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Direct linkYannick Rothacher; Carolin Strobl – Journal of Educational and Behavioral Statistics, 2024
Random forests are a nonparametric machine learning method, which is currently gaining popularity in the behavioral sciences. Despite random forests' potential advantages over more conventional statistical methods, a remaining question is how reliably informative predictor variables can be identified by means of random forests. The present study…
Descriptors: Predictor Variables, Selection Criteria, Behavioral Sciences, Reliability
Yamamoto, Scott H.; Alverson, Charlotte Y. – Autism & Developmental Language Impairments, 2022
Background and Aims: The fastest growing group of students with disabilities are those with Autism Spectrum Disorder (ASD). States annually report on post-high school outcomes (PSO) of exited students. This study sought to fill two gaps in the literature related to PSO for exited high-school students with ASD and the use of state data and…
Descriptors: Autism Spectrum Disorders, Students with Disabilities, High School Graduates, Outcomes of Education
Thao-Trang Huynh-Cam; Long-Sheng Chen; Tzu-Chuen Lu – Journal of Applied Research in Higher Education, 2025
Purpose: This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability. Design/methodology/approach: The real-world…
Descriptors: Foreign Countries, Undergraduate Students, At Risk Students, Dropout Characteristics
Cannistrà, Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria – Studies in Higher Education, 2022
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…
Descriptors: Dropouts, Potential Dropouts, Dropout Prevention, Dropout Characteristics
Download full textKelli A. Bird; Benjamin L. Castleman; Zachary Mabel; Yifeng Song – Annenberg Institute for School Reform at Brown University, 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, Higher Education, Predictive Measurement, Models
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
Kayri, Murat – Educational Sciences: Theory and Practice, 2015
The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…
Descriptors: Artificial Intelligence, Influences, Academic Achievement, College Students
Harati, Lina Wagih – ProQuest LLC, 2013
Leadership is a multifaceted skill as it requires the alignment of body, spirit and mind (Brown & Moshavi, 2005). Furthermore, it is a combination of behavior, actions and interaction between leaders and followers. It is characterized by the leaders' decisions' ability to influence followers' performance and achievement through their…
Descriptors: Emotional Intelligence, Decision Making, Ethics, Statistical Analysis
Sternberg, Robert J.; Bonney, Christina R.; Gabora, Liane; Merrifield, Maegan – Educational Psychologist, 2012
This article outlines shortcomings of currently used university admissions tests and discusses ways in which they could potentially be improved, summarizing two projects designed to enhance college and university admissions. The projects were inspired by the augmented theory of successful intelligence, according to which successful intelligence…
Descriptors: Intelligence, College Students, Grade Point Average, Prediction
The Complex Route to Success: Complex Problem-Solving Skills in the Prediction of University Success
Stadler, Matthias J.; Becker, Nicolas; Greiff, Samuel; Spinath, Frank M. – Higher Education Research and Development, 2016
Successful completion of a university degree is a complex matter. Based on considerations regarding the demands of acquiring a university degree, the aim of this paper was to investigate the utility of complex problem-solving (CPS) skills in the prediction of objective and subjective university success (SUS). The key finding of this study was that…
Descriptors: Success, Predictive Validity, Predictor Variables, Problem Solving
Puffer, Keith A. – Journal of Career Assessment, 2011
Among the plethora of career theories and counseling practices, human emotion continues to be underrepresented. The paucity is evoking discontentment. For many career specialists, a distal role for emotionality has become untenable. This study demonstrated emotional intelligence (EI) associates with familiar constructs within the career…
Descriptors: Emotional Intelligence, Career Counseling, Gender Differences, Career Choice
Hajhashemi, Karim – English Language Teaching, 2012
The purpose of this study was to examine whether performance in MI could predict the performance in reading competency. The other objectives were to identify the components of MI which are correlated with the reading test scores, and to determine the relationship between the multiple intelligences and reading proficiency. A descriptive and ex post…
Descriptors: Reading Achievement, Predictor Variables, Language Proficiency, Multiple Intelligences
Coyle, Thomas R.; Pillow, David R. – Intelligence, 2008
This research examined whether the SAT and ACT would predict college grade point average (GPA) after removing g from the tests. SAT and ACT scores and freshman GPAs were obtained from a university sample (N=161) and the 1997 National Longitudinal Study of Youth (N=8984). Structural equation modeling was used to examine relationships among g, GPA,…
Descriptors: Intelligence, Grade Point Average, Structural Equation Models, Predictive Validity
Peer reviewedSternberg, Robert J.; Grigorenko, Elena L.; Bundy, Donald A. – Merrill-Palmer Quarterly, 2001
Reviews findings on the predictive validity of psychometric tests of intelligence. Concludes that conventional tests of intelligence can be useful but only if they are interpreted very carefully, taking into account the factors that can affect them, and in conjunction with other measures. (Author)
Descriptors: Aptitude Tests, Children, Cognitive Ability, Early Childhood Education
Peer reviewedRushton, J. Philippe – Intelligence, 1989
Genetic influence was estimated on Wechsler Intelligence Scale for Children subtests from inbreeding depression scores calculated on cousin marriages in Japan (n=1,854 children) and correlated with American Black-White racial differences. The genetic contribution of racial differences in cognitive performance may be more robust than was previously…
Descriptors: Black Students, Children, Cognitive Ability, Genetics
