Using Deep Learning Techniques for the Classification of Slow and Fast Learners

Cognitive learning strategies are focused on the improvement of the learner’s ability to analyze information in a deeper manner, 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 earners based on their past performance. This technique is taking 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.  Deep learning classifier with Multi-Layer 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. This upload consists of the code and the dataset used for the above-mentioned research.