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Download full textGitinabard, Niki; Barnes, Tiffany; Heckman, Sarah; Lynch, Collin F. – International Educational Data Mining Society, 2019
Students' interactions with online tools can provide us with insights into their study and work habits. Prior research has shown that these habits, even as simple as the number of actions or the time spent on online platforms can distinguish between the higher performing students and low-performers. These habits are also often used to predict…
Descriptors: Blended Learning, Student Adjustment, Online Courses, Study Habits
Weitzman, R. A. – 1982
The goal of this research was to predict from a recruit's responses to the Armed Services Vocational Aptitude Battery (ASVAB) items whether the recruit would pass the Armed Forces Qualification Test (AFQT). The data consisted of the responses (correct/incorrect) of 1,020 Navy recruits to 200 items of the ASVAB together with the scores of these…
Descriptors: Adults, Armed Forces, Computer Oriented Programs, Computer Simulation
Hackman, Judith Dozier; And Others – 1978
This paper examines the effectiveness of the Project MECCA (Make Every Child Capable of Achieving) model for early identification and mainstreaming of children with potential specific learning disabilities (SLD). The MECCA model incorporates collaboration between the learning disabilities teacher and the classroom teacher within the classroom…
Descriptors: Diagnostic Teaching, Diagnostic Tests, Identification, Individualized Instruction
