Heuristic Technique to Find Optimal Learning Rate of LSTM for Predicting Student Dropout Rate

Abstract

Predictive analytics is being increasingly recognized as being important for evaluating university students’ academic achievement. Utilizing big data analytics particularly students’ demographic information, offers valuable insights to bolster academic success and enhance completion rates. For instance, learning analytics is a vital element of big data within university settings, offering strategic decision-makers the chance to conduct time series analyses on learning activities. We have used semesters first and second records of Polytechnic Institute of Portalegre students. Advanced deep learning methods, such as the Long short-term memory (LSTM) model are employed to analyze students at risk of retention issues. Typically, the best design for a deep neural network model was found by trial and error, which is a laborious and exponential combinatorial challenge. Hence we proposed the heuristic technique to configure the parameters of the neural network. The parameter taken into account in this work is the learning rate of the Adam optimizer. In this study, we have presented the ant colony optimization (ACO) technique to determine the ideal learning rate for model training. Experimental results obtained with the predictive model indicated that prediction of student retention is possible with a high level of accuracy using ACO-LSTM approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.