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Alarcon, Gene M.; Edwards, Jean M. – Journal of Educational Psychology, 2013
The current study explored individual differences in ability and motivation factors of retention in first-year college students. We used discrete-time survival mixture analysis to model university retention. Parents' education, gender, American College Test (ACT) scores, conscientiousness, and trait affectivity were explored as predictors of…
Descriptors: Academic Achievement, Student Motivation, Academic Persistence, Individual Differences
Jodaei, Hojjat – Online Submission, 2012
This study investigated the effect of motivational factors on English achievement in an intensive English course. The participants consisted of 41 Iranian male military staff, with the age range from 21 to 28. The participants filled a translated and adapted version of the mini-Attiude Motivation Test Battery (a=0.70). Factor analysis of the…
Descriptors: English (Second Language), Second Language Instruction, Military Personnel, Young Adults
Ackerman, Phillip L.; Kanfer, Ruth; Beier, Margaret E. – Journal of Educational Psychology, 2013
Prediction of academic success at postsecondary institutions is an enduring issue for educational psychology. Traditional measures of high-school grade point average and high-stakes entrance examinations are valid predictors, especially of 1st-year college grades, yet a large amount of individual-differences variance remains unaccounted for.…
Descriptors: Cognitive Ability, Gender Differences, Academic Achievement, Educational Psychology

Bar-Tal, Daniel – Review of Educational Research, 1978
After presenting the attributional model, this paper reviews research on individual differences in beliefs about causes of success and failure. Studies illustrating differential performance on achievement-related tasks by individuals who differ in their attributions are reviewed. Causal perceptions of success and failure should be considered, in…
Descriptors: Academic Achievement, Achievement Need, Affective Behavior, Attribution Theory
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

Gardner, R. C.; Tremblay, Paul F.; Masgoret, Anne-Marie – Modern Language Journal, 1997
Investigates numerous individual difference measures to determine their underlying dimensions, to contrast their predictive validities and to evaluate their contributions in a causal model of second language acquisition. Findings indicate that substantial links exist among affective measures and achievement. (56 references) (Author/CK)
Descriptors: Academic Achievement, Affective Behavior, Anxiety, College Students
Peck, Robert F. – 1975
Eight propositions regarding the interaction of teachers and students to create educational outcomes are presented and discussed: 1. It is necessary to look at both cognitive and affective outcomes whenever we want to evaluate the effectiveness of teachers, or an educational program. 2. There is a need for more research on the reciprocal…
Descriptors: Academic Achievement, Affective Behavior, Cognitive Development, Cognitive Measurement
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection