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Abhinava Barthakur; Rebecca Marrone; Shadi Esnaashari; Vitomir Kovanovic; Shane Dawson – Journal of Computer Assisted Learning, 2025
Background: There is growing recognition in the education sector of the critical role empirical data plays in aiding strategic decision-making and supporting personalised learning. The call for increased and more nuanced data-driven decision-making has been primarily addressed by the institutional use of student learning dashboards and learner…
Descriptors: Holistic Approach, Decision Making, Data Use, Educational Research
Halima Alnashiri; Mladen Rakovic; Sadia Nawaz; Xinyu Li; Joni Lamsa; Lyn Lim; Maria Bannert; Sanna Jarvela; Dragan Gasevic – Journal of Computer Assisted Learning, 2025
Background: Integrating information from multiple sources is a common yet challenging learning task for secondary school students. Many underuse metacognitive skills, such as monitoring and control, which are essential for promoting engagement and effective learning outcomes. Objective: This study aims to examine the relationship between…
Descriptors: Secondary School Students, Metacognition, Writing (Composition), English
Atezaz Ahmad; Jan Schneider; Dai Griffiths; Daniel Biedermann; Daniel Schiffner; Wolfgang Greller; Hendrik Drachsler – Journal of Computer Assisted Learning, 2024
Background: During the past decade, the increasingly heterogeneous field of learning analytics has been critiqued for an over-emphasis on data-driven approaches at the expense of paying attention to learning designs. Method and objective: In response to this critique, we investigated the role of learning design in learning analytics through a…
Descriptors: Instructional Design, Learning Analytics, Data Use, Literature Reviews
Jing Chen; Tianhui Chen – Journal of Computer Assisted Learning, 2025
Background: The creation of Intelligent Supervision Platforms in universities leverages Big Data for robust monitoring and decision-making, which significantly enhances overall efficiency and adaptability in educational environments. Objectives: This research focuses on evaluating how Big Data-driven Intelligent Supervision Platforms in…
Descriptors: Educational Change, Higher Education, Universities, Supervision
Zamecnik, Andrew; Kovanovíc, Vitomir; Joksimovíc, Srécko; Grossmann, Georg; Ladjal, Djazia; Marshall, Ruth; Pardo, Abelardo – Journal of Computer Assisted Learning, 2023
Background: Maintaining cohesion is critical for teams to achieve shared goals and performance outcomes within a work-integrated learning (WIL) environment. Cohesion is an emergent state that develops over time, representing the synchrony of different behavioural interactions. Cohesive teams will exhibit such phenomena by their temporal…
Descriptors: Data Use, Group Dynamics, College Students, Cooperative Learning
Helsabeck, Nathan P.; Justice, Laura M.; Logan, Jessica A. R. – Journal of Computer Assisted Learning, 2022
Background: Process data, data generated by a user's interaction with a web-based application, is an emerging tool in educational research. The current study explores using process data as a measure of implementation fidelity to a randomized control trial (RCT) of the Read It Again Mobile (RIA-M) curricular supplement. Objectives: To determine the…
Descriptors: Fidelity, Program Implementation, Early Intervention, Handheld Devices
Ishari Amarasinghe; Konstantinos Michos; Francisco Crespi; Davinia Hernández-Leo – Journal of Computer Assisted Learning, 2024
Background: Data-driven educational technology solutions have the potential to support teachers in different tasks, such as the designing and orchestration of collaborative learning activities. When designing, such solutions can improve teacher understanding of how learning designs impact student learning and behaviour; and guide them to refine…
Descriptors: Learning Activities, Educational Technology, Design, Cooperative Learning

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