Affiliation:
1. National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China
2. Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
3. School of Education, State University of New York at Oswego, Oswego, NY 13126, USA
Abstract
Traditional methods of student assessment (SA) include self-reported surveys, standardized tests, etc. These methods are widely regarded by researchers as inducing test anxiety. They also ignore students’ thinking processes and are not applicable to the assessment of higher-order skills. Digital game-based assessment (DGBA) is thought to address the shortcomings of traditional assessment methods. Given the advantages of DGBA, an increasing number of empirical studies are working to apply digital games for SA. However, there is a lack of any systematic review of DGBA studies. In particular, very little is known about the characteristics of the games, the content of the assessment, the methods of implementation, and the distribution of the results. This study examined the characteristics of DGBA studies, and the adopted games on SA in the past decade from different perspectives. A rigorous systematic review process was adopted in this study. First, the Web of Science (WOS) database was used to search the literature on DGBA published over the last decade. Then, 50 studies on SA were selected for subsequent analysis according to the inclusion and exclusion criteria. The results of this study found that DGBA has attracted the attention of researchers around the world. The participants of the DGBA studies were distributed across different educational levels, but the number of participants was small. Among all game genres, educational games were the most frequently used. Disciplinary knowledge is the most popular SA research content. Formative assessment modeling with process data and summative assessment using final scores were the most popular assessment methods. Correlation analysis was the most popular analysis method to verify the effectiveness of games on SA. However, many DGBA studies have reported unsatisfactory data analysis results. For the above findings, this study further discussed the reasons, as well as the meanings. In conclusion, this review showed the current status and gaps of DGBA in the SA application; directional references for future research of researchers and game designers are also provided.
Funder
National Natural Science Foundation of China
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference54 articles.
1. OECD (2019). PISA 2018 Assessment and Analytical Framework, OECD.
2. Tools for scaffolding students in a complex learning environment: What have we gained and what have we missed?;Puntambekar;Educ. Psychol.,2005
3. Classroom orchestration: Synthesis;Roschelle;Comput. Educ.,2013
4. DeepStealth: Game-Based Learning Stealth Assessment with Deep Neural Networks;Min;IEEE Trans. Learn. Technol.,2019
5. Zhu, S., Guo, Q., and Yang, H.H. (2022, January 10–13). Digital Game-Based Assessment on Student Evaluation: A Systematic Review. Proceedings of the International Conference on Blended Learning, Nagoya, Japan.