An Enhanced Student Engagement and Academic Performance Predictive System

Student engagement is one of the important constructs that is used to understand the behavior of the student towards the teaching-learning process, and it determines the students’ academic performance. The aim of this study is for predictive analytics to work on the comprehension of student commitment and scholarly execution and anticipate students who are in danger of low execution or commitment right on time before the evaluation to work with conceivable mediation to further develop the learning results in advanced education. This research adopts the process of machine learning such as linear regression, decision tree, naïve Bayes, KNN, Kmeans in order to identify the most effective determinants for student academic performance prediction. The result of this study shows that after testing the five attributes, we discovered so far that the attributes that has impact on student evaluation are their Race/Ethnicity and Parental level of Education. Thus, the early prediction of student performance can trigger educators to track student dropouts in a particular course at an early stage. The model can also be used as an early warning system to identify failure students in the classroom by the course coordinators and educators, to take strategic decisions to improve student performance.