Predicting the individual effects of team competition on college students’ academic performance in mobile edge computing

• Published in: Journal of cloud computing : advances, systems and applications Vol. 13; no. 1; pp. 38 - 11
• Main Authors: Zhang, Huiling, Wu, Huatao, Li, Zhengde, Gong, Wenwen, Yan, Yan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Academic achievement; Classrooms; Colleges & universities; Competition; Computer Communication Networks; Computer Science; Computer System Implementation; Computer Systems Organization and Communication Networks; Edge computing; Error analysis; Error reduction; Field experiment; Individual treatment effect; Information Systems Applications (incl.Internet); Learning; Machine learning; Mobile computing; Mobile Edge Computing Meets AI; Prediction; Prediction models; Software Engineering/Programming and Operating Systems; Special Purpose and Application-Based Systems; Students; Team competition; Teams; Field experiment; Team competition; Prediction; Individual treatment effect
• ISSN: 2192-113X; 2192-113X
• DOI: 10.1186/s13677-024-00591-2

Mobile edge computing (MEC) has revolutionized the way of teaching in universities. It enables more interactive and immersive experiences in the classroom, enhancing student engagement and learning outcomes. As an incentive mechanism based on social identity and contest theories, team competition has been adopted and shown its effectiveness in improving students’ participation and motivation in college classrooms. However, despite its potential benefit, there are still many unresolved issues: What type of students and teams benefit more from team competition? In what teaching context is team competition more effective? Which competition design methods better increase student academic performance? Mobile edge computing provides the ability to obtain the data of the teaching process and analyze the causal effect between team competition and students’ academic performance. In this paper, the authors first design a randomized field experiment among freshmen enrolled in college English courses. Then, the authors analyze the observation data collected from the online teaching platform, and predict individual treatment effects of academic performance in college English through linear and nonlinear machine learning models. Finally, by carefully investigating features of teams and individual students, the prediction error is reduced by up to 30%. In addition, through interpreting the predictive models, some valuable insights regarding the practice of team competition in college classrooms are discovered.