Data Mining Approach For Predicting Student Performance
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Cited by:
- Bilal Zorić, Alisa, 2019. "Predicting Students’ Success Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, pages 58-66, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
- Deepti Aggarwal & Sonu Mittal & Vikram Bali, 2021. "Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(3), pages 38-49, July.
- Alisa Bilal Zorić, 2020. "Predicting Students’ Academic Performance Based on Enrolment Data," International Journal of Innovation and Economic Development, Inovatus Services Ltd., vol. 6(4), pages 54-61, October.
- Anjeela Jokhan & Aneesh A. Chand & Vineet Singh & Kabir A. Mamun, 2022. "Increased Digital Resource Consumption in Higher Educational Institutions and the Artificial Intelligence Role in Informing Decisions Related to Student Performance," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
- January D. Febro & Jocelyn Barbosa, 2017. "Mining student at risk in higher education using predictive models," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 3(4), pages 117-132.
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Keywords
data mining; classification; prediction; student succes; higher education;All these keywords.
JEL classification:
- L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
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