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Yang, Xi; Zhou, Guojing; Taub, Michelle; Azevedo, Roger; Chi, Min – International Educational Data Mining Society, 2020
In the learning sciences, heterogeneity among students usually leads to different learning strategies or patterns and may require different types of instructional interventions. Therefore, it is important to investigate student subtyping, which is to group students into subtypes based on their learning patterns. Subtyping from complex student…
Descriptors: Grouping (Instructional Purposes), Learning Strategies, Artificial Intelligence, Learning Analytics
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)
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
Pond, Jarrad W. T.; Chini, Jacquelyn J. – Physical Review Physics Education Research, 2017
In this study, we explore the strategic self-regulatory and motivational characteristics of students in studio-mode physics courses at three universities with varying student populations and varying levels of success in their studio-mode courses. We survey students using questions compiled from several existing questionnaires designed to measure…
Descriptors: Algebra, Physics, Profiles, Student Characteristics
Barata, Gabriel; Gama, Sandra; Jorge, Joaquim; Gonçalves, Daniel – International Journal of Game-Based Learning, 2014
Gamification of education is a recent trend, and early experiments showed promising results. Students seem not only to perform better, but also to participate more and to feel more engaged with gamified learning. However, little is known regarding how different students are affected by gamification and how their learning experience may vary. In…
Descriptors: Educational Games, Learning Experience, College Students, Learning Strategies
Aagaard, Lola; Skidmore, Ronald L.; Conner, Timothy W., II – Online Submission, 2014
The purpose of this study was to investigate the relationship between academic self-efficacy and preferences regarding the use of text materials and in-class activities of college students at a university that serves one of the highest-poverty regions in the United States. A convenient cluster sample of 105 students taking summer classes at a…
Descriptors: College Students, Self Efficacy, Textbooks, Preferences
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