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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
Skulmowski, Alexander – Journal of Computer Assisted Learning, 2022
Background: Realistic visualizations have been associated with benefits in retention performance, but also with disadvantages in transfer tasks. Realistic details are considered to be helpful for the initial learning of content, but not for the generalization to other domains and tasks. Objectives: The contradictory nature of previous evidence…
Descriptors: Transfer of Training, Visualization, Anatomy, Geometric Concepts
van Marlen, Tim; van Wermeskerken, Margot; Jarodzka, Halszka; Raijmakers, Maartje; van Gog, Tamara – Journal of Computer Assisted Learning, 2022
Background: Eye movement modelling examples (EMME) are demonstrations in which learners' not only see a model's (e.g., a teacher's) task performance on a computer screen (as in regular video examples) but also the model's eye movements (represented as moving coloured dots overlaid on the screen). Thereby EMME help guide learners' attention towards…
Descriptors: Eye Movements, Logical Thinking, Technology Uses in Education, Task Analysis
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
Papamitsiou, Zacharoula; Economides, Anastasios A. – Journal of Computer Assisted Learning, 2021
This longitudinal study investigates the differences in learners' effortful behaviour over time due to receiving metacognitive help--in the form of on-demand task-related visual analytics. Specifically, learners' interactions (N = 67) with the tasks were tracked during four self-assessment activities, conducted at four discrete points in time,…
Descriptors: Metacognition, Help Seeking, Learning Analytics, Student Behavior
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
Jamshidifarsani, Hossein; Tamayo-Serrano, Paul; Garbaya, Samir; Lim, Theodore – Journal of Computer Assisted Learning, 2021
Training design for automatic skills has a vast domain of application, such as education, physical and cognitive rehabilitation, as well as sports, arts and professional training. Gamification concept used in technology-assisted training has the potential to increase motivation, engagement and adherence to the training programme. Currently, the…
Descriptors: Game Based Learning, Models, Computer Assisted Instruction, Task Analysis
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
Wang, Cixiao; Xu, Lingling; Liu, Hui – Journal of Computer Assisted Learning, 2022
Background: Virtual manipulatives (VMs) are increasingly adopted in inquiry activities. However, the effects of the ratio of mobile device-based VMs to students and external scripts (a guiding structure for prompting group process) provision on group interaction has not been detailed. Objectives: This study proposed four different technology…
Descriptors: Manipulative Materials, Educational Technology, Handheld Devices, Cooperative Learning
Kok, Ellen; Hormann, Olle; Rou, Jeroen; Saase, Evi; der Schaaf, Marieke; Kester, Liesbeth; Gog, Tamara – Journal of Computer Assisted Learning, 2022
Background: Performance monitoring plays a key role in self-regulated learning, but is difficult, especially for complex visual tasks such as navigational map reading. Gaze displays (i.e. visualizations of participants' eye movements during a task) might serve as feedback to improve students' performance monitoring. Objectives: We hypothesized…
Descriptors: Metacognition, Eye Movements, Task Analysis, Visualization
Chen, Ching-Huei; Yang, Chin-Kun; Huang, Kun; Yao, Kai-Chao – Journal of Computer Assisted Learning, 2020
Robotics education has received an increasing attention in recent years as a means to build students' motivation, team collaboration skills, and other valuable 21st century competencies. Yet there is a lack of experimental studies to investigate and identify strategies to facilitate robotics education. This study adopted a 2 × 2 quasi-experimental…
Descriptors: Computer Simulation, Competition, Robotics, 21st Century Skills
Gao, Ming; Zhang, Jingjing; Lu, Yu; Kahn, Ken; Winters, Niall – Journal of Computer Assisted Learning, 2023
Background: As a non-cognitive trait, grit plays an important role in human learning. Although students higher in grit are more likely to perform well on tests, how they learn in the process has been underexamined. Objectives: This study attempted to explore how students with different levels of grit behave and learn in an exploratory learning…
Descriptors: Resilience (Psychology), Academic Persistence, Personality Traits, Usability
He, Qiwei; Borgonovi, Francesca; Suárez-Álvarez, Javier – Journal of Computer Assisted Learning, 2023
Background: Data-driven investigations of how students transit pages in digital reading tasks and how much time they spend on each transition allow mapping sequences of navigation behaviours into students' navigation reading strategies. Objectives: The purpose of this study is threefold: (1) to identify students' navigation patterns in…
Descriptors: Data Analysis, Reading Processes, Task Analysis, Time on Task
Slof, Bert; van Leeuwen, Anouschka; Janssen, Jeroen; Kirschner, Paul A. – Journal of Computer Assisted Learning, 2021
In computer-supported collaborative learning research, studies examining the combined effects of individual level, group level and within-group differences level measures on individual achievement are scarce. The current study addressed this by examining whether individual, group and within-group differences regarding engagement and prior…
Descriptors: Cooperative Learning, Prior Learning, Secondary School Students, Academic Achievement