10.35940/ijrte.C4417.099320
https://zenodo.org/records/5843168
oai:zenodo.org:5843168
Palwinder Kaur Mangat
Palwinder Kaur Mangat
Assistant Professor, Department of Computer Science, National College for Women, Machhiwara, Punjab, India.
Kamaljit Singh Saini
Kamaljit Singh Saini
Assistant Professor, Department of Computer Science, National College for Women, Machhiwara, Punjab, India.
Predictive Analytics for Students" Performance Prediction
Zenodo
2020
Predictive Learning Analytics, Intelligent Tutoring Systems, Student Risk Prediction, Risk Prediction Systems, EDM, Early Warning Systems (EWS).
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Publisher
2020-09-30
eng
2277-3878
Creative Commons Attribution 4.0 International
Personalized learning is being popular due to digitizations that enable a large number of technologies to support it. To predict students’ learning abilities, it is necessary to estimate their behavior to know about their weaknesses and strengths. If it is possible for teachers to predict in advance atrisk and dropout students, they can plan more effectively to handle them. We are describing in this paper various intelligent tutoring systems with Educational Data Mining, Predictive Learning Analytics, prediction of at-risk students at an earlier basis, how this prediction task is done. We are describing various prediction models that can be used to predict students’ behavior and how portable these predictive models are and the various risk prediction systems that are being used.