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Pytlarz, Ian; Pu, Shi; Patel, Monal; Prabhu, Rajini – International Educational Data Mining Society, 2018
Identifying at-risk students at an early stage is a challenging task for colleges and universities. In this paper, we use students' oncampus network traffic volume to construct several useful features in predicting their first semester GPA. In particular, we build proxies for their attendance, class engagement, and out-of-class study hours based…
Descriptors: College Freshmen, Grade Point Average, At Risk Students, Academic Achievement
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Gitinabard, 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
Pace, Ann J.; And Others – 1986
An earlier study found virtually no relationship between college students' reported studying practices and various tests of reading comprehension, so a second study investigated whether different results would be obtained with an instrument based on study strategies college students actually reported using. Scores on this instrument were to be…
Descriptors: Academic Achievement, Advance Organizers, Content Area Reading, Data Analysis
Beach, David P. – 1981
A project was conducted to determine whether a vocational student's version of the Affective Work Competencies Inventory could be prepared to measure the psychological constructs of values, habits, and attitudes. A revised inventory was developed and administered to 194 students in eight selected programs at Toledo vocational high schools (data…
Descriptors: Attitude Measures, Competence, Data Analysis, High School Students