Abstract
Student modeling approaches are important to identify students' needs, learning styles, and to monitor their improvements for individual modules. Lecturers may incorrectly identify the students' needs and learning styles based on solely an exam grade or performance in the class. In doing so, students need to be classified using more parameters such as e-learning activities, attendance to virtual live class (for theory and practice) and submission time of the assignment, etc. This study proposes a novel color-labeled student modeling/classification approach using e-learning activities to identify students' learning styles and to monitor students' weekly improvements for individual modules. A novel Student Classification Rate (SCR) formula was created by combining three stages including pre-study stage, virtual_class stage, and virtual_LAB_class stage. In the evaluation part of the SCR, Artificial Neural Network and Random Forest algorithms were employed based on two different feature sets for an Object-Oriented Programming Module. Feature set 1 consisted of a combination of e-learning and regular data while the feature set 2 was referred as the combination of the SCR and the regular data. Random Forest yielded the lowest MAE (0.7) by using feature set 2. Also, the majority of the students' (81%) learning styles referred to attending the live virtual class. Students' weekly learning progress was also monitored successfully since the Pearson correlation was measured as 0.78 with the 95% confidence interval between the mean of SCR and lab grades. Additionally, SCR used for two more different modules yielded convincing results in the determination of students' learning styles. The obtained results reveal that the proposed SCR approach has significant potential to correctly classify students, identify students' learning styles, and help the lecturer to monitor the students' weekly progress. Finally, it seems that SCR would have a significant impact on improvement of students learning.