LEARNING STYLE IDENTIFICATION (Learning Style Identification Using Semi-Supervised Self-Taught Labeling)

Introduced by Ayyoub et al. in Learning Style Identification Using Semi-Supervised Self-Taught Labeling

The dataset was collected from two courses offered on the University of Jordan's E-learning Portal during the second semester of 2020, namely "Computer Skills for Humanities Students" (CSHS) and "Computer Skills for Medical Students" (CSMS). Over the sixteen-week duration of each course, students participated in various activities such as reading materials, video lectures, assignments, and quizzes. To preserve student privacy, the log activity of each student was anonymized. Data was aggregated from multiple sources, including the Moodle learning management system and the student information system, and consolidated into a single database. The dataset contains information on the number of learners and events for each course, as well as their launch and end dates. CSHS had 1749 learners and 1,139,810 events from January 21, 2020 to May 20, 2020, while CSMS had 564 learners and 484,410 events during the same period. The dataset is based on the Filder and Silverman learning style model (FSLSM), which captures students' preferences regarding each dimension of the model. At the beginning of each course, students completed the Index of Learning Styles (ILS) questionnaire, a 44-item questionnaire that gauges learners' personal preferences for each dimension by assigning values ranging from -11 to +11 per dimension based on the responses to eleven questions per dimension. The ILS questionnaire allowed us to capture students' preferences regarding the FSLSM dimensions.

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