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Araos, Andrés; Damsa, Crina; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: The surge of online platforms has generated interest in how specialized platforms support formal and informal learning in various disciplinary domains. Knowledge is still limited regarding how undergraduate students navigate and use platforms to learn. Objectives: This study explores computer and software engineering students' learning…
Descriptors: Computer Science Education, Computer Software, Learning Activities, Undergraduate Students
Julius Moritz Meier; Peter Hesse; Stephan Abele; Alexander Renkl; Inga Glogger-Frey – Journal of Computer Assisted Learning, 2024
Background: In example-based learning, examples are often combined with generative activities, such as comparative self-explanations of example cases. Comparisons induce heavy demands on working memory, especially in complex domains. Hence, only stronger learners may benefit from comparative self-explanations. While static text-based examples can…
Descriptors: Video Technology, Models, Cues, Problem Solving
Cheng, Meixia; Wang, Fuxing; Mayer, Richard E. – Journal of Computer Assisted Learning, 2023
Background: Learning-by-teaching is a generative learning strategy in which students are told they will have to teach what they are learning to others. Although learning-by-teaching has been shown to be effective in some cases, few studies have established guidelines for how to optimize the benefits of learning-by-teaching as a generative learning…
Descriptors: Educational Benefits, Student Developed Materials, Film Production, Instructional Films
Klingenberg, Sara; Fischer, Rachel; Zettler, Ingo; Makransky, Guido – Journal of Computer Assisted Learning, 2023
Introduction: This study investigates the effectiveness of the segmentation principle from the cognitive theory of multimedia learning as well as the effectiveness of the generative learning strategy of summarization in immersive virtual reality (IVR) within a sample of preadolescents. Although previous research has supported the effectiveness of…
Descriptors: Computer Simulation, Computer Uses in Education, Secondary School Students, STEM Education
Tingting Wang; Alejandra Ruiz-Segura; Shan Li; Susanne P. Lajoie – Journal of Computer Assisted Learning, 2024
Background: Scholars have confirmed the vital roles of self-regulated learning (SRL) behaviours in predicting task performance, especially within non-linear technology-rich learning environments (TREs). However, few studies focused on the learning costs (e.g., study effort and time-on-task) related to SRL and the efficiency outcome of SRL (i.e.,…
Descriptors: Problem Solving, Educational Environment, Efficiency, Student Behavior
Min Young Doo; Yeonjeong Park – Journal of Computer Assisted Learning, 2024
Background: Despite the many advantages of flipped learning, it is challenging for educators to ensure that students complete the pre-class learning assignments before the in-class session. Objectives: Using a learning analytics approach, this study analysed students' pre-class video-watching behaviour in flipped learning with a focus on learners'…
Descriptors: Flipped Classroom, Video Technology, Student Behavior, Learning Strategies
Geng, Xuewang; Yamada, Masanori – Journal of Computer Assisted Learning, 2023
Background: Augmented reality has been widely applied in various fields, and its benefits in language learning have been increasingly recognized. However, the investigation of effective learning behaviours and processes in augmented reality learning environments, taking into account temporality and analysis of differences in learning behaviours…
Descriptors: Learning Analytics, Second Language Learning, Second Language Instruction, Learning Processes
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
Esnaashari, Shadi; Gardner, Lesley A.; Arthanari, Tiru S.; Rehm, Michael – Journal of Computer Assisted Learning, 2023
Background: It is vital to understand students' Self-Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable-oriented approach. Moreover, little has been known about the unfolding process of students' SRL…
Descriptors: Metacognition, Student Attitudes, Learning Strategies, Questionnaires
Kittel, Anne Frieda Doris; Seufert, Tina – Journal of Computer Assisted Learning, 2023
Background: Most workplace learning is informal. However, some employees struggle to execute informal learning strategies effectively because they lack the necessary knowledge or skills or use their knowledge and skills ineffectively or not at all. Objectives: We examined the effects of computer-based micro-learning interventions that provide…
Descriptors: Informal Education, Learning Strategies, Workplace Learning, Job Skills
Lahza, Hatim; Khosravi, Hassan; Demartini, Gianluca – Journal of Computer Assisted Learning, 2023
Background: The use of crowdsourcing in a pedagogically supported form to partner with learners in developing novel content is emerging as a viable approach for engaging students in higher-order learning at scale. However, how students behave in this form of crowdsourcing, referred to as learnersourcing, is still insufficiently explored.…
Descriptors: Learning Analytics, Learning Strategies, Electronic Learning, Independent Study
Tomoko Yabukoshi; Atsushi Mizumoto – Journal of Computer Assisted Learning, 2024
Background: While self-regulated learning (SRL) strategy-based writing instruction has been proposed in English as a foreign language (EFL) classrooms, there is insufficient evidence with Japanese EFL learners and little discussion on incorporating online resources into SRL strategy-based writing instruction, despite the availability of various…
Descriptors: Writing (Composition), Educational Technology, Self Management, Learning Strategies
Truzoli, Roberto; Viganò, Caterina; Galmozzi, Paolo Gabriele; Reed, Phil – Journal of Computer Assisted Learning, 2020
The current study explored the relationship between problematic internet use (PIU) and motivation to learn, and examined psychological and social factors mediating this relationship. Two hundred and eighty-five students in an Italian University were recruited for the current study. There was a negative relationship between PIU and motivation to…
Descriptors: Internet, Learning Motivation, College Students, Psychological Patterns
Hüseyin Ates; Mustafa Köroglu – Journal of Computer Assisted Learning, 2024
Background: Online collaboration tools have been identified as potentially effective means for enhancing student learning, motivation, and engagement in science education. However, their effectiveness in improving science education outcomes among middle school students remains uncertain. Objectives: The study aimed to investigate the impact of…
Descriptors: Cooperative Learning, Comparative Analysis, Academic Achievement, Learner Engagement

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