Diverging Hybrid and Deep Learning Models into Predicting Students’ Performance in Smart Learning Environments – A Review

COVID-19 continues to overwhelm the education sectors globally posing threats to progress made towards inclusive and equity education in the previous years. Before COVID-19, continuous evaluation methods were systematically done manually in many learning institutions, now it is difficult to timely identify underperforming students, and students who are at risk of dropping out and provision of timely remedial and appropriate actions tailored for individual’s needs. To alleviate this situation, early prediction of students’ performance using deep learning techniques and data generated from smart learning environments becomes imperative. Therefore, this study aimed at providing a pioneering comprehensive review of hybrid and deep learning models to predict students’ performance in online learning environments. The study revealed that deep learning techniques extract hidden data to predict students’ performance, identify students at risk of dropping out, monitor students’ cognitive learning styles and unusual learning behaviours, emotional state of students to facilitate pedagogical content knowledge, instructional designs and appropriate action promptly. These models use various performance predictors such as course attributes, study time and duration, internal assessments, socio-economic, students’ legacy and learning environment. Furthermore, the study revealed that the psychological state of students was not taken into consideration, yet it impacts learning outcomes. However, the varying context of implementation could be the leading cause of differences in perspective to determine performance predictors that are reliable to predict student performance. Predicting students’ performance should be done prior, during and at the end of the course to ensure effective implementation of educational interventions.