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Abdullah Saykili; Sinan Aydin; Yusuf Zafer Can Ugurhan; Aylin Öztürk; Mustafa Kemal Birgin – Technology, Knowledge and Learning, 2025
Learning analytics offer unprecedented opportunities for tracking and storing learning behaviors, thereby providing chances for optimizing learner engagement and success. The limited adoption of learning analytics by educational institutions hinders efforts to optimize learning processes through organizational and educational interventions,…
Descriptors: Undergraduate Students, Online Courses, Learning Analytics, Student Characteristics
Marijn Martens; Ralf De Wolf; Lieven De Marez – Technology, Knowledge and Learning, 2025
Algorithmic decision-making systems such as Learning Analytics (LA) are widely used in an educational setting ranging from kindergarten to university. Most research focuses on how LA is used and adopted by teachers. However, the perspective of students and parents who experience the (in)direct consequences of these systems is underexplored. This…
Descriptors: Algorithms, Decision Making, Learning Analytics, Secondary School Students
Ozan Rasit Yürüm; Tugba Taskaya-Temizel; Soner Yildirim – Technology, Knowledge and Learning, 2024
The purpose of this study was to investigate the use of predictive video analytics in online courses in the literature. A systematic literature review was performed based on a hybrid search strategy that included both database searching and backward snowballing. In total, 77 related publications published between 2011 and April 2023 were…
Descriptors: Literature Reviews, Distance Education, Online Courses, Video Technology
Umar Bin Qushem; Solomon Sunday Oyelere; Gökhan Akçapinar; Rogers Kaliisa; Mikko-Jussi Laakso – Technology, Knowledge and Learning, 2024
Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students' early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine…
Descriptors: Predictor Variables, Learning Analytics, At Risk Students, Computer Science
Forsyth, Carol M.; Graesser, Arthur; Millis, Keith – Technology, Knowledge and Learning, 2020
The current study investigated predictors of shallow versus deep learning within a serious game known as Operation ARA. This game uses a myriad of pedagogical features including multiple-choice tests, adaptive natural language tutorial conversations, case-based reasoning, and an E-text to engage students. The game teaches 11 topics in research…
Descriptors: Educational Games, Predictor Variables, Evidence Based Practice, Learning Analytics

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