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Vanessa Echeverria; Gloria Fernandez Nieto; Linxuan Zhao; Evelyn Palominos; Namrata Srivastava; Dragan Gaševic; Viktoria Pammer-Schindler; Roberto Martinez-Maldonado – Journal of Computer Assisted Learning, 2025
Background: Dashboards play a prominent role in learning analytics (LA) research. In collaboration activities, dashboards can show traces of team participation. They are often evaluated based on students' perceived satisfaction and engagement with the dashboard. However, there is a notable methodological gap in understanding how these dashboards…
Descriptors: Learning Analytics, Educational Technology, Student Attitudes, Reflection
Rakovic, Mladen; Iqbal, Sehrish; Li, Tongguang; Fan, Yizhou; Singh, Shaveen; Surendrannair, Surya; Kilgour, Jonathan; Graaf, Joep; Lim, Lyn; Molenaar, Inge; Bannert, Maria; Moore, Johanna; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: Assignments that involve writing based on several texts are challenging to many learners. Formative feedback supporting learners in these tasks should be informed by the characteristics of evolving written product and by the characteristics of learning processes learners enacted while developing the product. However, formative feedback…
Descriptors: Artificial Intelligence, Essays, High Achievement, Writing Achievement
Huang, Xiaoshan; Li, Shan; Wang, Tingting; Pan, Zexuan; Lajoie, Susanne P. – Journal of Computer Assisted Learning, 2023
Background: Medical students use a variety of self-regulated learning (SRL) strategies in different medical reasoning (MR) processes to solve patient cases of varying complexity. However, the interplay between SRL and MR processes is still unclear. Objectives: This study investigates how self-regulated learning (SRL) and medical reasoning (MR)…
Descriptors: Medical Students, Self Management, Problem Solving, Logical Thinking
Sisman, Burak; Kucuk, Sevda; Ozcan, Neslihan – Journal of Computer Assisted Learning, 2022
Background: Educational robotics (ER) is a means of teaching technology and engineering to students that offers an active learning environment by encouraging them to create meaningful and unique products. ER also gives students the opportunity to work collaboratively. Objectives: In this study, elementary school students' behavioral patterns were…
Descriptors: Elementary School Students, Student Behavior, Behavior Patterns, Cooperative Learning
Wu, Bian; Hu, Yiling; Ruis, A. R.; Wang, Minhong – Journal of Computer Assisted Learning, 2019
Computational thinking (CT), the ability to devise computational solutions for real-life problems, has received growing attention from both educators and researchers. To better improve university students' CT competence, collaborative programming is regarded as an effective learning approach. However, how novice programmers develop CT competence…
Descriptors: Thinking Skills, Problem Solving, Teaching Methods, College Students
Wang, Hong-You; Lin, Vivien; Hwang, Gwo-Jen; Liu, Gi-Zen – Journal of Computer Assisted Learning, 2019
To assess the effects and learner perceptions of context-aware ubiquitous language learning (CAULL), a green-building English-learning application (GBELA) employing sensing technology was created to develop participants' English listening and reading skills using smartphones and QR codes. This study investigated the effects of 40 participants'…
Descriptors: Reading Skills, Listening Skills, English (Second Language), Second Language Learning
Lin, C.-C.; Guo, K.-H.; Lin, Y.-C. – Journal of Computer Assisted Learning, 2016
This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate…
Descriptors: Remedial Instruction, Artificial Intelligence, Intelligent Tutoring Systems, Electronic Learning