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Showing 106 to 120 of 990 results Save | Export
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Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
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Ting-Ting Wu; Hsin-Yu Lee; Pei-Hua Chen; Wei-Sheng Wang; Yueh-Min Huang – Journal of Computer Assisted Learning, 2025
Background: Conventional reflective learning methodologies in programming education often lack structured guidance and individualised feedback, limiting their pedagogical effectiveness. Whilst computational thinking (CT) offers a systematic problem-solving framework with decomposition, pattern recognition, abstraction, and algorithm design, its…
Descriptors: Computation, Thinking Skills, Educational Diagnosis, Diagnostic Tests
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Juan M. Pieschacon; Ross T. Smith; Andrew Cunningham; Ellen O'Callaghan; Daniel Harvie – Journal of Computer Assisted Learning, 2025
Background: Physiotherapy education traditionally relies on face-to-face instruction for teaching complex manual therapy techniques. Current remote teaching methods struggle to convey spatial aspects of physical techniques, limiting their effectiveness for practical skills training. Few examples were found of immersive training systems for…
Descriptors: Spatial Ability, Visualization, Physical Therapy, Training
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Elif Sari; Turgay Han – Journal of Computer Assisted Learning, 2024
Background: With the growing trend of integrating technology into teaching environments, using Automated Writing Evaluation (AWE) in writing instruction has been extensively studied over the last two decades. The studies on AWE mostly investigated its impact on students' writing proficiencies and revealed conflicting results. However, very few…
Descriptors: Writing Evaluation, Second Language Learning, Second Language Instruction, English (Second Language)
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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
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Parmaxi, Antigoni; Demetriou, Alan A. – Journal of Computer Assisted Learning, 2020
This systematic review study synthesizes research findings pertaining to the use of augmented reality (AR) in language learning. Published research from 2014 to 2019 has been explored and specific inclusion and exclusion criteria have been applied resulting in 54 relevant publications. Our findings determined: (a) devices and software employed for…
Descriptors: Computer Uses in Education, Language Acquisition, Computer Simulation, State of the Art Reviews
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Conijn, Rianne; Kleingeld, Ad; Matzat, Uwe; Snijders, Chris – Journal of Computer Assisted Learning, 2022
Background: Online and blended learning need an appropriate assessment strategy which ensures academic integrity. During the pandemic, many universities have chosen for online proctoring. Although some earlier examples suggest that online proctoring may reduce cheating, the potential side-effects of proctoring are largely unknown. Objectives:…
Descriptors: Supervision, Computer Assisted Testing, Integrity, Cheating
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Ezeamuzie, Ndudi O.; Leung, Jessica S. C.; Garcia, Raycelle C. C.; Ting, Fridolin S. T. – Journal of Computer Assisted Learning, 2022
Background: The idea of computational thinking is underpinned by the belief that anyone can learn and use the underlying concepts of computer science to solve everyday problems. However, most studies on the topic have investigated the development of computational thinking through programming activities, which are cognitively demanding. There is a…
Descriptors: Computation, Thinking Skills, Problem Solving, Cognitive Processes
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Timothy Gallagher; Bert Slof; Marieke van der Schaaf; Ryo Toyoda; Yusra Tehreem; Sofia Garcia Fracaro; Liesbeth Kester – Journal of Computer Assisted Learning, 2024
Background: The potential of learning analytics dashboards in virtual reality simulation-based training environments to influence occupational self-efficacy via self-reflection phase processes in the Chemical industry is still not fully understood. Learning analytics dashboards provide feedback on learner performance and offer points of comparison…
Descriptors: Learning Analytics, Self Efficacy, Reflection, Chemistry
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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
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Ezgi Dogan; Yusuf Levent Sahin – Journal of Computer Assisted Learning, 2024
Background: It is crucial to use innovative tools to gain clinical reasoning skills through experiential learning in pharmacy education, and one of the most effective tools used in this is virtual reality. However, the lack of research that empirically demonstrates the instructional principles of virtual environment design, especially in the…
Descriptors: Computer Simulation, Simulated Environment, Pharmacy, Pharmaceutical Education
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Gulnur Tyulepberdinova; Madina Mansurova; Talshyn Sarsembayeva; Sulu Issabayeva; Darazha Issabayeva – Journal of Computer Assisted Learning, 2024
Background: This study aims to assess how well several machine learning (ML) algorithms predict the physical, social, and mental health condition of university students. Objectives: The physical health measurements used in the study include BMI (Body Mass Index), %BF (percentage of Body Fat), BSC (Blood Serum Cholesterol), SBP (Systolic Blood…
Descriptors: Artificial Intelligence, Algorithms, Predictor Variables, Physical Health
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Jiang Xiaxia; Li Yahong; Kuang Ziyi; Yu Jiajun – Journal of Computer Assisted Learning, 2025
Background: Video conferencing technology has moved online education into a new stage of real-time video interaction. However, shortcomings such as students' lack of concentration and substantive engagement during video conferencing greatly limit the improvement of online learning effectiveness. According to social presence theory and the…
Descriptors: College Faculty, College Students, Electronic Learning, Distance Education
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Dominic Lohr; Marc Berges; Abhishek Chugh; Michael Kohlhase; Dennis Müller – Journal of Computer Assisted Learning, 2025
Background: Over the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models…
Descriptors: Resource Units, Semantics, Automation, Questioning Techniques
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Chiao Ling Huang; Lianzi Fu; Shih-Chieh Hung; Shu Ching Yang – Journal of Computer Assisted Learning, 2025
Background: Many studies have highlighted the positive effects of visual programming instruction (VPI) on students' learning experiences, programming self-efficacy and flow experience. However, there is a notable gap in the research on how these factors specifically impact programming achievement and learning intentions. Our study addresses this…
Descriptors: Attention, Self Efficacy, Visual Aids, Instructional Effectiveness
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