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Showing 1 to 15 of 28 results Save | Export
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Volchok, Edward – Community College Journal of Research and Practice, 2018
This retrospective study evaluates early semester predictors of whether or not community college students will successfully complete blended or hybrid courses. These predictors are available to faculty by the fourth week of the semester. Success is defined as receiving a grade of C- or higher. Failure is defined as a grade below a C- or a…
Descriptors: Community Colleges, Success, Blended Learning, Models
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Zwolak, Justyna P.; Zwolak, Michael; Brewe, Eric – Physical Review Physics Education Research, 2018
The lack of an engaging pedagogy and the highly competitive atmosphere in introductory science courses tend to discourage students from pursuing science, technology, engineering, and mathematics (STEM) majors. Once in a STEM field, academic and social integration has been long thought to be important for students' persistence. Yet, it is rarely…
Descriptors: Social Networks, STEM Education, Academic Persistence, Peer Relationship
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Kilicer, Kerem; Bardakci, Salih; Arpaci, Ibrahim – Contemporary Educational Technology, 2018
For today's societies trying to cope with the current globally increased competition, existence of individuals who can take risks, solve problems and adopt changes an innovation has gained more importance when compared to the past. This situation brings responsibility to educational institutions for increasing the number of innovative individuals…
Descriptors: Predictor Variables, Technology Uses in Education, Innovation, Student Characteristics
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Almeda, Ma. Victoria; Zuech, Joshua; Utz, Chris; Higgins, Greg; Reynolds, Rob; Baker, Ryan S. – Online Learning, 2018
Online education continues to become an increasingly prominent part of higher education, but many students struggle in distance courses. For this reason, there has been considerable interest in predicting which students will succeed in online courses and which will receive poor grades or drop out prior to completion. Effective intervention depends…
Descriptors: Performance Factors, Online Courses, Electronic Learning, Models
Ibrahim, Sara – ProQuest LLC, 2017
The insider security threat causes new and dangerous dimensions in cloud computing. Those internal threats are originated from contractors or the business partners' input that have access to the systems. A study of trustworthiness and transparency might assist the organizations to monitor employees' activity more cautiously on cloud technologies…
Descriptors: Trust (Psychology), Accountability, Regression (Statistics), Computer Security
Liu, Ran; Koedinger, Kenneth R. – International Educational Data Mining Society, 2015
A growing body of research suggests that accounting for student specific variability in educational data can improve modeling accuracy and may have implications for individualizing instruction. The Additive Factors Model (AFM), a logistic regression model used to fit educational data and discover/refine skill models of learning, contains a…
Descriptors: Models, Regression (Statistics), Learning, Classification
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Huang, Liuli; Roche, Lahna R.; Kennedy, Eugene; Brocato, Melissa B. – International Journal of Higher Education, 2017
Many researchers have explored the relationships between the likelihood of graduating from college and demographic and pre-college factors such as gender, race/ethnicity, high school grade point average (GPA), and standardized test scores. However, additional factors such as a student's college major, home address, or use of learning support in…
Descriptors: Graduation Rate, Predictor Variables, Predictive Measurement, Predictive Validity
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Conijn, Rianne; Snijders, Chris; Kleingeld, Ad; Matzat, Uwe – IEEE Transactions on Learning Technologies, 2017
With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…
Descriptors: Blended Learning, Predictor Variables, Predictive Validity, Predictive Measurement
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Koon, Sharon; Petscher, Yaacov – Regional Educational Laboratory Southeast, 2015
The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…
Descriptors: Classification, Regression (Statistics), Models, At Risk Students
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Reardon, Robert C.; Melvin, Brittany; McClain, Mary-Catherine; Peterson, Gary W.; Bowman, William J. – Journal of College Student Retention: Research, Theory & Practice, 2015
Conducting research and engaging in discussions with administrators and legislators can be important contributions toward alleviating the trend toward lower graduation rates among college students. This study used archival data obtained from the university registrar to examine how engagement in a credit-bearing undergraduate career course related…
Descriptors: Graduation Rate, Student Records, Prediction, Predictive Validity
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Cahan, Sorel; Fono, Dafna; Nirel, Ronit – Journal of Learning Disabilities, 2012
The regression-based discrepancy definition of learning disabilities has been suggested by Rutter and Yule as an improvement of the well-known and much criticized achievement-intelligence discrepancy definition, whereby the examinee's predicted reading attainment is substituted for the intelligence score in the discrepancy expression. Even though…
Descriptors: Intelligence, Learning Disabilities, Predictive Validity, Definitions
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Herrera, Cheryl; Blair, Jennifer – Research in Higher Education Journal, 2015
As the U.S. population ages and policy changes emerge, such as the Patient Protection and Affordable Care Act of 2010, the U.S. will experience a significant shortage of Registered Nurses (RNs). Many colleges and universities are attempting to increase the size of nursing cohorts to respond to this imminent shortage. Notwithstanding a 2.6%…
Descriptors: Prediction, Success, Nursing Education, Nursing Students
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Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – International Educational Data Mining Society, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
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Fernandes Malaquias, Rodrigo; de Oliveira Malaquias, Fernanda Francielle – Turkish Online Journal of Distance Education, 2014
The objective of this study was to validate a scale for assessment of academic projects. As a complement, we examined its predictive ability by comparing the scores of advised/corrected projects based on the model and the final scores awarded to the work by an examining panel (approximately 10 months after the project design). Results of…
Descriptors: Predictive Measurement, Predictive Validity, Predictor Variables, Test Construction
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