Predictive Analytics for Students‟ Performance Prediction
Palwinder Kaur Mangat1, Kamaljit Singh Saini2
1Palwinder Kaur Mangat, Assistant Professor, Department of Computer Science, National College for Women, Machhiwara, Punjab, India.
2Kamaljit Singh Saini, Assistant Professor, Department of Computer Science, National College for Women, Machhiwara, Punjab, India.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 300-305 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4417099320 | DOI: 10.35940/ijrte.C4417.099320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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 at-risk 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.
Keywords: Predictive Learning Analytics, Intelligent Tutoring Systems, Student Risk Prediction, Risk Prediction Systems, EDM, Early Warning Systems (EWS).