article · IEEE Access
Online educators often struggle to evaluate how actively students participate due to the lack of face-to-face interaction, which can contribute to higher dropout rates. A new hybrid framework evaluates student engagement and examines its link to course success using data collected from 1,356 learners across three winter terms between 2020 and 2022. The model analyses activity measures, including time spent on the platform and forum postings, alongside learner emotions extracted from discussion text using bidirectional long short-term memory networks and FastText embeddings. Learners were grouped by engagement level using unsupervised clustering, followed by the evaluation of multiple supervised classifiers. A decision tree rule model proved most effective, reaching an accuracy of 98 per cent and an area under the curve of 0.97. Analysis showed that most students act primarily as observers, and the relationship between engagement and academic success is nonlinear.
Understanding student engagement in digital environments is vital for preventing dropouts and improving online learning outcomes. By automatically identifying emotional and behavioural participation signals from routine platform data, course instructors can better recognise disengaged students early and tailor their pedagogical support, even in large remote classes without direct physical observation.
This methodology could enable software developers to integrate automated engagement tracking and early-warning dropout tools into commercial learning management systems or online education platforms. Evaluated on real historical course data, the underlying algorithms represent applied research that is tested at a prototype stage, though the abstract does not indicate whether it has been deployed in a live operational product.
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Learner engagement is a critical concept that can lead to satisfaction, motivation, and success in e-learning courses. It covers contextual, emotional, behavioral, cognitive, and social aspects. The instructors have difficulties identifying who is involved in the courses and the lack of face-to-face interaction with a learning resource to act upon and reduce the dropout rate. This paper presents a novel approach that aims to predict learner engagement in online courses and quantify the relationship between the learners’ success and their engagement. For this purpose, we used the traces gathered from 1 356 learners’ reactions in e-learning courses during the winters of 2020, 2021, and 2022, to implement this approach. To model the learning engagement, a variety of features were considered such as the total number of posts made in forums and the total time spent on the e-learning platform. This study uses the BiLSTM method with FastText word embedding to detect learners’ emotions from the forum discussions. Then, an unsupervised clustering technique based on the new dataset was used to cluster learners into groups according to their engagement level. Several supervised classification algorithms were trained and their performance was evaluated using cross-validation techniques and diverse precision metrics. The findings indicate that the decision tree rule model is more relevant than others, with an accuracy of 98% and an AUC score of 0.97. The conclusions of this research reveal that most learners are observers and that there is a nonlinear correlation between learning success and learning engagement.
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DOI: 10.1109/access.2023.3293827
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