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Nick Hopwood; Tracey-Ann Palmer; Gloria Angela Koh; Mun Yee Lai; Yifei Dong; Sarah Loch; Kun Yu – International Journal of Research & Method in Education, 2025
Student emotions influence assessment task behaviour and performance but are difficult to study empirically. The study combined qualitative data from focus group interviews with 22 students and 4 teachers, with quantitative real-time learning analytics (facial expression, mouse click and keyboard strokes) to examine student emotional engagement in…
Descriptors: Psychological Patterns, Student Evaluation, Learning Analytics, Learner Engagement
Yang, Tzu-Chi; Chen, Sherry Y. – Interactive Learning Environments, 2023
Individual differences exist among learners. Among various individual differences, cognitive styles can strongly predict learners' learning behavior. Therefore, cognitive styles are essential for the design of online learning. There are a variety of cognitive style dimensions and overlaps exist among these dimensions. In particular, Witkin's field…
Descriptors: Student Behavior, Educational Technology, Electronic Learning, Cognitive Style
Shihui Feng; David Gibson; Dragan Gaševic – Journal of Learning Analytics, 2025
Understanding students' emerging roles in computer-supported collaborative learning (CSCL) is critical for promoting regulated learning processes and supporting learning at both individual and group levels. However, it has been challenging to disentangle individual performance from group-based deliverables. This study introduces new learning…
Descriptors: Computer Assisted Instruction, Cooperative Learning, Student Role, Learning Analytics
Krumm, Andrew; Everson, Howard T.; Neisler, Julie – Journal of Learning Analytics, 2022
This paper describes a partnership-based approach for analyzing data from a learning management system (LMS) used by students in grades 6-12. The goal of the partnership was to create indicators for the ways in which students navigated digital learning activities, referred to as playlists, that were comprised of resources, pre-assessments, and…
Descriptors: Learning Management Systems, Data Analysis, Electronic Learning, Student Behavior
Yury Rishko; Diana Boboshko; Evgeniya Eliseeva; Aleksandr Malkin; Dmitrii Treistar – SAGE Open, 2025
Discussion of the effectiveness of distance learning as a means of delivering higher education programs at classical universities has been ongoing for the past decade. The article presents the findings of a study of changes in academic performance of university students, covering the period from fall 2018 to fall 2023. This period included a rapid…
Descriptors: Outcomes of Education, Educational Change, Electronic Learning, Online Courses
Frick, Theodore W.; Myers, Rodney D.; Dagli, Cesur – Educational Technology Research and Development, 2022
In this naturalistic design-research study, we tracked 172,417 learning journeys of students who were interacting with an online resource, the Indiana University Plagiarism Tutorials and Tests (IPTAT) at https://plagiarism.iu.edu. IPTAT was designed using First Principles of Instruction (FPI; Merrill in Educ Technol Res Dev 50:43-59, 2002,…
Descriptors: Time, Educational Principles, Instructional Design, Instructional Effectiveness
Rachel Dickler – ProQuest LLC, 2021
New educational technologies present an opportunity to help teachers monitor and support their students remotely in Science, Technology, Engineering, and Mathematics (STEM). However, most technologies do not have the capacity to comprehensively assess and report on students' critical 21st century practice competencies (such as those described in…
Descriptors: Inquiry, STEM Education, Educational Technology, Distance Education
Wu, Fati; Lai, Song – Distance Education, 2019
Open, flexible and distance learning has become part of mainstream education in China. Using a blended learning program in a Chinese high school as the case, this study adopted data-mining approaches to establish predictive models using personality traits. Results showed that, for students with high OE and low extraversion, and students who are…
Descriptors: Personality Traits, Learning Analytics, Foreign Countries, At Risk Students