IEEE Access (Jan 2023)
Deep FM-Based Predictive Model for Student Dropout in Online Classes
Abstract
The student’s high dropout rate is a severe issue in online learning courses. As a result, it is creating concerns for academics and administrators in the field of education. A practical method of preventing dropouts is predicting students’ likelihood of dropping out. This study uses an explainable factorization machine and deep-learning approach to predict students’ dropouts with two datasets, namely HarvardX Person-Course Academic Year 2013 De-Identified and MOOC datasets. With the solvable approach, the aim is to enable the interpretation of the predictive models to produce actionable insights for related online educational interventions. This approach creates a DeepFM-based prediction model for student dropout, which entails multiple processes, including data preparation, feature engineering, model construction, training, assessment, and deployment. Moreover, the DeepFM design combines a factorization machine with DNN models to forecast student dropouts. It examines performance metrics, including recall, F1 score, accuracy, precision, and AUC-ROC. After ten iterations and 64 batches, the DeepFM model accurately predicted student dropout from online courses with a 99% accuracy rate on validation data. It also outperformed other techniques because of its capacity to capture complicated non-linear connections between features, combine dense and sparse information, and consider the unique properties of online learning. This study illustrated using an explainable factorization machine learning and DNN approach called DeepFM to interpret the underlying reasons for predicting students’ dropout from online classes. Moreover, this approach has the potential to be extended to additional Massively open online courses (MOOC) datasets to assist educators and institutions in identifying at-risk students and providing targeted interventions to enhance their learning results.
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