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Parhizkar, Amirmohammad; Tejeddin, Golnaz; Khatibi, Toktam – Education and Information Technologies, 2023
Increasing productivity in educational systems is of great importance. Researchers are keen to predict the academic performance of students; this is done to enhance the overall productivity of educational system by effectively identifying students whose performance is below average. This universal concern has been combined with data science…
Descriptors: Algorithms, Grade Point Average, Interdisciplinary Approach, Prediction
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Qin, Lu; Phillips, Glenn Allen – International Journal of Higher Education, 2019
The 3-year graduation rate is a rarely measured metric in higher education compared to its 4- or 6- year graduation rate counterparts. For the first time in college (FTIC) students to graduate in three years, they must come with certain skills, abilities, plans, supports, or motivations. This project considers two distinct but interrelated ways of…
Descriptors: Graduation Rate, Time to Degree, College Credits, Grade Point Average
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Gardner, Josh; Brooks, Christopher; Li, Warren – Journal of Learning Analytics, 2018
In this paper, we evaluate the complete undergraduate co-enrollment network over a decade of education at a large American public university. We provide descriptive and exploratory analyses of the network, demonstrating that the co-enrollment networks evaluated follow power-law degree distributions similar to many other large-scale networks; that…
Descriptors: Markov Processes, Classification, Undergraduate Students, Grade Point Average
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Antaramian, Susan – Journal of Psychoeducational Assessment, 2015
A dual-factor mental health model includes measures of positive psychological well-being in addition to traditional indicators of psychopathology to comprehensively determine mental health status. The current study examined the utility of this model in understanding the psychological adjustment and educational functioning of college students. A…
Descriptors: Mental Health, Measures (Individuals), Models, Symptoms (Individual Disorders)
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Yin, Sylvia Chong Nguik – IAFOR Journal of Education, 2016
Universities are inundated with detailed applicant and enrolment data from a variety of sources. However, for these data to be useful there is a need to convert them into strategic knowledge and information for decision-making processes. This study uses predictive modelling to identify at-risk adult learners in their first semester at SIM…
Descriptors: Foreign Countries, Predictor Variables, Models, College Freshmen
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Gray, Geraldine; McGuinness, Colm; Owende, Philip; Hofmann, Markus – Journal of Learning Analytics, 2016
This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrollment data and an online, self-reporting,…
Descriptors: Prediction, At Risk Students, Academic Failure, College Freshmen
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Gultice, Amy; Witham, Ann; Kallmeyer, Robert – Advances in Physiology Education, 2015
High failure rates in introductory college science courses, including anatomy and physiology, are common at institutions across the country, and determining the specific factors that contribute to this problem is challenging. To identify students at risk for failure in introductory physiology courses at our open-enrollment institution, an online…
Descriptors: Anatomy, Physiology, Online Surveys, Science Education
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Sato, Brian K.; Kadandale, Pavan; He, Wenliang; Murata, Paige M. N.; Latif, Yama; Warschauer, Mark – CBE - Life Sciences Education, 2014
Primary literature is essential for scientific communication and is commonly utilized in undergraduate biology education. Despite this, there is often little time spent "training" our students how to critically analyze a paper. To address this, we introduced a primary literature module in multiple upper-division laboratory courses. In…
Descriptors: Biology, Science Instruction, Large Group Instruction, Science Laboratories
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Bowers, Alex J.; Sprott, Ryan – Journal of Educational Research, 2012
Historically, students who fail to graduate from secondary school are considered as a single category of school dropouts. However, emerging literature indicates that there may be multiple subgroups of high school dropouts, termed a "dropout typology". The authors' purpose was to assess the extent to which a typology of dropouts was present in a…
Descriptors: Grade Point Average, Dropouts, Classification, High Schools
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Musso, Mariel F.; Kyndt, Eva; Cascallar, Eduardo C.; Dochy, Filip – Frontline Learning Research, 2013
Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not only for the study of learning processes, but also as tools for improving curriculum designs, tutorial systems, and students'…
Descriptors: Prediction, Academic Achievement, Networks, Learning Processes
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Du, Chan – American Journal of Business Education, 2011
This paper examines whether a blended course that introduces lower-level education online learned by students before they come into class and after class online assignments and online discussions enhances student performance for an introductory principles of accounting course over the period 2009-2010. The blended course design includes (1)…
Descriptors: Accounting, Conventional Instruction, Blended Learning, Classification
Kobrin, Jennifer L.; Kim, Rachel; Sackett, Paul – College Board, 2011
There is much debate on the merits and pitfalls of standardized tests for college admission, with questions regarding the format (multiple-choice versus constructed response), cognitive complexity, and content of these assessments (achievement versus aptitude) at the forefront of the discussion. This study addressed these questions by…
Descriptors: College Entrance Examinations, Mathematics Tests, Test Items, Predictive Validity
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Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis