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Weijuan Li – European Journal of Education, 2025
In recent years, the integration of big data and learning analytics has emerged as a significant trend across educational systems worldwide. The implementation of such technologies within universities -- particularly in China -- holds considerable potential for transforming teaching and learning practices. By enabling personalised, data-driven…
Descriptors: Universities, Learning Analytics, Educational Practices, Foreign Countries
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Marek Hatala; Sina Nazeri – Journal of Learning Analytics, 2024
An essential part of making dashboards more effective in motivating students and leading to desirable behavioural change is knowing what information to communicate to the student and how to frame and present it. Most of the research studying dashboards' impact on learning analyzes learning indicators of students as a group. Understanding how a…
Descriptors: Educational Technology, Information Dissemination, Learning Processes, Algorithms
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Xiuyu Lin; Zehui Zhan; Xuebo Zhang; Jiayi Xiong – IEEE Transactions on Learning Technologies, 2024
The attribution of learning success or failure is crucial for students' learning and motivation. Effective attribution of their learning success or failure in the context of a small private online course (SPOC) could generate students' motivation toward learning success while an incorrect attribution would lead to a sense of helplessness. Based on…
Descriptors: Learning Analytics, Learning Processes, Learning Motivation, Attribution Theory
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Sahin, Muhittin; Ifenthaler, Dirk – International Association for Development of the Information Society, 2022
Within digitally-supported learning environments, learners need to observe themselves so that they can reflect on their strengths and weaknesses and take a step toward autonomous learning. Within the scope of this research, a technology and analytics enhanced assessment environment in which students can assess themselves was implemented and…
Descriptors: Foreign Countries, College Students, Behavior Patterns, Learning Processes
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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
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Joseph-Richard, Paul; Uhomoibhi, James; Jaffrey, Andrew – International Journal of Information and Learning Technology, 2021
Purpose: The aims of this study are to examine affective responses of university students when viewing their own predictive learning analytics (PLA) dashboards, and to analyse how those responses are perceived to affect their self-regulated learning behaviour. Design/methodology/approach: A total of 42 Northern Irish students were shown their own…
Descriptors: Prediction, Learning Analytics, Student Behavior, Affective Behavior
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Zhang, Jia-Hua; Zou, Liu-cong; Miao, Jia-jia; Zhang, Ye-Xing; Hwang, Gwo-Jen; Zhu, Yue – Interactive Learning Environments, 2020
Extensive studies have been conducted to diagnose and predict students' academic performance by analyzing a large amount of data related to their learning behaviors in a blended learning environment. But there is a lack of research examining how individualized learning interventions could improve students' academic performance in such a learning…
Descriptors: Individualized Instruction, Academic Achievement, Interaction, Blended Learning