A Systematic Review of AI-Driven Educational Assessment in STEM Education

Author:

Ouyang FanORCID,Dinh Tuan Anh,Xu Weiqi

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Energy Engineering and Power Technology,Fuel Technology

Reference55 articles.

1. *Aiouni, R., Bey, A., & Bensebaa, T. (2016). An automated assessment tool of flowchart programs in introductory programming course using graph matching. Journal of e-Learning and Knowledge Society, 12(2). https://www.learntechlib.org/p/173461/

2. Aljuaid, L. Z., & Said, M. Y. (2021). Deep learning-based method for prediction of software engineering project teamwork assessment in higher education. Journal of Theoretical and Applied Information Technology, 99(9), 2012–2030. http://www.jatit.org/volumes/Vol99No9/8Vol99No9.pdf

3. *Arfah Baharudin, S., & Lajis, A. (2021). Deep learning approach for cognitive competency assessment in computer programming subject. International Journal of Electrical and Computer Engineering Systems, 1, 51–57. https://hrcak.srce.hr/266730

4. *Ariely, M., Nazaretsky, T., & Alexandron, G. (2023). Machine learning and hebrew NLP for automated assessment of open-ended questions in biology. International Journal of Artificial Intelligence in Education, 33(1), 1–34. https://doi.org/10.1007/s40593-021-00283-x

5. Bertolini, R., Finch, S. J., & Nehm, R. H. (2023). An application of Bayesian inference to examine student retention and attrition in the STEM classroom. Frontiers in Education, 8, 1073829. https://doi.org/10.3389/feduc.2023.1073829

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