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Nielsen, Sara E.; Yezierski, Ellen J. – Chemistry Education Research and Practice, 2016
Academic tracking, placing students in different classes based on past performance, is a common feature of the American secondary school system. A longitudinal study of secondary students' chemistry self-concept scores was conducted, and one feature of the study was the presence of academic tracking. Though academic tracking is one way to group…
Descriptors: Cluster Grouping, Chemistry, Self Concept, Secondary School Students
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
Costa, Carolina; Alvelos, Helena; Teixeira, Leonor – Technology, Pedagogy and Education, 2016
This study analyses and compares the use of Web 2.0 tools by students in both learning and leisure contexts. Data were collected based on a questionnaire applied to 234 students from the University of Aveiro (Portugal) and the results were analysed by using descriptive analysis, paired samples t-tests, cluster analyses and Kruskal-Wallis tests.…
Descriptors: Foreign Countries, Web 2.0 Technologies, College Students, Questionnaires

Koslowsky, Meni – Educational and Psychological Measurement, 1979
Recent trends in the analysis of categorical or nominal variables were discussed for univariate, multivariate, and psychometric problems. It was shown that several statistical procedures commonly used with these problems have analogues which can be applied to assessing categorical variables. (Author/CTM)
Descriptors: Classification, Cluster Grouping, Correlation, Discriminant Analysis
Gierl, Mark J.; Leighton, Jacqueline P.; Tan, Xuan – Journal of Educational Measurement, 2006
DETECT, the acronym for Dimensionality Evaluation To Enumerate Contributing Traits, is an innovative and relatively new nonparametric dimensionality assessment procedure used to identify mutually exclusive, dimensionally homogeneous clusters of items using a genetic algorithm ( Zhang & Stout, 1999). Because the clusters of items are mutually…
Descriptors: Program Evaluation, Cluster Grouping, Evaluation Methods, Multivariate Analysis