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Tanya Chichekian; Joel Trudeau; Tawfiq Jawhar; Dylan Corliss – Journal of Computer Assisted Learning, 2024
Background: Despite its obvious relevance to computer science, computational thinking (CT) is transdisciplinary with the potential of impacting one's analytical ability. Although countless efforts have been invested across K-12 education, there is a paucity of research at the postsecondary level about the extent to which CT can contribute to…
Descriptors: College Students, Computation, Thinking Skills, Transfer of Training
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
Zhongling Pi; Huixin Chai; La Li; Xinru Zhang; Xiying Li – Journal of Computer Assisted Learning, 2024
Background: Learning from video lectures with peers, that is, co-viewing video lectures, is a common mode of learning across a wide range of ages and topics in the information age. Objectives: The present study tested the effects of learners' motivation on co-viewing video lectures in terms of learning performance, mental effort, and interpersonal…
Descriptors: Learning Motivation, Cooperative Learning, Video Technology, Lecture Method
Pieter Vanneste; Kim Dekeyser; Luis Alberto Pinos Ullauri; Dries Debeer; Frederik Cornillie; Fien Depaepe; Annelies Raes; Wim Van den Noortgate; Sameh Said-Metwaly – Journal of Computer Assisted Learning, 2024
Background: Augmented reality (AR) is receiving increasing interest as a tool to create an interactive and motivating learning environment. Yet, it is unclear how instructional support affects performance in AR. Objectives: This study sought to explore how varying the instructional support in AR can affect performance-related behaviours of…
Descriptors: Computer Simulation, Artificial Intelligence, Cognitive Ability, Student Behavior
Katharina Alexandra Whalen; Alexander Renkl; Alexander Eitel; Inga Glogger-Frey – Journal of Computer Assisted Learning, 2024
Background: Students often show unfavourable attribution: they attribute poor school performance to stable factors such as lack of ability and good school performance to variable factors such as effort. However, attribution can be influenced by individualized digital re-attributional feedback leading to positive motivational effects and higher…
Descriptors: Feedback (Response), Computer Mediated Communication, Secondary School Mathematics, Student Motivation
Elena Drugova; Irina Zhuravleva; Ulyana Zakharova; Adel Latipov – Journal of Computer Assisted Learning, 2024
Background: Driven by the ongoing need to provide high-quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to…
Descriptors: Learning Analytics, Instructional Design, Higher Education, Instructional Improvement
Xiaojing Liu; Chunmiao Zhou – Journal of Computer Assisted Learning, 2024
Background: The global introduction of complex measures directed at the containment of the COVID-19 spread has spurred a massive shift to distance learning among educational institutions. As far as such a learning mode is rather forced and, probably, only a few establishments faced no difficulties with it, the matter of assuring teaching…
Descriptors: Teacher Role, Educational Technology, Technology Uses in Education, Distance Education
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
Chak-Him Fung; Kin-Keung Poon; Michael Besser; Ming-Chung Fung – Journal of Computer Assisted Learning, 2024
Background Study: The effects of the flipped classroom (FC) on students' academic performance remain ambiguous, and the use of pre-class videos may be one of the main factors hindering students' progress. A software package called GeoGebra has been proposed as a substitute for pre-class videos to aid students' learning. Objective: This study…
Descriptors: Flipped Classroom, Computer Software, Academic Achievement, Geometry
Ziyi Kuang; Fuxing Wang; Frank Andrasik; Xiangen Hu – Journal of Computer Assisted Learning, 2024
Background: Little is known about the effectiveness of instructors when presenting content in videos alone. In recent years, researchers have increasingly begun to explore the effects of instructors' social cues (e.g., eye gaze, body orientation, etc.) on learning. However, previous studies exploring the effects of eye gaze have confounded the…
Descriptors: Teacher Behavior, Eye Movements, Human Body, Teacher Effectiveness
Yuko Suzuki; Fridolin Wild; Eileen Scanlon – Journal of Computer Assisted Learning, 2024
Background: Cognitive load during AR use has been measured conventionally by performance tests and subjective rating. With the growing interest in physiological measurement using non-invasive biometric sensors, unbiased real-time detection of cognitive load in AR is expected. However, a range of sensors and parameters are used in various subject…
Descriptors: Computer Simulation, Cognitive Processes, Difficulty Level, Physiology
Sophie Gruhn; Eliane Segers; Jos Keuning; Ludo Verhoeven – Journal of Computer Assisted Learning, 2024
Background: Reading comprehension is an interactive process. Yet, instructional needs are usually identified with isolated componential tests. This study examined whether a dynamic approach, in which componential abilities are measured within the same text and global text comprehension is facilitated via feedback, can help in understanding…
Descriptors: Elementary School Students, Reading Comprehension, Feedback (Response), Reading Tests
Mugur V. Geana; Dan Cernusca; Pan Liu – Journal of Computer Assisted Learning, 2024
Background: Education is, after gaming, the second largest sector embracing augmented reality (AR) at an accelerated pace, yet studies on AR's potential as an efficient learning environment had mixed results. Objectives: This study's primary objective is to test students' interaction with graphical 3D elements in AR and its impact on information…
Descriptors: Learner Engagement, Computer Simulation, Technology Uses in Education, Information Dissemination
Hatice Yildiz Durak – Journal of Computer Assisted Learning, 2024
Background: Collaboration is a crucial concept in learning and has the potential to foster learning. However, the fact that collaborative groups act with a common understanding in a common task brings many difficulties. Therefore, there is a need for group regulation and guidance to support effective group regulation in collaborative learning. On…
Descriptors: Feedback (Response), Groups, Group Guidance, Cooperation
Tiphaine Colliot; Jean-Michel Boucheix – Journal of Computer Assisted Learning, 2024
Background: Previous studies have shown that dynamic illustrations, as compared to their static counterparts, lead to higher achievement levels, especially for hand-based procedures. Other researchers have investigated how the presence of seductive details (i.e., appealing but irrelevant adjunct displays) influences students' interest positively…
Descriptors: Illustrations, Animation, Handicrafts, Elementary School Students