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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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Dvorak, Tomas; Jia, Miaoqing – Journal of Learning Analytics, 2016
This study analyzes the relationship between students' online work habits and academic performance. We utilize data from logs recorded by a course management system (CMS) in two courses at a small liberal arts college in the U.S. Both courses required the completion of a large number of online assignments. We measure three aspects of students'…
Descriptors: Online Courses, Educational Technology, Study Habits, Academic Achievement
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Wright, Mary C.; McKay, Timothy; Hershock, Chad; Miller, Kate; Tritz, Jared – Change: The Magazine of Higher Learning, 2014
Learning Analytics (LA) has been identified as one of the top technology trends in higher education today (Johnson et al., 2013). LA is based on the idea that datasets generated through normal administrative, teaching, or learning activities--such as registrar data or interactions with learning management systems--can be analyzed to enhance…
Descriptors: STEM Education, Introductory Courses, Physics, Technology Uses in Education
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Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
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Briggs, Richard D.; And Others – Journal of Educational Research, 1971
Descriptors: Academic Achievement, Behavior Change, Data Analysis, Study Habits
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Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
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Strang, Kenneth D. – Multicultural Education & Technology Journal, 2008
Purpose: The purpose of this paper is to inform international student study strategies as well as course design and instructional approach. Design/methodology/approach: Multiple research methods are applied, starting with exploratory data analysis, principal component analysis, confirmatory ordinal factor analysis, then recursive regression.…
Descriptors: Foreign Students, Cognitive Style, Academic Achievement, Factor Analysis
Redding, Sam, Ed.; Murphy, Marilyn, Ed.; Sheley, Pam, Ed. – Academic Development Institute, 2011
This "Handbook" offers a broad definition of family and community engagement, seen through the lens of scholars and practitioners with a wide-ranging set of perspectives on why and how families, communities, and schools collaborate with one another. Taken together, the chapters in this "Handbook" sketch out the components of a…
Descriptors: Guides, Family Involvement, Community Involvement, Family School Relationship
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Hyatt, S. Aaron – Planning for Higher Education, 1980
A Western Carolina experiment examined what kinds of data could be obtained from students that could (1) help make physical facility planning more objective and factual and (2) determine what the academic or learning consequences of these decisions might be. Focus was on student use of time and facilities. (Author/MLW)
Descriptors: Academic Achievement, College Students, Data Analysis, Facility Planning
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
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Wirt, John; Choy, Susan; Gerald, Debra; Provasnik, Stephen; Rooney, Patrick; Watanabe, Satoshi; Tobin, Richard; Glander, Mark – National Center for Education Statistics, 2001
To ensure reliable, accurate, and timely data, which are necessary to monitor the progress of education in the United States, Congress has mandated that the National Center for Education Statistics (NCES) produce an annual report, "The Condition of Education." This year's report presents 59 indicators of important developments and trends…
Descriptors: Annual Reports, School Statistics, Educational Trends, Educational Indicators
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers