APPLICATION OF DATA ANALYTICS IN PREDICTING AT-RISK ND II STUDENTS IN THE COLLEGE OF ADMINISTRATION, MANAGEMENT AND TECHNOLOGY, POTISKUM, YOBE STATE, NIGERIA
Keywords:
Data Analytics,, Predictive Models, At Risk Students, Academic Performance, Early InterventionAbstract
This study examined the application of data analytics in predicting at-risk students in the College of Administration, Management and Technology (CAMTECH), Potiskum, Yobe State. The aim was to identify factors influencing students’ academic performance and to evaluate the effectiveness of predictive models in supporting timely academic interventions. The population comprised 1,248 ND II students, from which a sample of 295 students was selected using stratified random sampling. Data were collected through a structured questionnaire and secondary academic records, and analyzed using SPSS version 27.0. Predictive analysis was conducted using regression and classification techniques. Findings revealed that factors such as class attendance, timely submission of assignments, study habits, and participation in group study significantly influence academic performance. The study further showed that predictive models are effective in identifying at risk students and supporting early interventions, though some factors, such as ICT usage and predictive modeling of personality traits, were less impactful. Based on these findings, the study recommends the adoption of data-driven academic monitoring, enhancement of ICT skills among students, and implementation of targeted academic support programs.References
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