effects of online games thesis sample

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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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Authors and affiliations.

Centre for Health Research and School of Commerce, University of Southern Queensland, Workstation 15, Room T450, Block T, Toowoomba, QLD, 4350, Australia

Md Irteja Islam & Rasheda Khanam

Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Mohakhali, Dhaka, 1212, Bangladesh

Md Irteja Islam

Transport and Road Safety (TARS) Research Centre, School of Aviation, University of New South Wales, Sydney, NSW, 2052, Australia

Raaj Kishore Biswas

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M.I.I.: Methodology, Validation, Visualization, Investigation, Writing—Original draft preparation, Writing—Reviewing and Editing. R.K.B.: Methodology, Software, Data curation, Formal Analysis, Writing—Original draft preparation. R.K.: Conceptualization, Methodology, Supervision, Writing- Reviewing and Editing.

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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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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

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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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

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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The Effects of Online Games Towards The Academic Performance Of Selected Information Technology Students In Partial Requirement for The Completion in The Course Methods of Research

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Video games can have a positive impact on children—if designed with the right features, says new study

New research finds that digital games can contribute to the well-being of children, but game producers must design games to support positive outcomes.

Video games can contribute to and support the well-being of children if they are designed with the needs of children in mind, according to new research from UNICEF Innocenti and collaborating institutions, including New York University.

The report, the second in the Responsible Innovation in Technology for Children (RITEC) project, establishes that digital gaming companies and game designers can and should support the well-being of children through the games they produce and demonstrates that digital play has a particularly positive impact on children’s autonomy, competence, creativity, and identity when it responds to their deep interests, needs and desires. The project was co-founded by UNICEF and the LEGO Group and is funded by the LEGO Foundation.

NYU Steinhardt Professor Jan L. Plass led one of three studies appearing in the report. To understand the effect of digital play on children’s well-being, Plass and his colleagues implemented a 10-week intervention involving digital games including Lego Builder’s Journey and Rocket League with 255 children ages 8 through 12 years old. Among their findings, US children with a greater need for belonging relayed more positive feelings toward their social and parental relationships and an improved sense of autonomy. In Chile, children reported greater autonomy and improved parental relationships. In South Africa, the digital play generally supported well-being for all children.

“Many parents are concerned about screen time for their children, and this is the first study investigating the effect of digital play on well-being for this age group in the US, Chile, and South Africa,” said Plass, founding director of the Consortium for Research and Evaluation of Advanced Technology in Education (CREATE) Lab at NYU.

In addition to the experimental research led by Plass, observational research was conducted in the homes of 50 families over a 14-month period with children ages 6 through 12 in Australia, Cyprus, South Africa, and the UK. Lab-based research measured heart rate, eye tracking, facial expressions, and galvanic skin response (changes in sweat gland activity) of 69 children, ages 7 though 12, playing digital games in Australia.

The three studies found that games can support children’s senses of autonomy, competence, creativity, and identity, as well as help them regulate emotions and build relationships. But in order to support one or more of these aspects of well-being, games should consider certain features. For example, to support children’s sense of autonomy, a game could put them in control, allow them to make decisions about gameplay and encourage them to develop their own strategies to progress. Or to support creativity, a game could allow children to freely explore and solve problems or create their own characters or narratives.

“For decades, people have often assumed that playing video games is somehow bad for children, undermining their well-being. But our new study paints a far more complex picture—one in which these games can actually contribute to children’s well-being and positively support them as they grow up,” said Bo Viktor Nylund, director of UNICEF Innocenti.

Anna Rafferty, vice president of Digital Consumer Engagement, the LEGO Group, said: “This exciting research from UNICEF and leading academics shows that safe and inclusive digital play can have a profoundly positive impact on children’s lives. We’re proud to be partnering with like-minded organisations to understand how digital experiences can be designed in a way that puts children’s well-being first. These findings will empower responsible businesses to create a digital future where children are safe, nurtured and equipped to thrive.”

“The finding that digital play can enhance child well-being and meet children’s psychological needs, such as the need for connections to others, is especially important at a time when we are concerned about children’s mental health,” added Plass.

Said Nylund: “This research helps us understand not only how video games can impact the well-being of children, but also helps the producers and designers of these games understand what elements they can include to support children. We hope they will consider these findings as they design the games our children will be playing in the future.”

Also included in the RITEC project is the Joan Ganz Cooney Center; the Young & Resilient Research Centre at Western Sydney University; the Graduate Center, City University of New York; the University of Sheffield; and the Australian Centre of Excellence for the Digital Child.

The findings will be followed later this year by the launch of a guide to assist businesses to incorporate these findings into the games they design.

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