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Sharon Faur; Olivia Valdes; Frank Vitaro; Mara Brendgen; Michel Boivin; Brett Laursen – Child Development, 2024
According to the failure model (Patterson & Capaldi, 1990), peer rejection is the intermediary link between problem behaviors and internalizing symptoms. The present study tested the model with 464 monozygotic and same-sex dizygotic twin pairs (234 female, 230 male dyads). Teacher-reported reactive aggression and internalizing symptoms, and…
Descriptors: Symptoms (Individual Disorders), Genetics, Aggression, Rejection (Psychology)
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José M. Ortiz-Lozano; Pilar Aparicio-Chueca; Xavier M. Triadó-Ivern; Jose Luis Arroyo-Barrigüete – Studies in Higher Education, 2024
Student dropout is a major concern in studies investigating retention strategies in higher education. This study identifies which variables are important to predict student dropout, using academic data from 3583 first-year students on the Business Administration (BA) degree at the University of Barcelona (Spain). The results indicate that two…
Descriptors: Dropouts, Predictor Variables, Social Sciences, Law Students
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Hartmann, Christian; van Gog, Tamara; Rummel, Nikol – Applied Cognitive Psychology, 2020
Studies on the productive failure (PF) approach have demonstrated that attempting to solve a problem prepares students more effectively for later instruction compared to observing failed problem-solving attempts prior to instruction. However, the examples of failure used in these studies did not display the problem-solving-and-failing…
Descriptors: Failure, Problem Solving, Teaching Methods, Observation
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J. Bryan Osborne; Andrew S. I. D. Lang – Journal of Postsecondary Student Success, 2023
This paper describes a neural network model that can be used to detect at- risk students failing a particular course using only grade book data from a learning management system. By analyzing data extracted from the learning management system at the end of week 5, the model can predict with an accuracy of 88% whether the student will pass or fail…
Descriptors: Identification, At Risk Students, Learning Management Systems, Prediction
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Berriri, Mehdi; Djema, Sofiane; Rey, Gaëtan; Dartigues-Pallez, Christel – Education Sciences, 2021
Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses…
Descriptors: Student Evaluation, Artificial Intelligence, Classification, Foreign Countries
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Ellis, Jules L. – Educational and Psychological Measurement, 2021
This study develops a theoretical model for the costs of an exam as a function of its duration. Two kind of costs are distinguished: (1) the costs of measurement errors and (2) the costs of the measurement. Both costs are expressed in time of the student. Based on a classical test theory model, enriched with assumptions on the context, the costs…
Descriptors: Test Length, Models, Error of Measurement, Measurement
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Burton, Rob J. F. – Journal of Agricultural Education and Extension, 2020
Purpose: Demonstration farming has been an important part of agricultural extension since the first decades of the twentieth century. While Seaman Knapp is often credited with developing demonstration farming, his son acknowledged that the concept has much earlier origins in the nineteenth century development of model/pattern farms. However,…
Descriptors: Agricultural Education, Demonstration Programs, Farm Management, Extension Education
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Karalar, Halit; Kapucu, Ceyhun; Gürüler, Hüseyin – International Journal of Educational Technology in Higher Education, 2021
Predicting students at risk of academic failure is valuable for higher education institutions to improve student performance. During the pandemic, with the transition to compulsory distance learning in higher education, it has become even more important to identify these students and make instructional interventions to avoid leaving them behind.…
Descriptors: Grade Prediction, Academic Failure, Models, COVID-19
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Hu, Yung-Hsiang – International Review of Research in Open and Distributed Learning, 2022
Early warning systems (EWSs) have been successfully used in online classes, especially in massive open online courses, where it is nearly impossible for students to interact face-to-face with their teachers. Although teachers in higher education institutions typically have smaller class sizes, they also face the challenge of being unable to have…
Descriptors: Dropout Prevention, At Risk Students, Online Courses, Private Colleges
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Zhang, Chuankai; Huang, Yanzun; Wang, Jingyu; Lu, Dongyang; Fang, Weiqi; Stamper, John; Fancsali, Stephen; Holstein, Kenneth; Aleven, Vincent – Grantee Submission, 2019
"Wheel spinning" is the phenomenon in which a student fails to master a Knowledge Component (KC), despite significant practice. Ideally, an intelligent tutoring system would detect this phenomenon early, so that the system or a teacher could try alternative instructional strategies. Prior work has put forward several criteria for wheel…
Descriptors: Identification, Intelligent Tutoring Systems, Academic Failure, Criteria
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González-Rodríguez, Diego; Vieira, María-José; Vidal, Javier – Educational Research, 2019
Background: Early school leaving (ESL) is a significant and complex problem for most educational systems. Research has analysed this problem from a number of different perspectives but has been mainly focused on a specific set of variables that may influence ESL. Purpose: This study sought to identify the variables that influence ESL in compulsory…
Descriptors: Dropouts, Alienation, Academic Failure, Grade Repetition
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Zhang, Chuankai; Huang, Yanzun; Wang, Jingyu; Lu, Dongyang; Fang, Weiqi; Stamper, John; Fancsali, Stephen; Holstein, Kenneth; Aleven, Vincent – International Educational Data Mining Society, 2019
"Wheel spinning" is the phenomenon in which a student fails to master a Knowledge Component (KC), despite significant practice. Ideally, an intelligent tutoring system would detect this phenomenon early, so that the system or a teacher could try alternative instructional strategies. Prior work has put forward several criteria for wheel…
Descriptors: Identification, Intelligent Tutoring Systems, Academic Failure, Criteria
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David Light Shields; Christopher D. Funk – Journal of Character Education, 2023
In this study of first-year intercollegiate athletes (N = 2241), we examined predictors of the character-rich qualities of life purpose and integrity. Three dimensions of purpose were included--seeking purpose, found purpose, and purpose beyond-the-self. The predictor variables investigated included contesting orientations, fear of failure, goal…
Descriptors: Integrity, College Athletics, Student Athletes, Athletic Coaches
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Zakaria, Fathiah; Che Kar, Siti Aishah; Abdullah, Rina; Ismail, Syila Izawana; Md Enzai, Nur Idawati – Asian Journal of University Education, 2021
This paper presents a study of correlation between subjects of Diploma in Electrical Engineering (Electronics/Power) at Universiti Teknologi MARA(UiTM) Cawangan Terengganu using Artificial Neural Network (ANN). The analysis was done to see the effect of mathematical subjects (Pre-calculus and Calculus 1) and core subject (Electric Circuit 1) on…
Descriptors: Correlation, Teaching Methods, Artificial Intelligence, Universities
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Westera, Wim – Interactive Learning Environments, 2018
This paper presents a computational model for simulating how people learn from serious games. While avoiding the combinatorial explosion of a games micro-states, the model offers a meso-level pathfinding approach, which is guided by cognitive flow theory and various concepts from learning sciences. It extends a basic, existing model by exposing…
Descriptors: Computation, Models, Simulation, Games
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