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Ruben Till Wittrin; Benny Platte; Christian Roschke; Marc Ritter; Maximilian Eibl; Carolin Isabel Steiner; Volker Tolkmitt – IEEE Transactions on Learning Technologies, 2024
Virtual environments open up far-reaching possibilities with respect to knowledge impartation. Nevertheless, they have the potential to negatively influence learning behavior. As a possible positive determinant, especially in the digital context, the moment "game" can be listed. Accordingly, previous studies prove an overall positive…
Descriptors: Game Based Learning, Learning Motivation, Academic Achievement, Electronic Learning
Milos Ilic; Goran Kekovic; Vladimir Mikic; Katerina Mangaroska; Lazar Kopanja; Boban Vesin – IEEE Transactions on Learning Technologies, 2024
In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate…
Descriptors: Artificial Intelligence, Academic Achievement, Prediction, Programming
Analysis and Prediction of Students' Performance in a Computer-Based Course through Real-Time Events
Lucia Uguina-Gadella; Iria Estevez-Ayres; Jesus Arias Fisteus; Carlos Alario-Hoyos; Carlos Delgado Kloos – IEEE Transactions on Learning Technologies, 2024
Students learn not only directly from their teachers and books, but also by using their computers, tablets, and phones. Monitoring these learning environments creates new opportunities for teachers to track students' progress. In particular, this article is based on gathering real-time events as students interact with learning tools and materials…
Descriptors: Predictor Variables, Academic Achievement, Computer Assisted Instruction, Electronic Learning
Karamimehr, Zahra; Sepehri, Mohammad Mehdi; Sibdari, Soheil – IEEE Transactions on Learning Technologies, 2020
In this article, we offer and test a nonsurvey-based method to characterize learner emotions. Our method, instead of using surveys, uses logs of learner behaviors in learning management systems (LMS) to reason about the emotional state of the e-learner. We use the control value theory (CVT) as the theoretical base of measuring emotions. Using this…
Descriptors: Electronic Learning, Psychological Patterns, Integrated Learning Systems, Academic Achievement
Yousuf, Bilal; Conlan, Owen – IEEE Transactions on Learning Technologies, 2018
This paper introduces VisEN, a novel visual narrative framework that has been shown to facilitate, support, and enhance student engagement in an adaptive Online Learning Environment (OLE). VisEN provides explorable visual narratives personalized to students in order to support them in engaging with course content. The evaluation of VisEN showed…
Descriptors: Learner Engagement, Visual Aids, Electronic Learning, Individualized Instruction