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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
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
Alexandron, Giora; Wiltrout, Mary Ellen; Berg, Aviram; Gershon, Sa'ar Karp; Ruipérez-Valiente, José A. – Journal of Computer Assisted Learning, 2023
Background: Massive Open Online Courses (MOOCs) have touted the idea of democratizing education, but soon enough, this utopian idea collided with the reality of finding sustainable business models. In addition, the promise of harnessing interactive and social web technologies to promote meaningful learning was only partially successful. And…
Descriptors: MOOCs, Evaluation, Models, Learner Engagement
Biedermann, Daniel; Schneider, Jan; Drachsler, Hendrik – Journal of Computer Assisted Learning, 2021
Digital distractions can interfere with goal attainment and lead to undesirable habits that are hard to get red rid of. Various digital self-control interventions promise support to alleviate the negative impact of digital distractions. These interventions use different approaches, such as the blocking of apps and websites, goal setting, or…
Descriptors: Self Control, Intervention, Technology Uses in Education, Literature Reviews
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
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
Gökçearslan, Sahin; Yildiz Durak, Hatice; Esiyok, Elif – Journal of Computer Assisted Learning, 2023
Background: The COVID-19 pandemic has spread quickly, e-learning became compulsory and disseminated throughout the world. During the pandemic, smartphones are frequently used to access e-learning content, but connecting to technological tools increased the risk of cyberloafing during e-courses. Currently, there are a limited number of studies on…
Descriptors: Students, Emotional Response, Psychological Patterns, Self Management
Luo, Yi Fang; Yang, Shu Ching; Lu, Chia Mei – Journal of Computer Assisted Learning, 2021
Information technology provides the potential for polychronic learning. However, research on polychronicity in the educational field is scarce. The purposes of this study were to develop a multidimensional polychronicity scale for information technology learning and explore the relationship between polychronicity in information…
Descriptors: Information Technology, Measures (Individuals), Electronic Learning, Time Management
Aydin Bulut; Mustafa Yildiz – Journal of Computer Assisted Learning, 2024
Background: The use of computer-assisted reading comprehension is of critical importance in the context of promoting effective and engaging literacy education in the digital age. It provides students with the opportunity to work at their own pace and convenience, thereby facilitating self-directed learning and accommodating various learning…
Descriptors: Computer Assisted Instruction, Direct Instruction, Reading Comprehension, Technology Uses in Education
Li, Shuang; Wang, Shuang; Du, Junlei; Pei, Yu; Shen, Xinyi – Journal of Computer Assisted Learning, 2022
Background: Failure to effectively organize and manage learning time is an important factor influencing online learners' performance. Investigation of time-investment patterns for online learning will provide educators with useful knowledge of how learners engage in and regulate their online learning and support them in tailoring online course…
Descriptors: Online Courses, Time Management, Time Factors (Learning), Learning Strategies
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