Classification of Slow and Fast Learners Using Deep Learning Model

Abstract

Cognitive learning strategies are focused on the improvement of the learner’s ability to analyze information more deeply, efficiently handle new situations by transferring and applying the knowledge. These techniques result in enhanced and better-retained learning. To cater to the needs of different students having different levels of cognitive learning, it is very important to assess their learning ability. In this paper, a method based on deep learning is presented to classify the earners based on their past performance. This technique is taking the student’s 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. A final accuracy of 90% is achieved in the classification of the learners in their cognitive learning level.