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Miao Chao; Weiyi Sun; Jie Liu; Jiahui Ding; Ye Zhu – Journal of Computer Assisted Learning, 2025
Background: The use of social media among students has become debatable concern due to both positive and negative effects on academic performance. Yet, understanding of the diverse patterns of social media use and their influence on actual and perceived academic performance remains limited. Objectives: This study distinguishes between academic and…
Descriptors: Social Media, Performance, Influence of Technology, Predictor Variables
Blaženka Divjak; Barbi Svetec; Damir Horvat – Journal of Computer Assisted Learning, 2024
Background: Sound learning design should be based on the constructive alignment of intended learning outcomes (LOs), teaching and learning activities and formative and summative assessment. Assessment validity strongly relies on its alignment with LOs. Valid and reliable formative assessment can be analysed as a predictor of students' academic…
Descriptors: Automation, Formative Evaluation, Test Validity, Test Reliability
Muhammad Ashraf Fauzi; Zuria Akmal Saad; Muhammad Ariff Aripin; Noraina Mazuin Sapuan – Journal of Computer Assisted Learning, 2025
Background Study: Gamification is an effective and interactive approach to engaging students in teaching and learning. As digital technology develops, higher education institutions (HEIs) must embrace and keep pace with gamification for interactive teaching and learning approaches. Objective: The purpose of this study is to review gamification in…
Descriptors: State of the Art Reviews, Gamification, Game Based Learning, Bibliometrics
Lars de Vreugd; Anouschka van Leeuwen; Marieke van der Schaaf – Journal of Computer Assisted Learning, 2025
Background: University students need to self-regulate but are sometimes incapable of doing so. Learning Analytics Dashboards (LADs) can support students' appraisal of study behaviour, from which goals can be set and performed. However, it is unclear how goal-setting and self-motivation within self-regulated learning elicits behaviour when using an…
Descriptors: Learning Analytics, Educational Technology, Goal Orientation, Learning Motivation
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
Lihui Sun; Danhua Zhou – Journal of Computer Assisted Learning, 2024
Background: Integrating programming in K-12 curriculum has become a global consensus. Teachers are central figures in programming instruction. But the majority of current research focuses on teachers' external teaching behaviours and less on teachers' attitudes towards programming. Objectives: The purpose of this study is to validate the K-12…
Descriptors: Foreign Countries, Elementary School Teachers, Secondary School Teachers, Teacher Attitudes
Spitzer, Markus Wolfgang Hermann; Moeller, Korbinian – Journal of Computer Assisted Learning, 2022
Background: Mastering fractions seems among the most critical mathematical skills for students to acquire in school as fraction understanding significantly predicts later mathematic achievements, but also broader academic and vocational prospects. As such, identifying longitudinal predictors of fraction understanding (e.g., mastery of numbers and…
Descriptors: Fractions, Mathematics Instruction, Mathematics Skills, Predictor Variables
Hui Shi; Nuodi Zhang; Secil Caskurlu; Hunhui Na – Journal of Computer Assisted Learning, 2025
Background: The growth of online education has provided flexibility and access to a wide range of courses. However, the self-paced and often isolated nature of these courses has been associated with increased dropout and failure rates. Researchers employed machine learning approaches to identify at-risk students, but multiple issues have not been…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, At Risk Students
Okan Bulut; Guher Gorgun; Seyma Nur Yildirim-Erbasli – Journal of Computer Assisted Learning, 2025
Background: Research shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, leading to better tracking of learning progress.…
Descriptors: Formative Evaluation, Academic Achievement, Student Participation, Learning Processes
Minkai Wang; Jingdong Zhu; Gwo-Jen Hwang; Shao-Chen Chang; Qi-Fan Yang; Di Zhang – Journal of Computer Assisted Learning, 2025
Background: STEM education aims to develop innovation and problem-solving skills through interdisciplinary learning, yet struggles to foster student engagement and interdisciplinary thinking. Whilst alternate reality games (ARGs) can boost motivation via game-based problem-solving, integrating large language models (LLMs) remains underexplored.…
Descriptors: Learner Engagement, STEM Education, Natural Language Processing, Artificial Intelligence
Hui-Tzu Hsu; Chih-Cheng Lin – Journal of Computer Assisted Learning, 2024
Background: Behavioural intention (BI) has been predicted using other variables by adopting the technology acceptance model (TAM). However, few studies have examined whether BI can predict learning performance. Objectives: The present study used an extended TAM to investigate whether students' BI is a predictor of their listening learning…
Descriptors: Intention, Vocabulary Development, Handheld Devices, College Students
Alam, Sabrina Shajeen; Dubé, Adam Kenneth – Journal of Computer Assisted Learning, 2023
Background: A strong knowledge of mathematics, beginning at the elementary level, is critical for participation in today's complex world. The home may be one way to facilitate individualized mathematics instruction, given that children spend more time at home than in an academic institution. Therefore, researchers are interested to see whether the…
Descriptors: Foreign Countries, Elementary School Students, Numeracy, Family Environment
Yu Cui; Lingjie Tang; Fang Fang – Journal of Computer Assisted Learning, 2025
Background Study: With the rapid transition to remote learning necessitated by the closure of traditional educational infrastructures globally, the arena of informal digital learning of English (IDLE) has received much attention, particularly among English as a Foreign Language (EFL) learners in China. Objective: This study explores how…
Descriptors: Electronic Learning, Artificial Intelligence, Predictor Variables, Informal Education
Chen, Yi-Ching; Chang, Yu-Shan; Chuang, Meng-Jung – Journal of Computer Assisted Learning, 2022
Virtual reality (VR) can promote design performance, and may generate a high cognitive load and affect creative design thinking as well. In order to examine the effect of VR application on cognitive load and engineering design creativity, this study recruited 81 eighth-grade students as participants and employed a non-equivalent-groups…
Descriptors: Computer Simulation, Cognitive Processes, Difficulty Level, Creative Thinking
Niu, Liwei; Wang, Xinghua; Wallace, Matthew P.; Pang, Hui; Xu, Yanping – Journal of Computer Assisted Learning, 2022
Background: In view of the widespread use of digital technologies in English as a foreign language (EFL) learning and the importance of students' approaches to learning (SAL) and digital competence, as well as the threats of technostress in digital settings, digital EFL learning requires a critical examination. Objectives: This study sought to…
Descriptors: English (Second Language), Educational Technology, Electronic Learning, Second Language Learning

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