Students’ knowledge assessment using the ensemble methods

Abstract

Modeling students’ learning behavior for knowledge assessment is crucial for predicting the academic performance of students. This becomes a challenge in case of online teaching and learning scenarios since the students and teachers may not be physically present at the same geographical location in contrast to physical classroom teaching. Modeling of students’ learning patterns can lead to the proper prediction of students’ academic performance; thus, early identification of students at risk of academic failure is possible. The correct assessment of student knowledge can lead to corrective measures for students and fruitful feedback for instructors. In this paper, we have used the ensemble classifiers with various machine learning algorithms on the students’ knowledge dataset to predict the level of knowledge acquired by the students. This combination of the algorithms achieved better performance as compared to an individual algorithm. It was found that taking the classifiers which are independent and have the diversity of opinion leads to improved results.