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Nabizadeh, Amir Hossein; Goncalves, Daniel; Gama, Sandra; Jorge, Joaquim – IEEE Transactions on Learning Technologies, 2022
The main challenge in higher education is student retention. While many methods have been proposed to overcome this challenge, early and continuous feedback can be very effective. In this article, we propose a method for predicting student final grades in a course using only their performance data in the current semester. It assists students in…
Descriptors: College Students, Prediction, Grades (Scholastic), Game Based Learning
Mejia, Carolina; Florian, Beatriz; Vatrapu, Ravi; Bull, Susan; Gomez, Sergio; Fabregat, Ramon – IEEE Transactions on Learning Technologies, 2017
Existing tools aim to detect university students with early diagnosis of dyslexia or reading difficulties, but there are not developed tools that let those students better understand some aspects of their difficulties. In this paper, a dashboard for visualizing and inspecting early detected reading difficulties and their characteristics, called…
Descriptors: Clinical Diagnosis, Dyslexia, Visualization, Metacognition
Tempelaar, Dirk T.; Rienties, Bart; Nguyen, Quan – IEEE Transactions on Learning Technologies, 2017
Studies in the field of learning analytics (LA) have shown students' demographics and learning management system (LMS) data to be effective identifiers of "at risk" performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students…
Descriptors: Student Behavior, Integrated Learning Systems, Personality, Educational Research