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Gulnur Tyulepberdinova; Madina Mansurova; Talshyn Sarsembayeva; Sulu Issabayeva; Darazha Issabayeva – Journal of Computer Assisted Learning, 2024
Background: This study aims to assess how well several machine learning (ML) algorithms predict the physical, social, and mental health condition of university students. Objectives: The physical health measurements used in the study include BMI (Body Mass Index), %BF (percentage of Body Fat), BSC (Blood Serum Cholesterol), SBP (Systolic Blood…
Descriptors: Artificial Intelligence, Algorithms, Predictor Variables, Physical Health
Daniels, Lia M.; Bulut, Okan – Journal of Computer Assisted Learning, 2020
In computer-based testing (CBT) environments instructors can provide students with feedback immediately. Commonly, instructors give students their percentage correct without additional descriptive feedback. Our objectives were (a) to compare students' perceived usefulness of a percentage-only score report vs. a descriptive feedback report in a CBT…
Descriptors: Computer Assisted Testing, Feedback (Response), Value Judgment, Student Attitudes
Veerbeek, Jochanan; Vogelaar, Bart; Verhaegh, Janneke; Resing, Wilma C. M. – Journal of Computer Assisted Learning, 2019
Task solving processes and changes in these processes have long been expected to provide valuable information about children's performance in school. This article used electronic tangibles (concrete materials that can be physically manipulated) and a dynamic testing format (pretest, training, and posttest) to investigate children's task solving…
Descriptors: Young Children, Pretests Posttests, Problem Solving, Outcomes of Education