The power of Deep Learning techniques for predicting student performance in Virtual Learning Environments: A systematic literature review

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With the advances in Artificial Intelligence (AI) and the increasing volume of online educational data, Deep Learning techniques have played a critical role in predicting student performance. Recent developments have assisted instructors in determining the strengths and weaknesses of student achievement. This understanding will benefit from adopting the necessary interventions to assist students in improving their performance, helping at-risk of failure students, and preventing dropout rates. The review analyzed 46 studies between 2019 and 2023 that apply one or more Deep Learning (DL) techniques, either single or in combination with Machine Learning (ML) or Ensemble Learning techniques. Moreover, the review utilized datasets from public Massive Open Online Courses (MOOCs), private Learning Management Systems (LMSs), and other platforms. Four categories were used to group the features: demographic, previous academic performance, current academic performance, and learning behavior/activity features. The analysis revealed that the DNNs and CNN-LSTM models were the most common techniques. Moreover, the studies that used DL techniques, such as CNNs, DNNs, and LSTMs, performed well by achieving high prediction accuracy above 90%; other studies achieved accuracy ranging (60 to 90)%. For datasets used within the reviewed studies, even though 44% of the studies used LMSs datasets, Open University Learning Analytics Dataset (OULAD) was the most used dataset from MOOCs. The analysis of grouped features shows that among the various categories examined, learning behavior and activity features stand out as the most significant predictors, suggesting that students engagement with their learning environment through their overall participation offers crucial insights into their success. The educational prediction findings hopefully serve as a strong foundation for administrators and instructors to observe student performance and provide a suitable educational adaptation that can meet their needs to protect them from failure and prevent their dropout.