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Showing 1 to 15 of 23 results Save | Export
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Xin Gong; Weiqi Xu; Ailing Qiao; Zhixia Li – Journal of Computer Assisted Learning, 2025
Background: Robot programming can simultaneously cultivate learners' computational thinking (CT) and spatial thinking (ST). However, there is a noticeable gap in research focusing on the micro-level development patterns of learners' CT and ST and their interconnections. Objectives: This study aims to uncover the intricate development patterns and…
Descriptors: Mental Computation, Thinking Skills, Skill Development, Robotics
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Zhihao Cui; Oi-Lam Ng; Morris Siu-yung Jong; Xiaojing Weng – Journal of Computer Assisted Learning, 2025
Background: Amidst the increasing application of online education in the post-COVID era, new challenges in student engagement have emerged. However, most studies on online engagement have adopted macro-level approaches and relied on self-report measures of retrospective engagement. Few have examined micro-level engagement in terms of real-time and…
Descriptors: Middle School Students, Learner Engagement, Attention, Synchronous Communication
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Veugen, Maria Joanna; Gulikers, Judith Theresia Maria; den Brok, Perry – Journal of Computer Assisted Learning, 2022
Introduction: During the COVID-19 lockdown in 2020, teachers had to shift their teaching and assessment to online. Formative assessment (FA) can help teachers to engage, guide and monitor students' (online) learning. However, more knowledge is needed of how teachers could use the full FA process online. Methods: In this study data from 50…
Descriptors: Secondary School Teachers, Formative Evaluation, Student Evaluation, Electronic Learning
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Jia Li; Novera Roihan; Matthew McGravey – Journal of Computer Assisted Learning, 2025
Background: Online learning has become a popular form of education. Prior to the COVID-19 pandemic, online learning was mainly associated with higher education, with an incremental growth at the K-12 level. The pandemic changed this situation rapidly. Online instruction has been increasingly integrated into secondary schools and has significantly…
Descriptors: High School Students, Student Attitudes, Electronic Learning, Student Experience
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Teresa M. Ober; Ying Cheng; Matthew F. Carter; Cheng Liu – Journal of Computer Assisted Learning, 2024
Background: Students' tendencies to seek feedback are associated with improved learning. Yet, how soon this association becomes robust enough to make predictions about learning is not fully understood. Such knowledge has strong implications for early identification of students at-risk for underachievement via digital learning platforms.…
Descriptors: Academic Achievement, Feedback (Response), Student Behavior, At Risk Students
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Eshuis, Elise H.; ter Vrugte, Judith; Anjewierden, Anjo; de Jong, Ton – Journal of Computer Assisted Learning, 2022
Background: Creating concept maps can help students overcome challenges of accurate knowledge monitoring and thus foster learning. However, students' knowledge often contains gaps and misconceptions, even after concept map creation. Theoretically, students could benefit from additional support, but it is unclear whether this might also be the case…
Descriptors: Reflection, Concept Mapping, Knowledge Representation, Instructional Effectiveness
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John L. Nietfeld; Kristin F. Hoffmann – Journal of Computer Assisted Learning, 2024
Background: Goal setting has been established in the literature as a critical component of self-regulated learning and for effective problem-solving. Yet, surprisingly little attention has been focused on goal-directed behaviour in digital game-based learning environments (GBLEs) despite their expanding use in educational contexts. Objectives: The…
Descriptors: Game Based Learning, Educational Environment, Goal Orientation, Electronic Learning
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Zhidkikh, Denis; Saarela, Mirka; Kärkkäinen, Tommi – Journal of Computer Assisted Learning, 2023
Background: Measurement of students' self-regulation skills is an active topic in education research, as effective assessment helps devising support interventions to foster academic achievement. Measures based on event tracing usually require large amounts of data (e.g., MOOCs and large courses), while aptitude measures are often qualitative and…
Descriptors: Independent Study, Junior High School Students, Secondary School Mathematics, Mathematics Education
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Liu, Caihua; Zowghi, Didar; Kearney, Matthew; Bano, Muneera – Journal of Computer Assisted Learning, 2021
Recent years have seen a growing call for inquiry-based learning in science education, and mobile technologies are perceived as increasingly valuable tools to support this approach. However, there is a lack of understanding of mobile technology-supported inquiry-based learning (mIBL) in secondary science education. More evidence-based, nuanced…
Descriptors: Active Learning, Inquiry, Electronic Learning, Technology Integration
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Jon-Chao Hong; Kai-Hsin Tai; Tzu-Yu Tai – Journal of Computer Assisted Learning, 2024
Background: This study was motivated by the need to enhance Mandarin learning through embodied interactions and physical engagement methods. While most studies confirm the positive effects of touchscreen devices on learning, mixed findings suggest a need for more engaging methods. This research addresses these gaps by exploring the benefits of…
Descriptors: Electronic Learning, Computer Oriented Programs, Audience Response Systems, Second Language Learning
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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
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Fangzhou Jin; Xiangmei Peng; Lanfang Sun; Zicong Song; Keyi Zhou; Chin-Hsi Lin – Journal of Computer Assisted Learning, 2025
Background: There are various challenges to teachers' use of generative artificial intelligence (GenAI) for professional learning. Although GenAI is expected to play a transformative role in teachers' learning, its impact on them remains subtle. Objectives: Guided by community of practice, this paper examines the integration of GenAI into an…
Descriptors: Artificial Intelligence, Communities of Practice, Technology Uses in Education, Experienced Teachers
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Menabò, Laura; Sansavini, Alessandra; Brighi, Antonella; Skrzypiec, Grace; Guarini, Annalisa – Journal of Computer Assisted Learning, 2021
Background: The rapid spread of COVID-19 forced many countries to adopt severe containment measures, transferring all didactic activities into virtual environments. However, the integration of technology in teaching may present difficulties, especially in some countries, such as Italy. Objectives: The present study analyzed how the two main…
Descriptors: Technology Integration, Intention, Adoption (Ideas), Electronic Learning
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Blau, Ina; Shamir-Inbal, Tamar; Hadad, Shlomit – Journal of Computer Assisted Learning, 2020
The purpose of this study was to examine the influence of online collaborative learning experiences on students' digital collaboration skills and on the sustainability of e-collaboration in schools' culture--comparing individualistic versus collectivistic cultures. In addition, we explored how the leadership experience of schools' ICT coordinators…
Descriptors: Electronic Learning, Cooperative Learning, Individualism, Collectivism
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Truzoli, Roberto; Pirola, Veronica; Conte, Stella – Journal of Computer Assisted Learning, 2021
The lockdown due to COVID-19 in Italy resulted in the sudden closure of schools, with a shift from traditional teaching to the online one. Through an online questionnaire, this survey explores teachers' experience of online teaching, the level of risk factors (e.g., stress) and protective factors (e.g., locus of control) and their impact on…
Descriptors: Risk, Stress Variables, Depression (Psychology), Mental Health
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