Beyond the Scoreboard: A Machine Learning Investigation of Online Games’ Influence on Jordanian University Students’ Grades

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
M. Alshraideh, Abeer Abdel-Jabbar Abu-Zayed, Martin Leiner, Iyad Muhsen AlDajani
{"title":"Beyond the Scoreboard: A Machine Learning Investigation of Online Games’ Influence on Jordanian University Students’ Grades","authors":"M. Alshraideh, Abeer Abdel-Jabbar Abu-Zayed, Martin Leiner, Iyad Muhsen AlDajani","doi":"10.1155/2024/1337725","DOIUrl":null,"url":null,"abstract":"In the latter part of the 21st century, the prevalence of online games has significantly increased, encompassing titles connected to the Internet via smart devices, enabling multiplayer interaction. Recent media attention has shed light on the adverse effects associated with online gaming. This research paper explores the viewpoints of 4,700 university students in Jordan regarding the physical, psychological, and behavioural impacts of Internet games. Additionally, it predicts how these impacts may affect the academic performance of 1,410 students. To analyze student trends and forecast outcomes based on sustained game engagement, a convolutional neural network (CNN) was specifically developed for the neural network. The findings revealed student consensus with recommended university measures to limit online game usage, emphasizing a prevalent belief in the negative influence of games on the body, behaviour, and mental health. In terms of the prediction process, the training data encompassed 60%, 70%, and 80% of the dataset. The results revealed that the highest accuracy, 96.69%, was achieved at the 70% threshold for predicting students’ grade point average (GPA). The analysis suggested that projecting a decrease in the percentage of hours dedicated to playing online games could act as a mitigating factor to prevent GPA decline. Consequently, the system advises a range from 99.9% to 4.1%. This implies that a student with a maximum of 99.9% is encouraged to significantly reduce playing hours to preserve their GPA, while a student with a minimum of 4.1% is recommended to decrease playing hours by 4.1%. On average, for the 1,090 students, the system proposes a 48.36% reduction in playing hours to safeguard their GPAs and mitigate potential risks. This high level of accuracy played a crucial role in forecasting students’ GPA outcomes following a year of sustained daily engagement with online games. Notably, the results unveiled a concerning revelation that 80% of students would face a detrimental impact on their academic performance after one year of such consistent online game involvement.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computational Intelligence and Soft Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2024/1337725","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

In the latter part of the 21st century, the prevalence of online games has significantly increased, encompassing titles connected to the Internet via smart devices, enabling multiplayer interaction. Recent media attention has shed light on the adverse effects associated with online gaming. This research paper explores the viewpoints of 4,700 university students in Jordan regarding the physical, psychological, and behavioural impacts of Internet games. Additionally, it predicts how these impacts may affect the academic performance of 1,410 students. To analyze student trends and forecast outcomes based on sustained game engagement, a convolutional neural network (CNN) was specifically developed for the neural network. The findings revealed student consensus with recommended university measures to limit online game usage, emphasizing a prevalent belief in the negative influence of games on the body, behaviour, and mental health. In terms of the prediction process, the training data encompassed 60%, 70%, and 80% of the dataset. The results revealed that the highest accuracy, 96.69%, was achieved at the 70% threshold for predicting students’ grade point average (GPA). The analysis suggested that projecting a decrease in the percentage of hours dedicated to playing online games could act as a mitigating factor to prevent GPA decline. Consequently, the system advises a range from 99.9% to 4.1%. This implies that a student with a maximum of 99.9% is encouraged to significantly reduce playing hours to preserve their GPA, while a student with a minimum of 4.1% is recommended to decrease playing hours by 4.1%. On average, for the 1,090 students, the system proposes a 48.36% reduction in playing hours to safeguard their GPAs and mitigate potential risks. This high level of accuracy played a crucial role in forecasting students’ GPA outcomes following a year of sustained daily engagement with online games. Notably, the results unveiled a concerning revelation that 80% of students would face a detrimental impact on their academic performance after one year of such consistent online game involvement.
超越记分牌:网络游戏对约旦大学生成绩影响的机器学习研究
在 21 世纪后半叶,网络游戏的普及率大幅提高,包括通过智能设备连接到互联网、实现多人互动的游戏。最近,媒体关注了与网络游戏相关的负面影响。本研究论文探讨了约旦 4,700 名大学生对网络游戏的生理、心理和行为影响的看法。此外,本文还预测了这些影响可能会如何影响 1410 名学生的学习成绩。为了分析学生的趋势并根据持续参与游戏的情况预测结果,专门为神经网络开发了一个卷积神经网络(CNN)。研究结果表明,学生对学校建议的限制网络游戏使用的措施达成了共识,强调了游戏对身体、行为和心理健康的负面影响的普遍看法。在预测过程中,训练数据分别占数据集的 60%、70% 和 80%。结果显示,在预测学生平均学分绩点(GPA)时,70%的临界值准确率最高,达到96.69%。分析表明,减少玩网络游戏的时间比例可以作为防止 GPA 下降的缓解因素。因此,系统建议的范围为 99.9% 至 4.1%。这意味着,鼓励最高达 99.9% 的学生大幅减少游戏时间,以保持其 GPA,而建议最低达 4.1% 的学生减少 4.1% 的游戏时间。平均而言,在 1090 名学生中,系统建议减少 48.36% 的游戏时间,以保障他们的 GPA 并降低潜在风险。这种高度的准确性在预测学生在每天持续参与网络游戏一年后的 GPA 结果方面发挥了至关重要的作用。值得注意的是,结果揭示了一个令人担忧的问题,即 80% 的学生在持续参与网络游戏一年后,其学业成绩将受到不利影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Applied Computational Intelligence and Soft Computing
Applied Computational Intelligence and Soft Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
6.10
自引率
3.40%
发文量
59
审稿时长
21 weeks
期刊介绍: Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信