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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
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
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

Eckes, Thomas – Personality and Social Psychology Bulletin, 1995
Used a new statistical procedure to examine consensual beliefs about situations common to universities. This approach aims at constructing a joint hierarchical or non-hierarchical classification of situational categories. Results indicate that a psychologically meaningful and methodologically satisfactory representation of the cognitive…
Descriptors: Associative Learning, Behavior, Cluster Analysis, Cluster Grouping