Implementation of Deep Learning Methods in Predicting Student Performance: A Systematic Literature Review
Abstract
The use of machine learning, which is one of the implementations in the field of artificial intelligence, has penetrated into various fields, including education. By using a combination of machine learning techniques, statistics, and databases, educational data mining can be carried out to find out the patterns that exist in a particular dataset. One use of educational data mining is to predict student performance. The results of student performance predictions can be used as an instrument for monitoring and evaluating the learning process so that it can help determine further steps in order to improve the learning process. This study aims to determine the state of the art implementation of deep learning which is part of machine learning in the context of educational data mining, especially regarding student performance predictions. In this study, a systematic literature review is presented to determine the variation of deep learning techniques or algorithms used and their performance. Twenty scientific publications were found and the average performance achieved in making predictions was 89.85%. The majority of the techniques used are Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) with demographic, behavioral, and academic data features.
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