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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
Yuxiao Xie; Ziyi Xie; Siyu Chen; Lei Shen; Zhizhuang Duan – Education and Information Technologies, 2025
The National College English Test Band 4 (CET-4) is a key test to assess the English language ability of Chinese university students, and the success rate of the test is important to improve the quality of their English learning. Artificial intelligence technology can be used to predict and explore the factors influencing the success rate. This…
Descriptors: Language Tests, English (Second Language), Second Language Learning, Second Language Instruction
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
Lim, Lisa-Angelique; Dawson, Shane; Gaševic, Dragan; Joksimovic, Srecko; Pardo, Abelardo; Fudge, Anthea; Gentili, Sheridan – Assessment & Evaluation in Higher Education, 2021
Research and development in learning analytics has established viable solutions for scaling personalised feedback to all students. However, questions remain regarding how such feedback is perceived, interpreted and acted upon by stakeholders. The present study reports on the analysis of focus group data from four courses to understand students'…
Descriptors: Student Attitudes, College Students, Emotional Response, Individualized Instruction
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
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
Whitcomb, Kyle M.; Guthrie, Matthew W.; Singh, Chandralekha; Chen, Zhongzhou – Physical Review Physics Education Research, 2021
In two earlier studies, we developed a new method to measure students' ability to transfer physics problem-solving skills to new contexts using a sequence of online learning modules, and implemented two interventions in the form of additional learning modules designed to improve transfer ability. The current paper introduces a new data analysis…
Descriptors: Accuracy, Measurement Techniques, Electronic Learning, Learning Modules