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Lars de Vreugd; Anouschka van Leeuwen; Marieke van der Schaaf – Journal of Computer Assisted Learning, 2025
Background: University students need to self-regulate but are sometimes incapable of doing so. Learning Analytics Dashboards (LADs) can support students' appraisal of study behaviour, from which goals can be set and performed. However, it is unclear how goal-setting and self-motivation within self-regulated learning elicits behaviour when using an…
Descriptors: Learning Analytics, Educational Technology, Goal Orientation, Learning Motivation
Slaviša Radovic; Niels Seidel; Joerg M. Haake; Regina Kasakowskij – Journal of Computer Assisted Learning, 2024
Background: Self-assessment serves to improve learning through timely feedback on one's solution and iterative refinement as a way to improve one's competence. However, the complexity of the self-assessment process is widely recognized, as well as that students can benefit from it only if their assessment is accurate enough. Objectives: In order…
Descriptors: Self Evaluation (Individuals), Distance Education, Student Behavior, Accuracy
Juanjuan Chen; Minhong Wang; Tina A. Grotzer; Chris Dede – Journal of Computer Assisted Learning, 2024
Background: In scientific inquiry learning, students often have difficulties conducting hypothetical reasoning with multiple intertwined variables. Concept maps have a potential to facilitate complex thinking and reasoning. However, there is little investigation into the content of student-constructed concept maps and its association with inquiry…
Descriptors: Concept Mapping, Task Analysis, Inquiry, Active Learning
Jiarui Hou; James F. Lee; Stephen Doherty – Journal of Computer Assisted Learning, 2025
Background: Recent research has demonstrated the potential of mobile-assisted learning to enhance learners' learning outcomes. In contrast, the learning processes in this regard are much less explored using eye tracking technology. Objective: This systematic review study aims to synthesise the relevant work to reflect the current state of eye…
Descriptors: State of the Art Reviews, Eye Movements, Electronic Learning, Handheld Devices
Martin Merkt; Daniel Bodemer – Journal of Computer Assisted Learning, 2024
Background: When watching educational online videos, learners need to determine whether the videos' contents are suitable for learning. Whereas this may induce metacognitive monitoring processes, it may also distract learners from the learning materials. Objectives: In the current set of experiments, we investigated whether asking participants to…
Descriptors: Video Technology, Teaching Methods, Metacognition, Instructional Materials