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Kenneth Tyler Wilcox; Ross Jacobucci; Zhiyong Zhang; Brooke A. Ammerman – Grantee Submission, 2023
Text is a burgeoning data source for psychological researchers, but little methodological research has focused on adapting popular modeling approaches for text to the context of psychological research. One popular measurement model for text, topic modeling, uses a latent mixture model to represent topics underlying a body of documents. Recently,…
Descriptors: Bayesian Statistics, Content Analysis, Undergraduate Students, Self Destructive Behavior
Sami Mejri; Steven Borawski – International Journal on E-Learning, 2023
This article will address predictors of success for online students. A survey questionnaire was used to gather data concerning online students' social and educational readiness levels at a four-year private university in the Midwestern United States. Of the 4,050 potential participants, 250 (6.23%) responded to the survey. Stepwise regression…
Descriptors: Academic Persistence, Success, Online Courses, Readiness
Belland, Brian R.; Kim, Chanmin; Zhang, Anna Y.; Lee, Eunseo – ACM Transactions on Computing Education, 2023
This article reports the analysis of data from five different studies to identify predictors of preservice, early childhood teachers' views of (a) the nature of coding, (b) integration of coding into preschool classrooms, and (c) relation of coding to fields other than computer science (CS). Significant changes in views of coding were predicted by…
Descriptors: Predictor Variables, Preservice Teachers, Student Attitudes, Programming
Cris E. Haltom; Tate F. Halverson – Journal of American College Health, 2024
Objective: This study examined relationships between eating disorder risk (EDR), lifestyle variables (e.g., exposure to healthy eating media), and differences among male and female college students. Participants: College students (N = 323) completed survey questionnaires (Fall, 2016). Fifty-three participants retook the survey at a later time.…
Descriptors: Eating Disorders, Life Style, At Risk Students, Gender Differences
Alalouch, Chaham – Education Sciences, 2021
Cognitive styles affect the learning process positively if tasks are matched to the cognitive style of learners. This effect becomes more pronounced in complex education, such as in engineering. We attempted to critically assess the effect of cognitive styles and gender on students' academic performance in eight engineering majors to understand…
Descriptors: Cognitive Style, Gender Differences, Academic Achievement, Engineering Education
Cyrenne, Philippe; Chan, Alan – Canadian Journal of Higher Education, 2022
The ability of universities and colleges to predict the success of admitted students continues to be a key concern of higher education officials. Apart from a desire to see students have successful academic careers, there is also the fiscal reality of greater tuition revenues providing needed support for university budgets. Using administrative…
Descriptors: College Students, Academic Achievement, Predictor Variables, Statistical Analysis
Carter, Rose A. – ProQuest LLC, 2022
This study aimed to assess the effectiveness of existing insolvency predictive models employed for non-profit Higher Education Institutions (HEIs) and test a proposed predictive model utilizing statistical and ratio analysis by comparing HEIs in operations with those that closed from 2017 to 2020. The researcher incorporated a non-experimental,…
Descriptors: Prediction, Models, Higher Education, Nonprofit Organizations
Najib A. Mozahem – Sage Research Methods Cases, 2021
The internet has had a vast and pervasive effect on many industries. It has resulted in the creation of new industries and has overhauled the dynamics that governed existing industries. One of the most traditional industries that is now struggling to cope with the changes brought on by the internet is the industry of higher education. Students can…
Descriptors: Social Sciences, Electronic Learning, Learning Management Systems, Higher Education
Reddy, Pritika; Chaudhary, Kaylash; Sharma, Bibhya; Chand, Ronil – Electronic Journal of e-Learning, 2022
The individuals living in the 21st century have become the consumers of digital innovations and have to adapt, adopt and adapt to the new norm of surviving and thriving in the digital society. Familiarity with the latest technologies is not the only requirement for survival. One also needs to have relevant digital competencies to complete tasks…
Descriptors: Digital Literacy, Evaluation, Predictor Variables, Higher Education
Kelli A. Bird; Benjamin L. Castleman; Zachary Mabel; Yifeng Song – AERA Open, 2021
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…
Descriptors: At Risk Students, Identification, Two Year College Students, Community Colleges
Bernard L. Dillard – ProQuest LLC, 2024
The Upward Bound Math and Science (UBMS) program at Temple University (TU) seeks to guide first-generation, low-income high school students from Philadelphia in their quest to obtain postsecondary STEM degrees that lead to related careers. This study aims to evaluate the efficacy of the program by analyzing its students' performance in light of…
Descriptors: Program Evaluation, Statistical Analysis, Program Effectiveness, College Preparation
Park, Eunkyoung; Dooris, John – Assessment & Evaluation in Higher Education, 2020
This study uses decision tree analysis to determine the most important variables that predict high overall teaching and course scores on a student evaluation of teaching (SET) instrument at a large public research university in the United States. Decision tree analysis is a more robust and intuitive approach for analysing and interpreting SET…
Descriptors: Predictor Variables, Student Evaluation of Teacher Performance, Decision Making, Statistical Analysis
Robert Garlick; Joshua Hyman – Annenberg Institute for School Reform at Brown University, 2018
We use a natural experiment to evaluate sample selection correction methods' performance. In 2007, Michigan began requiring that all students take a college entrance exam, increasing the exam-taking rate from 64 to 99%. We apply different selection correction methods, using different sets of predictors, to the pre-policy exam score data. We then…
Descriptors: Quasiexperimental Design, Predictor Variables, College Entrance Examinations, Scores
Bostwick, Keiko C. P.; Becker-Blease, Kathryn A. – Psychology Learning and Teaching, 2018
Having a growth mindset has been shown to predict better academic performance in a variety of educational settings. Efforts to instill a growth mindset through educational interventions have demonstrated positive effects on academic success. However, many of the interventions previously tested are relatively time intensive and costly for some…
Descriptors: Predictor Variables, Academic Achievement, Introductory Courses, Psychology
Shogren, Karrie A.; Wehmeyer, Michael L.; Shaw, Leslie A.; Grigal, Meg; Hart, Debra; Smith, Frank A.; Khamsi, Sheida – Education and Training in Autism and Developmental Disabilities, 2018
Given the increasing enrollment of students with intellectual and developmental disabilities in postsecondary education and the potential impact of self-determination on postsecondary outcomes, this study analyzed data on the self-determination status of students with intellectual and developmental disabilities completing their first year of a…
Descriptors: Self Determination, Postsecondary Education, Predictor Variables, Intellectual Disability