PERCEPTION OF LECTURERS AND STUDENTS ON THE USE OF MACHINE LEARNING-BASED EARLY WARNING SYSTEMS FOR IDENTIFYING AT-RISK STUDENTS IN GOMBE STATE COLLEGE OF EDUCATION AND LEGAL STUDIES, NAFADA
Keywords:
Intelligent early warning system, machine learning, at-risk students, academic performance, retention, higher educationAbstract
This study investigated the effect of an intelligent early warning system on at-risk students using machine learning techniques at Gombe State College of Education and Legal Studies, Nafada. A Research and Development (R&D) design combined with a quasi-experimental pretest–post-test control group design was adopted. The population of the study comprised all registered NCE and undergraduate students in the Departments of Mathematics/Computer Science and Legal Studies totalling 429 students. Using the Krejcie and Morgan sample size determination table, a sample of 152 participants was selected. This comprised 141 at-risk students, with 71 assigned to the experimental group and 70 to the control group and all 11 lecturers and academic advisers who were purposively selected to provide expert input and support the implementation of the intervention. The findings revealed that an intelligent early warning system can be effectively developed using machine learning techniques to identify at-risk students (grand mean = 3.64). The study further showed that the system had a positive effect on the early identification and intervention of at-risk students (grand mean = 3.69) and significantly improved students’ academic performance and retention (grand mean = 3.69). The hypothesis tests indicated statistically significant effects on early identification and intervention, t(139) = 4.86, p < .001; academic performance, t(139) = 5.42, p < .001; and retention, t(139) = 5.87, p < .001. The study concluded that intelligent early warning systems based on machine learning techniques are effective tools for identifying academically vulnerable students and enhancing their academic performance and retention. It was recommended that Gombe State College of Education and Legal Studies, Nafada, should adopt and institutionalize the system, train relevant staff on its use, and integrate it into the college’s academic monitoring and student support framework.References
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