Predicting academic performance of students from VLE big data using deep learning models

• Published in: Computers in human behavior 2020-03, Vol.104, p.106189, Article 106189
• Main Authors: Waheed, Hajra, Hassan, Saeed-Ul, Aljohani, Naif Radi, Hardman, Julie, Alelyani, Salem, Nawaz, Raheel
• Format: Article
• Language: English
• Subjects: Academic achievement; Artificial neural networks; CAI; Computer assisted instruction; Decision analysis; Decision making; Deep learning; Education; Educational data; Feature extraction; Learning analytics; Learning management systems; Machine learning; Model accuracy; Neural networks; Performance prediction; Predicting success; Regression analysis; Students; Support vector machines; Virtual environments; Virtual learning environments (VLE)
• ISSN: 0747-5632; 1873-7692
• DOI: 10.1016/j.chb.2019.106189

The abundance of accessible educational data, supported by the technology-enhanced learning platforms, provides opportunities to mine learning behavior of students, addressing their issues, optimizing the educational environment, and enabling data-driven decision making. Virtual learning environments complement the learning analytics paradigm by effectively providing datasets for analysing and reporting the learning process of students and its reflection and contribution in their respective performances. This study deploys a deep artificial neural network on a set of unique handcrafted features, extracted from the virtual learning environments clickstream data, to predict at-risk students providing measures for early intervention of such cases. The results show the proposed model to achieve a classification accuracy of 84%–93%. We show that a deep artificial neural network outperforms the baseline logistic regression and support vector machine models. While logistic regression achieves an accuracy of 79.82%–85.60%, the support vector machine achieves 79.95%–89.14%. Aligned with the existing studies - our findings demonstrate the inclusion of legacy data and assessment-related data to impact the model significantly. Students interested in accessing the content of the previous lectures are observed to demonstrate better performance. The study intends to assist institutes in formulating a necessary framework for pedagogical support, facilitating higher education decision-making process towards sustainable education. •The data generated by the technology-enhanced learning platforms has enabled sustainable data-driven decision making.•The clickstream data from the virtual learning environments can predict at-risk students for early intervention.•The artificial neural network outperforms existing models in predicting students at-risk.•The inclusion of legacy and assessment-related data improve the prediction power of the model.