Effectiveness of educational video games in English vocabulary acquisition: One case in China classroom context

Abstract

Rote memory (RM) has become the primary method of learning vocabulary for decades in China. However, RM is tedious, leading to reduced motivation and concentration. In contrast, Educational Video Games (EVGs) are attractive and fun, which could be an alternative to RM. Although most studies have investigated EVGs’ effectiveness, empirical research in China’s classrooms is still scarce. Besides, the combination of EVGs and traditional classrooms is constrained by the school bell, English syllabus, hardware, etc. Consequently, their results cannot be directly applied to China’s environment. Therefore, our research compares the learning performance between RM and our Snake Game (SG) in pronunciation, spelling, and recognition. 30 junior high school students tried to remember 20 words through RM (the control group); after days, they managed to learn an additional 20 words presented through the SG (the experimental group). It was found that (1) the SG outperforms the RM in pronunciation; (2) the SG is as effective as the RM in recognition; and (3) although the RM is slightly better than the SG in spelling, the shortfall can be redeemed by the continued enjoyment and motivation of the SG. In summary, students are satisfied with the effectiveness and enjoyment of the SG.

Keywords:

• rote memory
• vocabulary
• educational video games
• effectiveness
• traditional classrooms

Reviewing editor:

• John Lee, Editor-in-Chief, Education University of Hong Kong, Hong Kong

Subjects:

• Computer Science (General)
• Bilingualism / ESL
• Study Skills
• Classroom Practice
• Curriculum Studies

Acknowledgement

The authors would like to thank the head of the junior high school who agreed with the conduction of the experiment at school. The authors would like to thank the 30 participants and the English teacher, without whom the experiment could not be conducted. Thanks, John R Woodward and Atm S Alam, who supported methodology, and statistical analysis. Massive thanks to Lei Liu, Sha Wang, John R Woodward, Atm S Alam, who provided valuable advice on the development of the SG.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the China Scholarship Council with the Queen Mary University of London under Grant 202006930007.

Notes on contributors

Jianshu Qiao

Jianshu Qiao is a candidate Ph.D. in the Game AI group at the Queen Mary University of London. My research focuses on developing a framework for the design of EVG that sustain students’ durable motivation and concentration in learning English, since a self-designed EVG gives more flexibility in research. This paper confirmed that lightweight EVG not only can integrate into classrooms but also has acceptable effectiveness and enjoyment in learning vocabulary. Based on this foundation, we plan to incorporate artificial intelligence to personalize learning experience based on individual students’ English literacy. Then, we will improve the entertainment of EVG and integrate more learning theories to maximize their learning potential. By sharing findings, we hope to provide a blueprint for other researchers to develop effective, engaging, and intelligent EVGs that align with the specific needs of their school infrastructures, schedules, English syllabus, and the computer literacy of students and teachers, etc.

John R. Woodward

Dr. John R. Woodward is Reader in Computer Science and is the Head of Department (Computer Science) at Loughborough University. He received the B.Sc. degree in theoretical physics, M.S. degree in cognitive science, and Ph.D. degree in artificial intelligence from the University of Birmingham, Birmingham. He has previously led the Operational Research group at Queen Mary, University of London (http://or.qmul.ac.uk/people.html) and prior to that lectured at the Universities of Stirling, Nottingham and Birmingham. He was with the European Organization for Nuclear Research (CERN), Switzerland, where he conducted research into particle physics, the Royal Air Force as an Environmental Noise Scientist, and Electronic Data Systems as a Systems Engineer. His research interests include Machine Learning, Operational Research, Optimization, Artificial Intelligence, Computational Intelligence And Automatically Design Algorithms.

Atm S. Alam

Dr. Atm S. Alam is currently an Assistant Professor at the Queen Mary University of London. He received the BSc degree in information and communication engineering from the University of Rajshahi, Bangladesh, the MSc degree in telecommunications and computer networks engineering from London South Bank University, and the Ph.D. degree in wireless communications from The Open University, UK. His research interests include the areas of intelligent wireless communications and networks, enhanced teaching and learning, gamification for creative teaching and learning, intelligent transport systems. He is also an Associate Fellow of Higher Education Academy. Previously, he worked on several European and U.K., funded projects as a Post-Doctoral Research Fellow.