Predictive Analytics for Students" Performance Prediction
- 1. Assistant Professor, Department of Computer Science, National College for Women, Machhiwara, Punjab, India.
Contributors
- 1. Publisher
Description
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.
Files
C4417099320.pdf
Files
(646.8 kB)
Name | Size | Download all |
---|---|---|
md5:e0dfa26e8dcbafed9f52022478031a88
|
646.8 kB | Preview Download |
Additional details
Related works
- Is cited by
- Journal article: 2277-3878 (ISSN)
Subjects
- ISSN
- 2277-3878
- Retrieval Number
- 100.1/ijrte.C4417099320