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Virtual learning environment to predict withdrawal by leveraging deep learning
Author(s) -
Hassan SaeedUl,
Waheed Hajra,
Aljohani Naif R.,
Ali Mohsen,
Ventura Sebastián,
Herrera Francisco
Publication year - 2019
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22129
Subject(s) - computer science , artificial intelligence , machine learning , baseline (sea) , deep learning , analytics , learning analytics , recall , multidisciplinary approach , artificial neural network , educational data mining , logistic regression , data science , psychology , social science , oceanography , sociology , cognitive psychology , geology
The current evolution in multidisciplinary learning analytics research poses significant challenges for the exploitation of behavior analysis by fusing data streams toward advanced decision‐making. The identification of students that are at risk of withdrawals in higher education is connected to numerous educational policies, to enhance their competencies and skills through timely interventions by academia. Predicting student performance is a vital decision‐making problem including data from various environment modules that can be fused into a homogenous vector to ascertain decision‐making. This research study exploits a temporal sequential classification problem to predict early withdrawal of students, by tapping the power of actionable smart data in the form of students' interactional activities with the online educational system, using the freely available Open University Learning Analytics data set by employing deep long short‐term memory (LSTM) model. The deployed LSTM model outperforms baseline logistic regression and artificial neural networks by 10.31% and 6.48% respectively with 97.25% learning accuracy, 92.79% precision, and 85.92% recall.