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Yingbin Zhang; Yafei Ye; Luc Paquette; Yibo Wang; Xiaoyong Hu – Journal of Computer Assisted Learning, 2024
Background: Learning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an…
Descriptors: Learning Analytics, Learning Processes, Test Reliability, Psychometrics
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Saleh Alhazbi; Afnan Al-ali; Aliya Tabassum; Abdulla Al-Ali; Ahmed Al-Emadi; Tamer Khattab; Mahmood A. Hasan – Journal of Computer Assisted Learning, 2024
Background: Measuring students' self-regulation skills is essential to understand how they approach their learning tasks in order to identify areas where they might need additional support. Traditionally, self-report questionnaires and think aloud protocols have been used to measure self-regulated learning skills (SRL). However, these methods are…
Descriptors: Learning Analytics, Independent Study, Higher Education, College Students
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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
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Gruss, Richard; Clemons, Josh – Journal of Computer Assisted Learning, 2023
Background: The sudden growth in online instruction due to COVID-19 restrictions has given renewed urgency to questions about remote learning that have remained unresolved. Web-based assessment software provides instructors an array of options for varying testing parameters, but the pedagogical impacts of some of these variations has yet to be…
Descriptors: Test Items, Test Format, Computer Assisted Testing, Mathematics Tests
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Schmitz, Birgit; Hanke, Katja – Journal of Computer Assisted Learning, 2023
Background: The COVID-19 lockdown forced students and teachers to adjust to remote lecturers and digital learning material and design criteria for online classes became the centre of discussion. Objectives: The purpose of this empirical study was to investigate the relationship between design principles of educational online practices in higher…
Descriptors: Learner Engagement, Expectation, Instructional Effectiveness, Instructional Design
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Tingting Wang; Alejandra Ruiz-Segura; Shan Li; Susanne P. Lajoie – Journal of Computer Assisted Learning, 2024
Background: Scholars have confirmed the vital roles of self-regulated learning (SRL) behaviours in predicting task performance, especially within non-linear technology-rich learning environments (TREs). However, few studies focused on the learning costs (e.g., study effort and time-on-task) related to SRL and the efficiency outcome of SRL (i.e.,…
Descriptors: Problem Solving, Educational Environment, Efficiency, Student Behavior
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Min Young Doo; Yeonjeong Park – Journal of Computer Assisted Learning, 2024
Background: Despite the many advantages of flipped learning, it is challenging for educators to ensure that students complete the pre-class learning assignments before the in-class session. Objectives: Using a learning analytics approach, this study analysed students' pre-class video-watching behaviour in flipped learning with a focus on learners'…
Descriptors: Flipped Classroom, Video Technology, Student Behavior, Learning Strategies
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Maarten Sluijs; Uwe Matzat – Journal of Computer Assisted Learning, 2024
Background: Technological innovations such as Learning Management Systems (LMS) are becoming more and more prevalent in the learning environments of students. Distilling and acting on knowledge gathered from these systems, the field known as learning analytics, allows educators to hone their craft and support students more effectively by providing…
Descriptors: Time Management, Learning Analytics, Learning Management Systems, Predictive Measurement
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Shan Li; Xiaoshan Huang; Gaoxia Zhu; Hanxiang Du; Tianlong Zhong; Chenyu Hou; Juan Zheng – Journal of Computer Assisted Learning, 2024
Background: Social annotation has emerged as a promising educational technology that fosters collaborative reading and discussion of digital resources among learners. While the positive impact of social annotation on students' learning process and performance is widely acknowledged, students' behavioural patterns in social annotation are…
Descriptors: Undergraduate Students, Cooperative Learning, Reading Strategies, Group Activities
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Ameloot, Elise; Rotsaert, Tijs; Schellens, Tammy – Journal of Computer Assisted Learning, 2022
Background: Although blended learning (BL) has multiple educational prospects, it also poses challenges such as keeping students motivated. Objectives: This study investigates students' perceptions of how learning analytics (LA) can be used to support the design of a BL environment in order to promote students' basic need for relatedness, which is…
Descriptors: Learning Analytics, Blended Learning, Student Attitudes, Need Gratification
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Candel, Carmen; Vidal-Abarca, Eduardo; Cerdán, Raquel; Lippmann, Marie; Narciss, Susanne – Journal of Computer Assisted Learning, 2020
This study examines the effects of timing of corrective formative feedback on processing text information on question-answering. Undergraduate students read an expository text and answered questions in two attempts. Students were randomly assigned to a no feedback, immediate feedback and delayed feedback conditions. Students in the feedback…
Descriptors: Time Factors (Learning), Feedback (Response), Computer Assisted Instruction, Undergraduate Students
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Ahmad Uzir, Nora'ayu; Gaševic, Dragan; Matcha, Wannisa; Jovanovic, Jelena; Pardo, Abelardo – Journal of Computer Assisted Learning, 2020
This paper aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. Specifically, an exploratory study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N = 1,134). Trace data about activities were initially coded…
Descriptors: Time Management, Blended Learning, Learning Analytics, Undergraduate Students
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Heo, Heeok; Bonk, Curtis J.; Doo, Min Young – Journal of Computer Assisted Learning, 2021
Background: Due to the global COVID-19 pandemic, online learning became the only way to learn during this unprecedented crisis. This study began with a simple but vital question: What factors influenced the success of online learning during the COVID-19 pandemic with a focus on online learning self-efficacy? Objectives: The purpose of this study…
Descriptors: Learner Engagement, Technology Integration, Technological Literacy, Self Efficacy
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Luciana Maria Cavichioli Gomes Almeida; Stefan Münzer; Tim Kühl – Journal of Computer Assisted Learning, 2024
Background: According to the personalization effect in multimedia learning, the use of personal and possessive pronouns in instructional materials (e.g., 'you' and 'your') is beneficial. However, current research suggests that the personalization effect is inverted for emotionally aversive content (e.g., illnesses). Objective: This study…
Descriptors: Foreign Countries, Health Education, Health Promotion, Information Sources
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Fu, En; Gao, Qiufeng; Wei, Chuqian; Chen, Qianyi; Liu, Yijun – Journal of Computer Assisted Learning, 2021
Smartphone use in learning settings is a common behaviour amongst college students. Building on the theory of consumerism, self-efficacy and addictive behaviours, the current study developed a three-component conceptual framework to understand college students' smartphone use in organizational as well as self-directed learning settings. One…
Descriptors: Foreign Countries, College Students, Handheld Devices, Telecommunications
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