Student Psychology Teaching Learning Optimisation-Based Deep Long Short-Term Memory for Predicting Student Performance

Abstract

Knowledge Tracing (KT) represents an analysis of the state in terms of knowledge among students to predict if the student can answer a problem based on test results. Generally, a human teacher tracks the knowledge of students and customises the teaching based on the needs of the students. Nowadays, the rise of online education platforms leads to the development of machines for tracking the knowledge of students and improving their learning experience. The accuracy of the classical KT techniques needs to be improved. Thus, this paper implemented the Student Psychology Teaching Learning Optimisation-based Deep Long Short-Term Memory (SPTLO-based DLSTM) for predicting student performance. Here, [Formula: see text] -score normalisation is adapted for performing normalisation of data to make the data value rely on a specific range. Furthermore, the Synthetic Minority Oversampling Technique (SMOTE) is engaged to augment data to make data apt for enhanced handling. The Deep Maxout Network (DMN) with Ruzicka similarity is considered for feature fusion. The integration of deep KT to predict student performance is executed with Deep Long Short-Term Memory (DLSTM), which is trained to employ SPTLO. The SPTLO is generated by unifying Student Psychology Based Optimisation (SPBO) and Teaching-Learning-Based Optimisation (TLBO). Here, SPTLO-based DLSTM presented supreme accuracy of 92.5%, Mean Absolute Error (MAE) of 0.064 and Root mean square error (RMSE) of 0.312.