Unleashing the Power of Predictive Analytics to Identify At-Risk Students in Computer Science
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Human-Computer Interaction,Education,Mathematics (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s10758-023-09674-6.pdf
Reference44 articles.
1. Akçapınar, G., Altun, A., & Aşkar, P. (2019a). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1), 1–20.
2. Akçapınar, G., Hasnine, M. N., Majumdar, R., Flanagan, B., & Ogata, H. (2019b). Developing an early-warning system for spotting at-risk students by using eBook interaction logs. Smart Learning Environments, 6(1), 4.
3. Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49.
4. Arnold, K. E., & Pistilli, M. D. (2012). April. Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267–270).
5. Ayán, M. N. R., & García, M. T. C. (2008). Prediction of university students’ academic achievement by linear and logistic models. The Spanish journal of psychology, 11(1), 275–288.
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Predicting Academic Success in Large Online Courses at a Mega ODL University;Technology, Knowledge and Learning;2024-07-09
2. Development of an Early Warning System to Support Educational Planning Process by Identifying At-Risk Students;IEEE Access;2024
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