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Predictive Model Using a Machine Learning Approach for Enhancing the Retention Rate of Students At-Risk

Predictive Model Using a Machine Learning Approach for Enhancing the Retention Rate of Students At-Risk

Hani Sami Brdesee, Wafaa Alsaggaf, Naif Aljohani, Saeed-Ul Hassan
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 21
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.299859
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MLA

Brdesee, Hani Sami, et al. "Predictive Model Using a Machine Learning Approach for Enhancing the Retention Rate of Students At-Risk." IJSWIS vol.18, no.1 2022: pp.1-21. http://doi.org/10.4018/IJSWIS.299859

APA

Brdesee, H. S., Alsaggaf, W., Aljohani, N., & Hassan, S. (2022). Predictive Model Using a Machine Learning Approach for Enhancing the Retention Rate of Students At-Risk. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-21. http://doi.org/10.4018/IJSWIS.299859

Chicago

Brdesee, Hani Sami, et al. "Predictive Model Using a Machine Learning Approach for Enhancing the Retention Rate of Students At-Risk," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-21. http://doi.org/10.4018/IJSWIS.299859

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Abstract

Student retention is a widely recognized challenge in the educational community to assist the institutes in the formation of appropriate and effective pedagogical interventions. This study intends to predict the students at-risk of low performances during an on-going course, those at-risk of graduating late than the tentative timeline and predicting the capacity of students in a campus. The data constitutes of demographics, learning, academic and educational related attributes which are suitable to deploy various machine learning algorithms for the prediction of at-risk students. For class balancing, Synthetic Minority Over Sampling Technique, is also applied to eliminate the imbalance in the academic award-gap performances and late/timely graduates. Results reveal the effectiveness of the deployed techniques with Long short-term Memory (LSTM) outperforming other models for early prediction of at-risk students. The main contribution of this work is a machine learning approach capable of enhancing the academic decision making related to student performance.