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• W4285196632 abstract "Predicting student dropout is becoming imperative in online learning platforms. Before COVID-19, predicting student dropout was systematically done manually. Therefore, there is a need to analyse students’ behaviour, cognitive learning styles, and other metacognitive patterns of learning in real-time from available data repositories to reduce student dropout and subsequently develop robust strategies for instructional design and remedial interventions to enhance student success and retention. In this study, we present a comprehensive review of deep learning models applied to predict student dropout in online learning platforms. In addition, challenges and opportunities associated with online learning are presented in this study. The study revealed that convolutional neural networks, recurrent neural networks, long short-term memory and bidirectional long short-term memory have been predominantly used to predict student dropout using predictors such as course assessments, socio-economic, access to online resources, personal skills and course attributes. However, the study revealed that the psychological state of students was not taken into consideration by many authors, yet it impacts students’ learning outcomes and assists policymakers in providing remedial interventions. Therefore, future work can delve deeper into the integration of psychological attributes such as stress, anxiety, attitude towards studying, student interests and counselling sessions to predict student dropout during disasters and health emergencies." @default.
• W4285196632 created "2022-07-14" @default.
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• W4285196632 date "2022-01-01" @default.
• W4285196632 modified "2023-09-26" @default.
• W4285196632 title "Predicting Student Dropout in Massive Open Online Courses Using Deep Learning Models - A Systematic Review" @default.
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• W4285196632 doi "https://doi.org/10.1007/978-3-031-09073-8_20" @default.
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