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Direct linkGulnur 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
Li, Z. – Journal of Computer Assisted Learning, 2013
Much of the research in educational technology with a primary concern over how technology enhances learning has been criticized as privileging the immediate learning settings over the other dimensions of learners' social life and the wider social and economic contexts in which learning and technology are located. The ability to develop a rich…
Descriptors: Foreign Countries, Electronic Learning, Educational Theories, Theory Practice Relationship
Stevenson, O. – Journal of Computer Assisted Learning, 2011
Informed by "critical" approaches to "educational technology", this paper aims to move away from presenting a "could" and "should" explanation of children learning with technology to a more nuanced, context-rich analyses of how information and communication technologies (ICTs) are being used by technologically privileged families at home. Here, a…
Descriptors: Social Life, Family Life, Educational Technology, Public Policy
