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Baneres, David; Rodriguez-Gonzalez, M. Elena; Guerrero-Roldan, Ana Elena – IEEE Transactions on Learning Technologies, 2023
Course dropout is a concern in online higher education, mainly in first-year courses when different factors negatively influence the learners' engagement leading to an unsuccessful outcome or even dropping out from the university. The early identification of such potential at-risk learners is the key to intervening and trying to help them before…
Descriptors: Prediction, Models, Identification, Potential Dropouts
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Gupta, Anika; Garg, Deepak; Kumar, Parteek – IEEE Transactions on Learning Technologies, 2022
With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the…
Descriptors: Markov Processes, Online Courses, Learning Management Systems, Learning Analytics
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Nam, SungJin; Frishkoff, Gwen; Collins-Thompson, Kevyn – IEEE Transactions on Learning Technologies, 2018
In an intelligent tutoring system (ITS), it can be useful to know when a student has disengaged from a task and might benefit from a particular intervention. However, predicting disengagement on a trial-by-trial basis is a challenging problem, particularly in complex cognitive domains. In the present work, data-driven methods were used to address…
Descriptors: Intervention, Learner Engagement, Middle School Students, Vocabulary Development