is an uncommon but valuable standardized effect size measure that can be used to assess the size of local effects ( Selya et al., 2012 ). When f 2 reaches 0.02 it represents a small effect size, 0.150 represents a medium effect size, and 0.350 represents a high effect size ( Hair et al., 2014 ). The explanatory power of online game addiction on behavioral engagement was 23.6%, and f 2 was 0.309. The explanatory power of online game addiction on emotional engagement was 18.5%, and f 2 was 0.227. The explanatory power of online game addiction on cognitive engagement was 13.7%, and f 2 was 0.159. The explanatory power of behavioral, emotional, and cognitive engagement on reduced academic achievement motivation was 23.9%, and f 2 was 0.314. Figure 2 illustrates the above findings.

4.6. Indirect effects analysis

Scholars are often interested in whether variables mediate the association between predicting and outcome variables. Therefore, mediating variables can partially or entirely explain the association ( Hwang et al., 2019 ). In research fields such as psychology and behavior, where the research situation is often more complex, multiple mediating variables are often required to clearly explain the effects of the independent variables on the dependent variables ( MacKinnon, 2012 ). Scientific quantitative research requires tests of confidence interval (CI; Thompson, 2002 ), and the standard value of the test numbers is often determined by 95% CI ( Altman and Bland, 2011 ). CI value not containing 0 indicates the statistical significance of the analysis results ( Nakagawa and Cuthill, 2007 ). According to the statistical results shown in Table 4 , behavioral engagement significantly positively mediated the relationship between online game addiction and reduced academic achievement motivation with a path coefficient of 0.230 and 95% CI ranging from 0.150 to 0.300 (excluding 0), p < 0.01; emotional engagement positively mediated the relationship between online game addiction and reduced academic achievement motivation with a path coefficient of 0.209, 95% CI ranging from 0.130 to 0.292 (excluding 0), p < 0.01; cognitive engagement positively mediated the relationship between online game addiction and reduced academic achievement motivation with a path coefficient of 0.170, 95% CI ranging from 0.100 to 0.250 (excluding 0), p < 0.01, as shown in Table 6 .

4.7. Discussion

4.7.1. analysis of the relationship between online game addiction and learning engagement.

Online game addiction is often negatively associated with students’ learning. For example, the problematic use of short videos was reported as negatively affecting students’ behavioral engagement, while behavioral engagement positively affected students’ emotional and cognitive engagement ( Ye et al., 2023 ). Meral (2019) highlighted that students’ learning attitudes and academic performance had a negative relationship with students’ addiction to online games. Demir and Kutlu (2018) found that online game addiction negatively affects students’ learning motivation. As the level of students’ game addiction increased, the level of their communication skills decreased ( Kanat, 2019 ). Furthermore, Tsai et al. (2020) pointed out a negative correlation between online game addiction and peer relationships as well as students’ learning attitudes. According to the results of the research model validation, it can be observed that: online game addiction negatively affected behavioral engagement, emotional engagement, and cognitive engagement. Therefore, it can be stated that online game addiction had significant and negative effects on all dimensions of learning engagement.

Online game addiction in the present study included aspects of computer game addiction and mobile phone game addiction. The results of the present study are consistent with the findings of Gao et al. (2021) , Choi (2019) , and Qi et al. (2020) , who pointed out that college students’ addiction to cell phones negatively affected their learning engagement.

4.7.2. Analysis of the relationship between learning engagement and reduced academic achievement motivation

For technology education in higher education, students’ intrinsic motivation for academic study predicts their learning engagement ( Dunn and Kennedy, 2019 ). In addition, learning engagement is positively correlated with academic achievement ( Fredricks and McColskey, 2012 ). Based on the research model validation results, behavioral, emotional, and cognitive engagement all negatively affected reduced academic achievement motivation. The findings are consistent with Hu et al.’s (2021) study which pointed out that cognitive engagement in the learning engagement dimension had the most significant effect on students’ academic achievement, and that emotional engagement was also an essential factor influencing students’ academic achievement. Lau et al. (2008) showed that achievement motivation positively predicted cognitive engagement in the learning engagement dimension. Mih et al. (2015) noted that achievement motivation positively predicted behavioral and emotional engagement in the learning engagement dimension. The present study supported the above discussion by confirming the association between learning engagement and reduced academic achievement motivation.

4.7.3. Analysis of the mediating role of learning engagement

According to the indirect effects analysis results of the present study, learning engagement negatively mediated the relationship between online game addiction and reduced academic achievement motivation. The findings support Haji Anzehai’s (2020) conclusion that social network addiction negatively correlated with students’ motivation to progress ( Haji Anzehai, 2020 ). It is also consistent with the findings of Chen and Gu (2019) that students addicted to online games generally had lower academic achievement motivation due to a lack of precise academic planning and motivation. Cao et al. (2008) found a significant negative correlation between Internet addiction and students’ achievement motivation. Similarly, Zhang et al. (2018) explored the intrinsic influencing mechanism of students’ Internet addiction on academic achievement decline in their late adolescence by identifying learning engagement as the important mediating variable. Li et al. (2019) proposed that social networking site addiction among college students significantly negatively affected learning engagement and that learning engagement mediated the relationship between social network addiction and students’ academic achievement. The present study findings also support the discussion above.

5. Conclusion and suggestions

5.1. conclusion.

Currently, the problem of online game addiction among college students is increasing. The relationship between online game addiction, learning engagement, and reduced academic achievement motivation still needs to be explored. The present study explored the relationships between the three aforementioned variables by performing SEM. The results of the study indicated that: (1) online game addiction negatively affected behavioral engagement; (2) online game addiction negatively affected emotional engagement; (3) online game addiction negatively affected cognitive engagement; (4) behavioral engagement negatively affected reduced academic achievement motivation; (5) emotional engagement negatively affected reduced academic achievement motivation; (6) cognitive behavioral engagement negatively affected reduced academic achievement motivation; (7) learning engagement mediated the relationship between online game addiction and reduced academic achievement motivation.

According to the research results, when college students are addicted to online games, their learning engagement can be affected, which may decrease their behavioral, emotional, and cognitive engagement; their academic achievement motivation may be further reduced and affect their academic success or even prevent them from completing their studies. The mediating role of learning engagement between online game addiction and reduced academic achievement motivation indicates that reduced academic achievement motivation influenced by online game addiction could be prevented or weakened by enhancing learning engagement.

5.2. Suggestions

Universities and families play a crucial role in preventing online game addiction among college students. One of the main reasons college students play online games may be that they lack an understanding of other leisure methods and can only relieve their psychological pressure through online games ( Fan and Gai, 2022 ). Therefore, universities should enrich college students’ after-school leisure life and help them cultivate healthy hobbies and interests. Besides, a harmonious parent–child relationship positively affects children’s learning engagement ( Shao and Kang, 2022 ). Parents’ stricter demands may aggravate children’s game addiction ( Baturay and Toker, 2019 ). Therefore, parents should assume a proper perspective on the rationality of gaming and adopt the right approach to guide their children.

One key factor influencing the quality of higher education is students’ learning engagement. The integration of educational information technology has disrupted traditional teaching methods. This trend has accelerated in the context of COVID-19. College students’ growth mindset can impact their learning engagement through the role of the perceived COVID-19 event strength and perceived stress ( Zhao et al., 2021 ). Moreover, students’ self-regulated learning and social presence positively affect their learning engagement in online contexts ( Miao and Ma, 2022 ). Students’ liking of the teacher positively affects their learning engagement ( Lu et al., 2022 ). Their perceived teacher support also positively affects their learning engagement ( An et al., 2022 ). Hence, educators should focus on teacher support and care in the teaching and learning process.

Students’ motivation for academic achievement can often be influenced by active interventions. Cheng et al. (2022) noted that the cumulative process of students gaining successful experiences contributed to an increased sense of self-efficacy, motivating them to learn. Zhou (2009) illustrated that cooperative learning motivated students’ academic achievement. In addition, Hong J. C. et al. (2021) showed that poor parent–child relationships (such as the behavior of “mama’ s boy” in adults) had a negative impact on students’ academic achievement motivation, and they concluded that cell phone addiction was more pronounced among students with low academic achievement motivation. Hence, enhancing students’ academic achievement motivation also requires family support.

5.3. Research limitations and suggestions for future research

Most of the past studies on the impact of online game addiction on academics have used quantitative research as the research method. The qualitative research approach regarding students’ online game addiction should not be neglected. By collecting objective factual materials in the form of qualitative research such as interviews a greater understanding of students’ actual views on games and the psychological factors of addiction can be achieved. Therefore, future studies could introduce more qualitative research to study online game addiction.

To pay attention to the problem of students’ online game addiction, universities and families should not wait until they become addicted and try to remedy it, but should start to prevent it before it gets to that stage. In terms of developing students’ personal psychological qualities, students’ sensation-seeking and loneliness can significantly affect their tendency to become addicted to online games ( Batmaz and Çelik, 2021 ). Adolescents’ pain intolerance problems can also contribute to Internet overuse ( Gu, 2022 ). Emotion-regulation methods affect the emotional experience and play a vital role in Internet addiction ( Liang et al., 2021 ). In this regard, it is necessary to pay attention to students’ mental health status and to guide them to establish correct values and pursue goals through psychological guidance and other means.

In addition to individual factors, different parenting can considerably impact adolescents. Adolescents who tend to experience more developmental assets are less likely to develop IGD ( Xiang et al., 2022a ), and external resources can facilitate the development of internal resources, discouraging adolescents from engaging in IGD ( Xiang et al., 2022b ). Relevant research indicates that the most critical factor in adolescents’ game addiction tendency comes from society or their parents rather than being the adolescents’ fault ( Choi et al., 2018 ). Adolescents who tend to be addicted to online games may have discordant parent–child relationships ( Eliseeva and Krieger, 2021 ). Better father-child and mother–child relationships predict lower initial levels of Internet addiction in adolescents ( Shek et al., 2019 ). Family-based approaches such as improved parent–child relationships and increased communication and understanding among family members can be a direction for adolescent Internet addiction prevention ( Yu and Shek, 2013 ).

At the school level, a close teacher-student relationship is one of the main factors influencing students’ psychological state. Students’ participation in and control over the teaching and learning process as well as their closeness to teachers can increase their satisfaction and thus enhance their learning-related well-being ( Yang J. et al., 2021 ). More school resources can lead to higher adolescent self-control, attenuating students’ online gaming disorders ( Xiang et al., 2022c ).

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

R-QS, and J-HY: concept and design and drafting of the manuscript. R-QS, and J-HY: acquisition of data and statistical analysis. G-FS, and J-HY: critical revision of the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by Beijing Normal University First-Class Discipline Cultivation Project for Educational Science (Grant number: YLXKPY-XSDW202211). The Project Name is “Research on Theoretical Innovation and Institutional System of Promoting the Modernization of Vocational Education with Modern Chinese Characteristics”.

Conflict of interest

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

Publisher’s note

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Keywords: college students, online game addiction, learning engagement, reduced academic achievement motivation, online games

Citation: Sun R-Q, Sun G-F and Ye J-H (2023) The effects of online game addiction on reduced academic achievement motivation among Chinese college students: the mediating role of learning engagement. Front. Psychol . 14:1185353. doi: 10.3389/fpsyg.2023.1185353

Received: 13 March 2023; Accepted: 08 June 2023; Published: 13 July 2023.

Reviewed by:

Copyright © 2023 Sun, Sun and Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jian-Hong Ye, [email protected]

† These authors have contributed equally to this work and share first authorship

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Playing games: advancing research on online and mobile gaming consumption

Internet Research

ISSN : 1066-2243

Article publication date: 9 April 2019

Issue publication date: 9 April 2019

Seo, Y. , Dolan, R. and Buchanan-Oliver, M. (2019), "Playing games: advancing research on online and mobile gaming consumption", Internet Research , Vol. 29 No. 2, pp. 289-292. https://doi.org/10.1108/INTR-04-2019-542

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

Introduction

Computer games consistently generate more revenue than the movie and music industries and have become one of the most ubiquitous symbols of popular culture ( Takahashi, 2018 ). Recent technological developments are changing the ways in which consumers are able to engage with computer games as individuals – adult gamers, parents and children ( Christy and Kuncheva, 2018 ) – and as collectives, such as communities, networks and subcultures ( Hamari and Sjöblom, 2017 ; Seo, 2016 ). In particular, with the proliferation of online and mobile technologies, we have witnessed the emergence of newer forms of both computer games themselves (e.g. advertising games (advergames), virtual and augmented reality games and social media games) ( Rauschnabel et al. , 2017 ) and of gaming practices (e.g. serious gaming, hardcore gaming and eSports) ( Seo, 2016 ).

It is, therefore, not surprising that the issues concerning the ways computer games consumption is changing in light of these technological developments have received much attention across diverse disciplines of social sciences, such as marketing (e.g. Seo et al. , 2015 ), information systems (e.g. Liu et al. , 2013 ), media studies (e.g. Giddings, 2016 ) and internet research (e.g. Hamari and Sjöblom, 2017 ). The purpose of this introductory paper to the special issue “Online and mobile gaming” is to chart future research directions that are relevant to a rapidly changing postmodern digital gaming landscape. In this endeavor, this paper first provides an integrative summary of the six articles that comprise this special issue, and then draws the threads together in order to elicit the agenda for future research.

An integrative summary of the special issue

The six articles that were selected for this special issue advance research into online and mobile gaming in several ways. The opening article by Pappas, Mikalef, Giannakos and Kourouthanassis draws attention to the complex ecosystem of mobile applications in which multiple factors influence consumer behavior in mobile games. Pappas and his colleagues shed light on how price value, game content quality, positive and negative emotions, gender, and gameplay time interact with one another to predict the intention to download mobile games. This study offers useful insights by demonstrating how fuzzy set qualitative comparative analysis methodology can be applied to advance research into computer games consumption.

The study by Bae, Kim, Kim and Koo addresses the digital virtual consumption that occurs within computer games. This second paper explores the relationship between in-game items and mood management to determine the affective value of purchasing in-game items. The findings reveal that game users manage their levels of arousal and mood valence through the use of in-game purchases, suggesting that stressed users are more likely to purchase decorative items, whereas bored users tend to purchase functional items. This study offers an informative perspective of how mood management and selective exposure theories can be applied to understand the in-game purchases. Continuing this theme, the third study by Bae, Park and Koo investigates the effect of perceived corporate social responsibility (CSR) initiatives. Park and colleagues extend previous research by identifying important motivational mechanisms, such as self-esteem and compassion, which link CSR initiative perceptions with the intentions to purchase in-game items.

The fourth and fifth studies of this special issue draw our attention to the use of avatars and game characters. Liao, Cheng and Teng use social identity and flow theories to construct a novel model that explains how avatar attractiveness and customization impact loyalty among online game consumers. In the fifth study, Choi explores the importance of game character characteristics being congruent with product types in order to make advergames more persuasive.

The final study by Lee and Ko reviews the predictors of game addiction based on loneliness, motivation and inter-personal competence. The findings of these authors suggest that regulatory focus mediates the effect of loneliness on online game addiction, and that inter-personal competence significantly buffers the indirect effect of loneliness on online game addiction. This study advances our knowledge about online game addiction through an investigation of the important role played by loneliness.

Future directions for research

Taken together, our introductory commentary and the six empirical studies that make up this special issue deepen and broaden the current understanding of how online and mobile technologies augment the consumption of computer games. In this final section of our paper, we outline potential directions for future research.

First, this special issue highlights that computer games consumption is a diverse interdisciplinary phenomenon, where important issues range from establishing the factors that determine the adoption of particular computer games to what consumers do within these games; from whether computer games enhance consumer well-being (e.g. Howes et al. , 2017 ), to whether they engender addiction (e.g. Frölich et al. , 2016 ); and from establishing how computer gaming experiences are influenced by internal psychological mechanisms to querying the effects of broader social aspects of consumer lives on computer games consumption ( Kowert et al. , 2015 ). Informed by these findings, we assert that as computer games consumption becomes more complex and interactive, incorporating more technology brought about by the proliferation of online and mobile gaming, it is important that our theorizing follows by tracking the mutual imbrication of consumers, play, technology, culture, well-being and other salient issues.

Computer games consumption is a phenomenon of global significance, which is reflected by the international interest that we have received for this special issue. This prompts us to consider similarities and differences in the ways that computer games are consumed across cultures ( Elmezeny and Wimmer, 2018 ). Many computer games themselves now foster intercultural, multicultural and transcultural experiences ( Cruz et al. , 2018 ) by enabling consumers from different countries and regions to connect and build relationships within the shared virtual space. How do such experiences shape the consumption of computer games? This gap in the literature has been previously noted ( Seo et al. , 2015 ), but it has not been either sufficiently detailed or theorised. Future studies should explore the role of various transcultural experiences and practices within online and mobile games consumption.

Finally, one increasingly promising area for future research is the rise of virtual reality (VR) applications. Although the earliest references to VR date back to the 1990s (e.g. Gigante, 1993 ), it has been only recently that technological developments have allowed VR to evolve from a niche technology into an everyday phenomenon that is readily available to consumers ( Lamkin, 2017 ; Oleksy and Wnuk, 2017 ). Given that VR is an experientially distinct medium, how will it augment computer games consumption experiences and practices? Will it foster more diverse applications of computer games across various aspects of consumer lives (e.g. Tussyadiah et al. , 2018 ), or will it increase computer games addiction (e.g. Chou and Ting, 2003 )? What are the current and future intersections between VR technology, online and mobile games, and how are they likely to develop and affect consumers? We envision that these and many other questions related to the application and proliferation of VR technology in computer games consumption will be an exceptionally fruitful area for future research.

In summary, we hope that this paper and the special issue, with its emphasis on online and mobile gaming, will offer new insights for researchers and practitioners who are interested in the advancement of research on computer games consumption.

Chou , T.J. and Ting , C.C. ( 2003 ), “ The role of flow experience in cyber-game addiction ”, CyberPsychology and Behavior , Vol. 6 No. 6 , pp. 663 - 675 .

Christy , T. and Kuncheva , L.I. ( 2018 ), “ Technological advancements in affective gaming: a historical survey ”, GSTF Journal on Computing , Vol. 3 No. 4 , pp. 32 - 41 .

Cruz , A.G.B. , Seo , Y. and Buchanan-Oliver , M. ( 2018 ), “ Religion as a field of transcultural practices in multicultural marketplaces ”, Journal of Business Research , Vol. 91 , pp. 317 - 325 .

Elmezeny , A. and Wimmer , J. ( 2018 ), “ Games without frontiers: a framework for analyzing digital game cultures comparatively ”, Media and Communication , Vol. 6 No. 2 , pp. 80 - 89 .

Frölich , J. , Lehmkuhl , G. , Orawa , H. , Bromba , M. , Wolf , K. and Görtz-Dorten , A. ( 2016 ), “ Computer game misuse and addiction of adolescents in a clinically referred study sample ”, Computers in Human Behavior , Vol. 55 , pp. 9 - 15 .

Giddings , S. ( 2016 ), “ Pokémon Go as distributed imagination ”, Mobile Media and Communication , Vol. 5 No. 1 , pp. 59 - 62 .

Gigante , M.A. ( 1993 ), “ Virtual reality: definitions, history and applications ”, in Earnshaw , R.A. (Ed.), Virtual Reality Systems , Academic Press , New York, NY , pp. 3 - 14 .

Hamari , J. and Sjöblom , M. ( 2017 ), “ What is eSports and why do people watch it ”, Internet Research , Vol. 27 No. 2 , pp. 211 - 232 .

Howes , S.C. , Charles , D.K. , Marley , J. , Pedlow , K. and McDonough , S.M. ( 2017 ), “ Gaming for health: systematic review and meta-analysis of the physical and cognitive effects of active computer gaming in older adults ”, Physical Therapy , Vol. 97 No. 12 , pp. 1122 - 1137 .

Kowert , R. , Vogelgesang , J. , Festl , R. and Quandt , T. ( 2015 ), “ Psychosocial causes and consequences of online video game play ”, Computers in Human Behavior , Vol. 45 , pp. 51 - 58 .

Lamkin , P. ( 2017 ), “ Virtual reality headset sales hit 1 million ”, available at: www.forbes.com/sites/paullamkin/2017/11/30/virtual-reality-headset-sales-hit-1-million/#241697c42b61/ (accessed October 4, 2018 ).

Liu , D. , Li , X. and Santhanam , R. ( 2013 ), “ Digital games and beyond: what happens when players compete ”, MIS Quarterly , Vol. 37 No. 1 , pp. 111 - 124 .

Oleksy , T. and Wnuk , A. ( 2017 ), “ Catch them all and increase your place attachment! The role of location-based augmented reality games in changing people–place relations ”, Computers in Human Behavior , Vol. 76 , pp. 3 - 8 .

Rauschnabel , P.A. , Rossmann , A. and tom Dieck , M.C. ( 2017 ), “ An adoption framework for mobile augmented reality games: the case of Pokémon Go ”, Computers in Human Behavior , Vol. 76 , pp. 276 - 286 .

Seo , Y. ( 2016 ), “ Professionalized consumption and identity transformations in the field of eSports ”, Journal of Business Research , Vol. 69 No. 1 , pp. 264 - 272 .

Seo , Y. , Buchanan‐Oliver , M. and Fam , K.S. ( 2015 ), “ Advancing research on computer game consumption: a future research agenda ”, Journal of Consumer Behaviour , Vol. 14 No. 6 , pp. 353 - 356 .

Takahashi , D. ( 2018 ), “ Newzoo: games market expected to hit $180.1 billion in revenues in 2021 ”, available at: https://venturebeat.com/2018/04/30/newzoo-global-games-expected-to-hit-180-1-billion-in-revenues-2021/ (accessed October 4, 2018 ).

Tussyadiah , I.P. , Wang , D. , Jung , T.H. and tom Dieck , M.C. ( 2018 ), “ Virtual reality, presence and attitude change: empirical evidence from tourism ”, Tourism Management , Vol. 66 , pp. 140 - 154 .

Acknowledgements

The guest editors would like to offer special thanks to the Editor of Internet Research , Christy Cheung, for supporting the publication of this special issue. The guest editors would also like to thank all of the authors who contributed to this research for the “Online and mobile gaming” special issue. Finally, the guest editors gratefully acknowledge the contribution of reviewers, who generously spent their time in helping to review submissions: Luke Butcher, Curtin University, Australia; Hsiu-Hua Chang, Feng Chia University, Taiwan; I-Cheng Chang, National Dong Hwa University, Taiwan; Chi-Wen Chen, California State University, USA; Zifei Fay Chen, University of San Francisco, USA; Sujeong Choi, Chonnam National University, Korea; Diego Costa Pinto, New University of Lisbon, Portugal; Angela Cruz, Monash University, Australia; Robert Davis, Massey University, New Zealand; Julia Fehrer, University of Auckland, New Zealand; Tony Garry, University of Otago, New Zealand; Tracy Harwood, De Montfort University, UK; Mu Hu, Beihang University, China; Tseng-Lung Huang, Yuan Ze University, Taiwan; Kun-Huang Huang, Feng Chia University, Taiwan; Chelsea Hughes, Virginia Commonwealth University, USA; Euejung Hwang, Otago University, New Zealand; Sang-Uk Jung, Hankuk University of Foreign Studies, Korea; Kacy Kim, Bryant University, USA; Dong-Mo Koo, Kyungpook National University, Korea; Jun Bum Kwon, University of New South Wales, Australia; Chun-Chia Lee, National Chiao Tung University, Taiwan; Jacob Chaeho Lee, Ulsan National Institute of Science and Technology, Korea; Loic Li, University of Auckland, New Zealand; Marcel Martončik, University of Presov, Slovakia; Mike Molesworth, University of Reading, UK; Gavin Northey, University of Auckland, New Zealand; James Richard, Victoria University of Wellington, New Zealand; Ryan Rogers, University of Pennsylvania, USA; Felix Septianto, University of Auckland, New Zealand; Zhen Shao, Harbin Institute of Technology, China; Kai-Shuan Shen, Fo Guang University, Taiwan; Jungmin Son, Chungnam National University; Korea; Yang Sun, Zhejiang Sci-Tech University, China; Eva van Reijmersdal, University of Amsterdam, Netherlands; Ekant Veer, University of Canterbury, New Zealand; John Velez, Indiana University, USA; Wei-Tsong Wang, National Cheng Kung University, Taiwan; Ya-Ling Wu, Tamkang University, Taiwan; Sheau-Fen Yap, Auckland University of Technology, New Zealand; and Sukki Yoon, Bryant University, USA.

Corresponding author

About the authors.

Yuri Seo is Senior Lecturer at the University of Auckland of Business School, New Zealand. His research interests include digital technology and consumption, cultural branding and multicultural marketplaces.

Rebecca Dolan is Lecturer at the University of Adelaide School of Business, Australia. Her research focuses on understanding, facilitating and optimizing customer relationships, engagement, and online communication strategies. She has a specific interest in the role that digital and social media play in the modern marketing communications environment.

Margo Buchanan-Oliver is Professor in the Department of Marketing and the Co-Director of the Centre of Digital Enterprise (CODE) at the University of Auckland Business School. Her research concerns interdisciplinary consumption discourse and practice, particularly that occurring at the intersection of the digital and physical worlds.

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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism. Run a free check.

Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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THE CAUSES OF ONLINE GAMES ADDICTION

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Video-game playing is popular among college students. Cognitive and negative consequences have been studied frequently. However, little is known about the influence of gaming behavior on IT college students' academic performance. An increasing number of college students take online courses, use social network websites for social interactions, and play video games online. To analyze the relationship between college students' gaming behavior and their academic performance, a research model is proposed and a survey study is conducted. The study result of a multiple regression analysis shows that self-control capability, social interaction using face-to-face or phone communications, and playing video games using a personal computer make statistically significant contributions to the IT college students' academic performance measured by GPA.

Francis Jesoro

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This paper focuses on the impact of online games among Malaysian undergraduate students. The purpose of this study is to investigate whether online games (especially MMOGs) impacted students positively or vice versa; focusing on three elements (time management, social life and emotion). A total of 83 respondents comprised from 14 Malaysia universities, randomly selected undergraduate students who plays MMOGs (casual and hard core gamers – addiction to MMOGs) were involved in this study. The results showed that only students' capability in time management were negatively affected, meanwhile as for the elements social life and emotion, MMOGs do not affected them negatively.

Michele Zorrilla Cabeen

To answer the question “Are video games good or bad?,” three areas of video game research are looked at. First, support for the idea that violent video games increase aggression is explored followed by research contradicting the idea. Second, research concerning benefits of playing video games is explored. Third, research supporting the idea of video game addiction is explored followed by research contradicting the idea and cautioning against the addiction label. Following the exploration of research, the author provides recommendations taken from other scholarly articles in the case of individual concern and the possibility of negative effects of video game play.

Marielle San Pedro

This study entitled, “Online Gaming: Influence on the Social Behavior and Psychological Well-being”, discusses on the effects caused by the practice of online gaming to the students’ social and psychological behavior. It will center on the relationship of gaming engagement to the interpersonal social behavior of gamers and the influence it has to psychological well-being. The main purpose of this research is to determine how online gaming influences the social behavior and psychological well-being of students by measuring their perceived gaming engagement, social behavior and psychological well-being. In order to test the hypothesis, 230 grade 12 ENGTECH and ACCESS students from Baliuag University participated in this study. Descriptive-correlational design was used in this study with the help of SPSS statistics. To test the significance of input and output variables, Pearson r Correlation analysis and Independent Sample T-test were used. Based on the findings, online gaming has a positive relationship with social behavior. Online games can also be an avenue of socially interacting with other people. It was also found that playing online games can help improve one’s psychological well-being if done moderately.

Mark D Griffiths

Massively multiplayer online role-playing games (MMORPGs) are a popular form of entertainment used by millions of gamers worldwide. Potential problems relating to MMORPG play have emerged, particu- larly in relation to being addicted to playing in such virtual environments. In the present study, factors relating to online gaming addiction and motivations for playing in MMORPGs were examined to establish whether they were associated with addiction. A sample comprised 1167 gamers who were surveyed about their gaming motivations. Latent Class Analysis revealed seven classes of motivations for playing MMORPGs, which comprised: (1) novelty; (2) highly social and discovery-orientated; (3) aggressive, anti-social and non-curious; (4) highly social, competitive; (5) low intensity enjoyment; (6) discovery- orientated; and (7) social classes. Five classes of gaming addiction-related experiences were extracted including: (1) high risk of addiction, (2) time-affected, (3) intermediate risk of addiction, (4) emotional control, and (5) low risk of addiction classes. Gender was a significant predictor of intermediate risk of addiction and emotional control class membership. Membership of the high risk of addiction class was significantly predicted by belonging to a highly social and competitive class, a novelty class, or an aggres- sive, anti-social, and non-curious class. Implications of these findings for assessment and treatment of MMORPG addiction are discussed.

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Zaheer Hussain , GA Williams

Massively multiplayer online role-playing games (MMORPGs) are a popular form of entertainment used by millions of gamers worldwide. Potential problems relating to MMORPG play have emerged, particularly in relation to being addicted to playing in such virtual environments. In the present study, factors relating to online gaming addiction and motivations for playing in MMORPGs were examined to establish whether they were associated with addiction. A sample comprised 1,167 gamers who were surveyed about their gaming motivations. Latent Class Analysis revealed seven classes of motivations for playing MMORPGs, which comprised: (1) novelty; (2) highly social and discovery-orientated; (3) aggressive, anti-social and non-curious; (4) highly social, competitive; (5) low intensity enjoyment; (6) discovery-orientated; and (7) social classes. Five classes of gaming addiction-related experiences were extracted including: (1) high risk of addiction, (2) time-affected, (3) intermediate risk of addiction, (4) emotional control, and (5) low risk of addiction classes. Gender was a significant predictor of intermediate risk of addiction and emotional control class membership. Membership of the high risk of addiction class was significantly predicted by belonging to a highly social and competitive class, a novelty class, or an aggressive, anti-social, and non-curious class. Implications of these findings for assessment and treatment of MMORPG addiction are discussed.

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15 Hypothesis Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

A hypothesis is defined as a testable prediction , and is used primarily in scientific experiments as a potential or predicted outcome that scientists attempt to prove or disprove (Atkinson et al., 2021; Tan, 2022).

In my types of hypothesis article, I outlined 13 different hypotheses, including the directional hypothesis (which makes a prediction about an effect of a treatment will be positive or negative) and the associative hypothesis (which makes a prediction about the association between two variables).

This article will dive into some interesting examples of hypotheses and examine potential ways you might test each one.

Hypothesis Examples

1. “inadequate sleep decreases memory retention”.

Field: Psychology

Type: Causal Hypothesis A causal hypothesis explores the effect of one variable on another. This example posits that a lack of adequate sleep causes decreased memory retention. In other words, if you are not getting enough sleep, your ability to remember and recall information may suffer.

How to Test:

To test this hypothesis, you might devise an experiment whereby your participants are divided into two groups: one receives an average of 8 hours of sleep per night for a week, while the other gets less than the recommended sleep amount.

During this time, all participants would daily study and recall new, specific information. You’d then measure memory retention of this information for both groups using standard memory tests and compare the results.

Should the group with less sleep have statistically significant poorer memory scores, the hypothesis would be supported.

Ensuring the integrity of the experiment requires taking into account factors such as individual health differences, stress levels, and daily nutrition.

Relevant Study: Sleep loss, learning capacity and academic performance (Curcio, Ferrara & De Gennaro, 2006)

2. “Increase in Temperature Leads to Increase in Kinetic Energy”

Field: Physics

Type: Deductive Hypothesis The deductive hypothesis applies the logic of deductive reasoning – it moves from a general premise to a more specific conclusion. This specific hypothesis assumes that as temperature increases, the kinetic energy of particles also increases – that is, when you heat something up, its particles move around more rapidly.

This hypothesis could be examined by heating a gas in a controlled environment and capturing the movement of its particles as a function of temperature.

You’d gradually increase the temperature and measure the kinetic energy of the gas particles with each increment. If the kinetic energy consistently rises with the temperature, your hypothesis gets supporting evidence.

Variables such as pressure and volume of the gas would need to be held constant to ensure validity of results.

3. “Children Raised in Bilingual Homes Develop Better Cognitive Skills”

Field: Psychology/Linguistics

Type: Comparative Hypothesis The comparative hypothesis posits a difference between two or more groups based on certain variables. In this context, you might propose that children raised in bilingual homes have superior cognitive skills compared to those raised in monolingual homes.

Testing this hypothesis could involve identifying two groups of children: those raised in bilingual homes, and those raised in monolingual homes.

Cognitive skills in both groups would be evaluated using a standard cognitive ability test at different stages of development. The examination would be repeated over a significant time period for consistency.

If the group raised in bilingual homes persistently scores higher than the other, the hypothesis would thereby be supported.

The challenge for the researcher would be controlling for other variables that could impact cognitive development, such as socio-economic status, education level of parents, and parenting styles.

Relevant Study: The cognitive benefits of being bilingual (Marian & Shook, 2012)

4. “High-Fiber Diet Leads to Lower Incidences of Cardiovascular Diseases”

Field: Medicine/Nutrition

Type: Alternative Hypothesis The alternative hypothesis suggests an alternative to a null hypothesis. In this context, the implied null hypothesis could be that diet has no effect on cardiovascular health, which the alternative hypothesis contradicts by suggesting that a high-fiber diet leads to fewer instances of cardiovascular diseases.

To test this hypothesis, a longitudinal study could be conducted on two groups of participants; one adheres to a high-fiber diet, while the other follows a diet low in fiber.

After a fixed period, the cardiovascular health of participants in both groups could be analyzed and compared. If the group following a high-fiber diet has a lower number of recorded cases of cardiovascular diseases, it would provide evidence supporting the hypothesis.

Control measures should be implemented to exclude the influence of other lifestyle and genetic factors that contribute to cardiovascular health.

Relevant Study: Dietary fiber, inflammation, and cardiovascular disease (King, 2005)

5. “Gravity Influences the Directional Growth of Plants”

Field: Agronomy / Botany

Type: Explanatory Hypothesis An explanatory hypothesis attempts to explain a phenomenon. In this case, the hypothesis proposes that gravity affects how plants direct their growth – both above-ground (toward sunlight) and below-ground (towards water and other resources).

The testing could be conducted by growing plants in a rotating cylinder to create artificial gravity.

Observations on the direction of growth, over a specified period, can provide insights into the influencing factors. If plants consistently direct their growth in a manner that indicates the influence of gravitational pull, the hypothesis is substantiated.

It is crucial to ensure that other growth-influencing factors, such as light and water, are uniformly distributed so that only gravity influences the directional growth.

6. “The Implementation of Gamified Learning Improves Students’ Motivation”

Field: Education

Type: Relational Hypothesis The relational hypothesis describes the relation between two variables. Here, the hypothesis is that the implementation of gamified learning has a positive effect on the motivation of students.

To validate this proposition, two sets of classes could be compared: one that implements a learning approach with game-based elements, and another that follows a traditional learning approach.

The students’ motivation levels could be gauged by monitoring their engagement, performance, and feedback over a considerable timeframe.

If the students engaged in the gamified learning context present higher levels of motivation and achievement, the hypothesis would be supported.

Control measures ought to be put into place to account for individual differences, including prior knowledge and attitudes towards learning.

Relevant Study: Does educational gamification improve students’ motivation? (Chapman & Rich, 2018)

7. “Mathematics Anxiety Negatively Affects Performance”

Field: Educational Psychology

Type: Research Hypothesis The research hypothesis involves making a prediction that will be tested. In this case, the hypothesis proposes that a student’s anxiety about math can negatively influence their performance in math-related tasks.

To assess this hypothesis, researchers must first measure the mathematics anxiety levels of a sample of students using a validated instrument, such as the Mathematics Anxiety Rating Scale.

Then, the students’ performance in mathematics would be evaluated through standard testing. If there’s a negative correlation between the levels of math anxiety and math performance (meaning as anxiety increases, performance decreases), the hypothesis would be supported.

It would be crucial to control for relevant factors such as overall academic performance and previous mathematical achievement.

8. “Disruption of Natural Sleep Cycle Impairs Worker Productivity”

Field: Organizational Psychology

Type: Operational Hypothesis The operational hypothesis involves defining the variables in measurable terms. In this example, the hypothesis posits that disrupting the natural sleep cycle, for instance through shift work or irregular working hours, can lessen productivity among workers.

To test this hypothesis, you could collect data from workers who maintain regular working hours and those with irregular schedules.

Measuring productivity could involve examining the worker’s ability to complete tasks, the quality of their work, and their efficiency.

If workers with interrupted sleep cycles demonstrate lower productivity compared to those with regular sleep patterns, it would lend support to the hypothesis.

Consideration should be given to potential confounding variables such as job type, worker age, and overall health.

9. “Regular Physical Activity Reduces the Risk of Depression”

Field: Health Psychology

Type: Predictive Hypothesis A predictive hypothesis involves making a prediction about the outcome of a study based on the observed relationship between variables. In this case, it is hypothesized that individuals who engage in regular physical activity are less likely to suffer from depression.

Longitudinal studies would suit to test this hypothesis, tracking participants’ levels of physical activity and their mental health status over time.

The level of physical activity could be self-reported or monitored, while mental health status could be assessed using standard diagnostic tools or surveys.

If data analysis shows that participants maintaining regular physical activity have a lower incidence of depression, this would endorse the hypothesis.

However, care should be taken to control other lifestyle and behavioral factors that could intervene with the results.

Relevant Study: Regular physical exercise and its association with depression (Kim, 2022)

10. “Regular Meditation Enhances Emotional Stability”

Type: Empirical Hypothesis In the empirical hypothesis, predictions are based on amassed empirical evidence . This particular hypothesis theorizes that frequent meditation leads to improved emotional stability, resonating with numerous studies linking meditation to a variety of psychological benefits.

Earlier studies reported some correlations, but to test this hypothesis directly, you’d organize an experiment where one group meditates regularly over a set period while a control group doesn’t.

Both groups’ emotional stability levels would be measured at the start and end of the experiment using a validated emotional stability assessment.

If regular meditators display noticeable improvements in emotional stability compared to the control group, the hypothesis gains credit.

You’d have to ensure a similar emotional baseline for all participants at the start to avoid skewed results.

11. “Children Exposed to Reading at an Early Age Show Superior Academic Progress”

Type: Directional Hypothesis The directional hypothesis predicts the direction of an expected relationship between variables. Here, the hypothesis anticipates that early exposure to reading positively affects a child’s academic advancement.

A longitudinal study tracking children’s reading habits from an early age and their consequent academic performance could validate this hypothesis.

Parents could report their children’s exposure to reading at home, while standardized school exam results would provide a measure of academic achievement.

If the children exposed to early reading consistently perform better acadically, it gives weight to the hypothesis.

However, it would be important to control for variables that might impact academic performance, such as socioeconomic background, parental education level, and school quality.

12. “Adopting Energy-efficient Technologies Reduces Carbon Footprint of Industries”

Field: Environmental Science

Type: Descriptive Hypothesis A descriptive hypothesis predicts the existence of an association or pattern related to variables. In this scenario, the hypothesis suggests that industries adopting energy-efficient technologies will resultantly show a reduced carbon footprint.

Global industries making use of energy-efficient technologies could track their carbon emissions over time. At the same time, others not implementing such technologies continue their regular tracking.

After a defined time, the carbon emission data of both groups could be compared. If industries that adopted energy-efficient technologies demonstrate a notable reduction in their carbon footprints, the hypothesis would hold strong.

In the experiment, you would exclude variations brought by factors such as industry type, size, and location.

13. “Reduced Screen Time Improves Sleep Quality”

Type: Simple Hypothesis The simple hypothesis is a prediction about the relationship between two variables, excluding any other variables from consideration. This example posits that by reducing time spent on devices like smartphones and computers, an individual should experience improved sleep quality.

A sample group would need to reduce their daily screen time for a pre-determined period. Sleep quality before and after the reduction could be measured using self-report sleep diaries and objective measures like actigraphy, monitoring movement and wakefulness during sleep.

If the data shows that sleep quality improved post the screen time reduction, the hypothesis would be validated.

Other aspects affecting sleep quality, like caffeine intake, should be controlled during the experiment.

Relevant Study: Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep (Waller et al., 2021)

14. Engaging in Brain-Training Games Improves Cognitive Functioning in Elderly

Field: Gerontology

Type: Inductive Hypothesis Inductive hypotheses are based on observations leading to broader generalizations and theories. In this context, the hypothesis deduces from observed instances that engaging in brain-training games can help improve cognitive functioning in the elderly.

A longitudinal study could be conducted where an experimental group of elderly people partakes in regular brain-training games.

Their cognitive functioning could be assessed at the start of the study and at regular intervals using standard neuropsychological tests.

If the group engaging in brain-training games shows better cognitive functioning scores over time compared to a control group not playing these games, the hypothesis would be supported.

15. Farming Practices Influence Soil Erosion Rates

Type: Null Hypothesis A null hypothesis is a negative statement assuming no relationship or difference between variables. The hypothesis in this context asserts there’s no effect of different farming practices on the rates of soil erosion.

Comparing soil erosion rates in areas with different farming practices over a considerable timeframe could help test this hypothesis.

If, statistically, the farming practices do not lead to differences in soil erosion rates, the null hypothesis is accepted.

However, if marked variation appears, the null hypothesis is rejected, meaning farming practices do influence soil erosion rates. It would be crucial to control for external factors like weather, soil type, and natural vegetation.

The variety of hypotheses mentioned above underscores the diversity of research constructs inherent in different fields, each with its unique purpose and way of testing.

While researchers may develop hypotheses primarily as tools to define and narrow the focus of the study, these hypotheses also serve as valuable guiding forces for the data collection and analysis procedures, making the research process more efficient and direction-focused.

Hypotheses serve as a compass for any form of academic research. The diverse examples provided, from Psychology to Educational Studies, Environmental Science to Gerontology, clearly demonstrate how certain hypotheses suit specific fields more aptly than others.

It is important to underline that although these varied hypotheses differ in their structure and methods of testing, each endorses the fundamental value of empiricism in research. Evidence-based decision making remains at the heart of scholarly inquiry, regardless of the research field, thus aligning all hypotheses to the core purpose of scientific investigation.

Testing hypotheses is an essential part of the scientific method . By doing so, researchers can either confirm their predictions, giving further validity to an existing theory, or they might uncover new insights that could potentially shift the field’s understanding of a particular phenomenon. In either case, hypotheses serve as the stepping stones for scientific exploration and discovery.

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Waller, N. A., Zhang, N., Cocci, A. H., D’Agostino, C., Wesolek‐Greenson, S., Wheelock, K., … & Resnicow, K. (2021). Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep. Child: care, health and development, 47 (5), 618-626.

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