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Ji Won You – Studies in Higher Education, 2024
Team project-based learning has become increasingly common in higher education. This study aimed to characterise and understand students' team learning experiences in team project-based learning by considering various aspects, such as individual qualities, teamwork, task, and instructor support. K-means clustering analysis was performed using…
Descriptors: Cooperative Learning, Profiles, Outcomes of Education, Student Projects
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Howlin, Colm P.; Dziuban, Charles D. – International Educational Data Mining Society, 2019
Clustering of educational data allows similar students to be grouped, in either crisp or fuzzy sets, based on their similarities. Standard approaches are well suited to identifying common student behaviors; however, by design, they put much less emphasis on less common behaviors or outliers. The approach presented in this paper employs fuzzing…
Descriptors: Data Collection, Student Behavior, Learning Strategies, Feedback (Response)
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Karagiannopoulou, Evangelia; Milienos, Fotios S.; Kamtsios, Spiridon; Rentzios, Christos – Educational Psychology, 2020
The study aims at investigating students' learning/defence profiles. It also explores students' profiles during different years of study. Participants comprised of 425 undergraduates. They completed the 'Approaches to Study and Skills Inventory' and the 'Defense Style Questionnaire'. The students' academic achievement was measured through grade…
Descriptors: Cognitive Style, Profiles, Measures (Individuals), Study Habits
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Arora, Skand; Goel, Manav; Sabitha, A. Sai; Mehrotra, Deepti – American Journal of Distance Education, 2017
The open nature of Massive Open Online Courses (MOOCs) attracts a large number of learners with different backgrounds, skills, motivations, and goals. This has brought a need to understand such heterogeneity in populations of MOOC learners. Categorizing these learners based upon their interaction with the course can help address this need and…
Descriptors: Online Courses, Heterogeneous Grouping, Learner Engagement, Student Characteristics
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Balint, Trevor A.; Teodorescu, Raluca; Colvin, Kimberly; Choi, Youn-Jeng; Pritchard, David – Physics Teacher, 2017
In this paper we examine how different types of participants in a physics Massive Open Online Course (MOOC) tend to use the existing course resources. We use data from the 2013 offering of the Massive Open Online Course 8.MReVx designed by the RELATE (REsearch in Learning Assessing and Tutoring Effectively) Group at the Massachusetts Institute of…
Descriptors: Physics, Online Courses, Instructional Materials, Educational Resources
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Shimada, Atsushi; Mouri, Kousuke; Taniguchi, Yuta; Ogata, Hiroaki; Taniguchi, Rin-ichiro; Konomi, Shin'ichi – International Educational Data Mining Society, 2019
In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students'…
Descriptors: Student Placement, Learning Activities, Learning Analytics, Cognitive Style
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Hu, Yiling; Wu, Bian; Gu, Xiaoqing – Interactive Learning Environments, 2017
Problem solving is considered a fundamental human skill. However, large-scale assessment of problem solving in K-12 education remains a challenging task. Researchers have argued for the development of an enhanced assessment approach through joint effort from multiple disciplines. In this study, a three-stage approach based on an evidence-centered…
Descriptors: Problem Solving, Online Courses, Elementary Secondary Education, Evidence Based Practice
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List, Alexandra; Grossnickle, Emily M.; Alexander, Patricia A. – Reading Psychology, 2016
The present study examined undergraduate students' multiple source use in response to two different types of academic questions, one discrete and one open-ended. Participants (N = 240) responded to two questions using a library of eight digital sources, varying in source type (e.g., newspaper article) and reliability (e.g., authors' credentials).…
Descriptors: Profiles, Questioning Techniques, Information Sources, Undergraduate Students
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Chan, Julia Y. K.; Bauer, Christopher F. – Chemistry Education Research and Practice, 2016
Students in general chemistry were partitioned into three groups by cluster analysis of six affective characteristics (emotional satisfaction, intellectual accessibility, chemistry self-concept, math self-concept, self-efficacy, and test anxiety). The at-home study strategies for exam preparation and in-class learning strategies differed among the…
Descriptors: Learning Strategies, Cognitive Style, Chemistry, Affective Behavior