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Direct linkVolchok, 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
Holdershaw, Judith; Melnyk, Valentyna; Gendall, Philip; Wright, Malcolm – International Journal of Social Research Methodology, 2018
Despite years of refinement and improvement to questionnaire design, the need remains to identify effective, but 'user-friendly' questioning procedures to predict behaviour, without compromising predictive performance. Questionnaires developed to predict behaviour typically use an attitudinal approach. However, those types of questionnaires are…
Descriptors: Behavior Theories, Predictor Variables, Comparative Analysis, College Students
Galla, Brian M.; Shulman, Elizabeth P.; Plummer, Benjamin D.; Gardner, Margo; Hutt, Stephen J.; Goyer, J. Parker; D'Mello, Sidney K.; Finn, Amy S.; Duckworth, Angela L. – American Educational Research Journal, 2019
Compared with admissions test scores, why are high school grades better at predicting college graduation? We argue that success in college requires not only cognitive ability but also self-regulatory competencies that are better indexed by high school grades. In a national sample of 47,303 students who applied to college for the 2009/2010 academic…
Descriptors: Grades (Scholastic), Predictor Variables, Time to Degree, Scores
Download full textAlkushi, Abdulmohsen; Althewini, Abdulaziz – International Education Studies, 2020
Admission criteria can be used to predict Saudi student performance in college, but significant differences across several studies exists. This study explores the predictive power of admission criteria for college assignment using King Saud bin Abdulaziz University for Health Sciences as a model. Scores from high school and standardized tests were…
Descriptors: Predictive Validity, Admission Criteria, College Admission, Grades (Scholastic)
Matthew J. Salganik; Ian Lundberg; Alexander T. Kindel; Caitlin E. Ahearn; Khaled Al-Ghoneim; Abdullah Almaatouq; Drew M. Altschul; Jennie E. Brand; Nicole Bohme Carnegie; Ryan James Compton; Debanjan Datta; Thomas Davidson; Anna Filippova; Connor Gilroy; Brian J. Goode; Eaman Jahani; Ridhi Kashyap; Antje Kirchner; Stephen McKay; Allison C. Morgan; Alex Pentland; Kivan Polimis; Louis Raes; Daniel E. Rigobon; Claudia V. Roberts; Diana M. Stanescu; Yoshihiko Suhara; Adaner Usmani; Erik H. Wang; Muna Adem; Abdulla Alhajri; Bedoor AlShebli; Redwane Amin; Ryan B. Amos; Lisa P. Argyle; Livia Baer-Bositis; Moritz Büchi; Bo-Ryehn Chung; William Eggert; Gregory Faletto; Zhilin Fan; Jeremy Freese; Tejomay Gadgil; Josh Gagné; Yue Gao; Andrew Halpern-Manners; Sonia P. Hashim; Sonia Hausen; Guanhua He; Kimberly Higuera; Bernie Hogan; Ilana M. Horwitz; Lisa M. Hummel; Naman Jain; Kun Jin; David Jurgens; Patrick Kaminski; Areg Karapetyan; E. H. Kim; Ben Leizman; Naijia Liu; Malte Möser; Andrew E. Mack; Mayank Mahajan; Noah Mandell; Helge Marahrens; Diana Mercado-Garcia; Viola Mocz; Katariina Mueller-Gastell; Ahmed Musse; Qiankun Niu; William Nowak; Hamidreza Omidvar; Andrew Or; Karen Ouyang; Katy M. Pinto; Ethan Porter; Kristin E. Porter; Crystal Qian; Tamkinat Rauf; Anahit Sargsyan; Thomas Schaffner; Landon Schnabel; Bryan Schonfeld; Ben Sender; Jonathan D. Tang; Emma Tsurkov; Austin van Loon; Onur Varol; Xiafei Wang; Zhi Wang; Julia Wang; Flora Wang; Samantha Weissman; Kirstie Whitaker; Maria K. Wolters; Wei Lee Woon; James Wu; Catherine Wu; Kengran Yang; Jingwen Yin; Bingyu Zhao; Chenyun Zhu; Jeanne Brooks-Gunn; Barbara E. Engelhardt; Moritz Hardt; Dean Knox; Karen Levy; Arvind Narayanan; Brandon M. Stewart; Duncan J. Watts; Sara McLanahan – Grantee Submission, 2020
How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning…
Descriptors: Life Satisfaction, Family Life, Quality of Life, Disadvantaged
Lee, Jihyun; Zhang, Yang; Stankov, Lazar – Educational Assessment, 2019
This study aims to identify which socio-economic status (SES) variables have the best predictive validity for academic achievement, based on the international data sets of the Programme for International Student Assessment (PISA) in 2012, 2009, 2006, and 2003. From among 10 SES measures, two composite variables - Index of economic, social and…
Descriptors: Predictive Validity, Socioeconomic Status, Academic Achievement, Predictor Variables
Fonteyne, Lot; Eelbode, Annick; Lanszweert, Isabelle; Roels, Elisabeth; Schelfhout, Stijn; Duyck, Wouter; De Fruyt, Filip – International Journal for Educational and Vocational Guidance, 2018
This study addresses the effects of negative attainability feedback on the shift from engagement to disengagement from a career goal. It was hypothesized that negative attainability feedback regarding study choice may lead to both goal engagement and goal disengagement and that this relation is mediated by self-efficacy, motivational beliefs, and…
Descriptors: Feedback (Response), Accuracy, Career Development, Learner Engagement
Hinchliffe, Lisa Janicke; Rand, Allison; Collier, Jillian – Communications in Information Literacy, 2018
The process of learning includes not only success in developing knowledge, skills, and abilities but also mistakes and errors that impede such success. In any domain of learning, instructors will have developed a sense of the typical errors learners make; however, there has been no systematic investigation and documentation of predictable…
Descriptors: Information Literacy, College Freshmen, Focus Groups, Misconceptions
Anglim, Jeromy; Bozic, Stefan; Little, Jonathon; Lievens, Filip – Advances in Health Sciences Education, 2018
The current study examined the degree to which applicants applying for medical internships distort their responses to personality tests and assessed whether this response distortion led to reduced predictive validity. The applicant sample (n = 530) completed the NEO Personality Inventory whilst applying for one of 60 positions as first-year…
Descriptors: High Stakes Tests, Personality Measures, Graduate Medical Education, Predictive Validity
Holzman, Brian; Duffy, Horace – Houston Education Research Consortium, 2020
Part II of the Houston Longitudinal Study on the Transition to College and Work (HLS) examined potential indicators of college enrollment school and district staff might use to identify and support students at risk of not attending college. The study used administrative data from the Houston Independent School District (HISD) and tracked two…
Descriptors: Enrollment, At Risk Students, Urban Schools, Predictor Variables
Holzman, Brian; Duffy, Horace – Houston Education Research Consortium, 2020
This report examined three potential indicators of college enrollment school and district staff might use to identify and support students at risk of not attending college: (1) Chicago: Designed to predict high school graduation; based on earning six course credits--the minimum to advance to the next grade in HISD--and having at most one semester…
Descriptors: Enrollment, At Risk Students, Urban Schools, Predictor Variables
Holzman, Brian; Duffy, Horace – Houston Education Research Consortium, 2020
These are the appendices for "Transitioning to College and Work. Part 2: A Study of Potential Enrollment Indicators," which examined potential indicators of college enrollment school and district staff might use to identify and support students at risk of not attending college. The study used administrative data from the Houston…
Descriptors: Enrollment, At Risk Students, Urban Schools, Predictor Variables
Olive, David Monllao; Huynh, Du Q.; Reynolds, Mark; Dougiamas, Martin; Wiese, Damyon – IEEE Transactions on Learning Technologies, 2019
A significant amount of research effort has been put into finding variables that can identify students at risk based on activity records available in learning management systems (LMS). These variables often depend on the context, for example, the course structure, how the activities are assessed or whether the course is entirely online or a…
Descriptors: Prediction, Identification, At Risk Students, Online Courses
Lee, Y.-H.; Heeter, C. – Journal of Computer Assisted Learning, 2017
Educational video games can impose high cognitive demands on its users. Two studies were conducted to examine the cognitive process involved in playing an educational digital game. Study 1 examined the effects of users' working memory capacity and gaming expertise on attention and comprehension of the educational messages. The results showed that…
Descriptors: Cognitive Ability, Expertise, Attention, Educational Games
Terrell, Misty – National Technical Assistance Center on Transition, 2017
Early warning systems (EWS), in the context of secondary transition, are tools that analyze individual student-level data and estimate each student's risk of dropping out of school or completing school on time. Such tools generally consider three primary types of data--commonly referred to as the A, B, Cs: attendance/absence data,…
Descriptors: Identification, Intervention, Secondary School Students, At Risk Students
