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Mohamad Iyad Al-Khiami; Martin Jaeger; Sayed Mohamad Soleimani; Abdulhadi Kazem – Journal of Computer Assisted Learning, 2024
Background Study: The research discusses the need for a paradigm shift in engineering education current practices to accommodate the digital native students. The paper emphasizes the importance of integrating disruptive technologies, namely Virtual Reality (VR) through Head Mounted Displays VR (HMD VR) and Desktop Based VR (DB VR) and comparing it…
Descriptors: Undergraduate Students, Engineering Education, Computer Simulation, Student Motivation
Meehyun Yoon; Koun Choi; Seonghye Yoon; Il-Hyun Jo – Journal of Computer Assisted Learning, 2024
Background: Virtual reality (VR) is becoming increasingly accessible and being utilized in various organizations to meet education and training needs. Despite its potential, research regarding VR applications has focused on measuring the effectiveness of VR programs relative to non-VR programs; consequently, the ways in which VR programs are…
Descriptors: Computer Simulation, Training, Program Effectiveness, Training Methods
Ting-Ting Wu; Edi Sarwono; Yueh-Min Huang – Journal of Computer Assisted Learning, 2025
Background: Virtual laboratories are used to supplement or even replace physical laboratories in engineering education. Although these virtual laboratories allow students to learn foundational experimental skills, they do not provide the learners with the chance to develop higher-order thinking skills (HOTS). Computational thinking (CT) is an…
Descriptors: Computation, Thinking Skills, Computer Simulation, Laboratories
Piia Näykki; Saara Pyykkönen; Jenni Latva-aho; Tuula Nousiainen; Emilia Ahlström; Tapio Toivanen – Journal of Computer Assisted Learning, 2024
Background: In recent years, the use of virtual reality (VR) environments for education has gained interest in research and education. However, little is known about the potential of social VR environments for collaborative learning. Objectives: This study explores pre-service teachers' (PSTs') collaborative learning and role-based drama activity,…
Descriptors: Computer Simulation, Cooperative Learning, Preservice Teachers, Drama
Qin Wang; Yanping Li – Journal of Computer Assisted Learning, 2024
Background: Virtual reality (VR), augmented reality (AR) and mixed reality (MR) have sparked recently in improving the effectiveness of teacher education. However, there is a lack of review regarding the utilisation of these technologies in this field. These three technologies, namely VR, AR and MR, can be collectively referred to as extended…
Descriptors: Computer Simulation, Technology Uses in Education, Teaching Methods, Educational Improvement
Alyssa P. Lawson; Richard E. Mayer – Journal of Computer Assisted Learning, 2024
Background: Immersive virtual reality (IVR) is a new technology that could motivate learners, but also could contain distracting elements that increase cognitive demands on learners. In contrast, learning with conventional media, such as a narrated slideshow could be less motivating, but also less distracting. Objectives: This experiment…
Descriptors: Computer Simulation, Individual Differences, Learning, Executive Function
Radovan Šikl; Karla Brücknerová; Hana Švedová; Filip Dechterenko; Pavel Ugwitz; Jirí Chmelík; Hana Pokorná; Vojtech Jurík – Journal of Computer Assisted Learning, 2024
Introduction: Media comparison studies examining the effectiveness of immersive virtual reality in education have yielded inconclusive findings, leaving the question of its impact on learning compared to conventional media unanswered. To address this issue, our study employs a novel approach that combines media comparison with an investigation on…
Descriptors: Computer Simulation, Educational Technology, Secondary School Students, Topography
Meiyan Huang; Tang Yongquan – Journal of Computer Assisted Learning, 2025
Aim: In recent years, the integration of cutting-edge technology into professional sports training (ST) has revolutionized the way athletes prepare for competition. The study aims to quantitatively analyse the impact of cutting-edge technology on enhancing performance, efficiency, and outcomes in professional ST programs. Purpose: The purpose of…
Descriptors: Athletics, Technology, Influence of Technology, Training
Cynthia Y. Delgado; Richard E. Mayer – Journal of Computer Assisted Learning, 2025
Background: In recent years, immersive virtual reality in education has garnered attention, however, there have been mixed findings on the efficacy of IVR in education. Thus, exploring which strategies are effective in transferring learning from IVR to real-world applications is imperative. Objective: This study aims to investigate the efficacy of…
Descriptors: Computer Simulation, Experiential Learning, Instructional Effectiveness, Transfer of Training
Alejandra Ruiz-Segura; Andrew Law; Sion Jennings; Alain Bourgon; Ethan Churchill; Susanne Lajoie – Journal of Computer Assisted Learning, 2024
Background: Flying accuracy is influenced by pilots' affective reactions to task demands. A better understanding of task-related emotions and flying performance is needed to enhance pilot training. Objective: Understand pilot trainees' performance and emotional dynamics (intensity, frequency and variability) based on training phase and difficulty…
Descriptors: Foreign Countries, Flight Training, Aviation Technology, Computer Simulation
Daniela Decker; Martin Merkt – Journal of Computer Assisted Learning, 2024
Background: Virtual reality (VR) offers much potential for learning, but it challenges learners' orientation. Objectives: This paper investigates whether it is possible to use light or movement cues to facilitate orientation in a search task in a desktop-VR environment so that participants can better attend to the learning content presented…
Descriptors: Cues, Educational Technology, Computer Simulation, Light
Burç Çeken; Nazim Taskin – Journal of Computer Assisted Learning, 2025
Background: Multimedia learning, encapsulating both visual and verbal information, has become pivotal in educational settings, with extensive research underscoring its influence. However, a notable research gap exists concerning the application and effectiveness of multimedia learning principles, precisely segmenting, pre-training and modality,…
Descriptors: Multimedia Instruction, Computer Simulation, Educational Environment, Instructional Effectiveness
Eleni Dermentzi – Journal of Computer Assisted Learning, 2024
Background: Degree apprenticeships (DA) help employees gain new knowledge and skills by allowing them to study towards a university degree while working full time. For these programmes to be effective, the learning design should be adapted to the needs of these students, who are goal oriented (i.e. they want to gain only knowledge that can be…
Descriptors: Foreign Countries, Apprenticeships, Game Based Learning, Flipped Classroom
Hans G. K. Hummel; Rob Nadolski; Hugo Huurdeman; Giel van Lankveld; Konstantinos Georgiadis; Aad Slootmaker; Hub Kurvers; Mick Hummel; Petra Neessen; Johan van den Boomen; Ron Pat-El; Julia Fischmann – Journal of Computer Assisted Learning, 2024
Background: Complex skills, like analytical thinking, are essential in preparing students for future professions. Serious games hold potential to stimulate the online acquisition of such professional skills in an active and experiential way. Objective: Rubrics are proven assessment and evaluation instruments, but were never directly integrated…
Descriptors: Game Based Learning, Scoring Rubrics, Educational Games, Computer Simulation
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