Classification of Slow and Fast Learners Using a Deep Learning Model

Abstract

Cognitive learning strategies are focused on the improvement of the learner’s ability to analyze information in a deeper manner and efficiently handle new situations by transferring and applying the knowledge. These techniques result in enhanced and better- retained learning. In order to cater to the needs of different students having different levels of cognitive learning, it’s very important to assess their learning ability. In this paper, a method based on deep learning is presented to classify the learners based on their past performance. This technique takes the students’ past semester marks, their total failures in subjects/passing heads, and their current semester attendance. The proposed method classifies the learners into three categories, namely slow, fast, and average learners. A deep learning classifier with multilayer perceptron–based nodes is built for the classification. The proposed method is fully automatic and robust. The final accuracy of 90% is achieved in the classification of the learners in their cognitive learning level.