Utilizing Social Clustering-Based Regression Model for Predicting Student's GPA

Abstract

The importance of e-learning has exceeded expectations over the past decade. Accordingly, several systems have been developed in completing intelligent assistive tools where students' behavior can be tracked and followed with suitable recommendations to enhance students' performance. This paper has two main objectives. First, the Community of Inquiry framework (CoI) is utilized as one of the most prominent student behavioral modeling to select features that best represent the students. According to experts' annotation, this study filters students' measured attributes from the StudentLife dataset to the CoI model, focusing on social presence. Second, the research looks at improving the accuracy and runtime of the Grade Point Average (GPA) prediction by introducing a hybrid model that combines combining k-means clustering phase based on student similarity with regression-based prediction. The clustering was performed on both static and Spatio-temporal (spatial time -series) students' attributes. Results show that LassoCV outperforms other regression techniques such as Standard Linear, Lasso, and Ridge Regression with an RMSE averaged around 0.15 and an average Adjusted R2 of 0.935 overall trials. Selecting the features according to the CoI reduces the number of features by 62.8%. Time-series clustering on its own was not beneficial; however, when conducted with the selection phase, it raised the quality of the model achieved by 2-3%.