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Jose Silva-Lugo; Heather Maness – Sage Research Methods Cases, 2025
The study provides a detailed methodological approach, cross-industry standard process for data mining, for predicting at-risk students with an imbalanced class. The objective was to identify the best machine learning model for predicting students at risk of failing the course during weeks 2-8 of the semester. We encountered issues in the dataset,…
Descriptors: Prediction, Predictor Variables, At Risk Students, Information Retrieval
Meyers, Coby V.; Wronowski, Meredith L.; VanGronigen, Bryan A. – Educational Assessment, Evaluation and Accountability, 2021
School improvement research has insufficiently considered the importance of intervening in schools with declining academic performance. Fields such as engineering and medicine have prioritized predicting decline to save structures or patients before they are in peril. Unfortunately, in education, school improvement policies and interventions are…
Descriptors: Identification, Educational Improvement, Academic Achievement, Predictor Variables
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
Wang, Qiang; Song, Xin; Hong, Jon-Chao; Li, Shuang; Zhang, Mengmeng; Yang, Xiantong – Education and Information Technologies, 2023
In response to the wide-ranging concern of online academic futility, the current study aimed to explore the independent variables and mediating variable from a novel perspective of parents during COVID-19. Based on the social comparison theory and the control-value theory of achievement emotions, social comparison and tutoring anxiety were…
Descriptors: Self Concept, Tutoring, Anxiety, Parent Attitudes
Hui Shi; Nuodi Zhang; Secil Caskurlu; Hunhui Na – Journal of Computer Assisted Learning, 2025
Background: The growth of online education has provided flexibility and access to a wide range of courses. However, the self-paced and often isolated nature of these courses has been associated with increased dropout and failure rates. Researchers employed machine learning approaches to identify at-risk students, but multiple issues have not been…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, At Risk Students
Eileen du Plooy; Daleen Casteleijn; Denise Franzsen; Gopika Ramkilawon – Journal of Occupational Therapy Education, 2025
Significant disparities in academic performance that may be associated with specific demographics can be utilized for supporting diverse student cohorts. Strategies can be developed for students to enhance equity and inclusivity in undergraduate occupational therapy education to ensure student success. This study aimed to determine the predictive…
Descriptors: Occupational Therapy, Allied Health Occupations Education, Academic Achievement, Predictor Variables
Analysis and Prediction of Students' Performance in a Computer-Based Course through Real-Time Events
Lucia Uguina-Gadella; Iria Estevez-Ayres; Jesus Arias Fisteus; Carlos Alario-Hoyos; Carlos Delgado Kloos – IEEE Transactions on Learning Technologies, 2024
Students learn not only directly from their teachers and books, but also by using their computers, tablets, and phones. Monitoring these learning environments creates new opportunities for teachers to track students' progress. In particular, this article is based on gathering real-time events as students interact with learning tools and materials…
Descriptors: Predictor Variables, Academic Achievement, Computer Assisted Instruction, Electronic Learning
Ntema, Ratoeba Piet – Journal of Student Affairs in Africa, 2022
Student dropout is a significant concern for university administrators, students and other stakeholders. Dropout is recognised as highly complex due to its multi-causality, which is expressed in the existing relationship in its explanatory variables associated with students, their socio-economic and academic conditions, and the characteristics of…
Descriptors: College Students, Dropout Characteristics, At Risk Students, Profiles
Roberts, Nicola – Journal of Further and Higher Education, 2023
Globally, statistical analyses have found a range of variables that predict the odds of first-year students failing to progress at their Higher Education Institution (HEI). Some of these studies have included students from a range of disciplines. Yet despite the rise in the number of criminology students in HEIs in the UK, little statistical…
Descriptors: Predictor Variables, Academic Achievement, Academic Failure, College Freshmen
Wang, Ling; Laird-Fick, Heather; Parker, Carol; Liao, Zongqiang; Solomon, David – Advances in Health Sciences Education, 2022
Purpose: Our US medical school uses National Board of Medical Examiners (NBME) tests as progress tests during the pre-clerkship curriculum to assess students. In this study, we examined students' growth patterns using progress tests in the first year of medical school to identify students at risk for failing United States Medical Licensing…
Descriptors: Medical Education, Medical Students, Medical Schools, Licensing Examinations (Professions)
Tisha L. N. Emerson; KimMarie McGoldrick – Journal of Economic Education, 2024
Using data from 11 institutions, the authors investigate enrollments in intermediate microeconomics to determine characteristics of successful and unsuccessful students and follow the retake behavior of unsuccessful students. Successful students are significantly different from unsuccessful ones, and unsuccessful students differ by type…
Descriptors: Microeconomics, Student Attrition, Withdrawal (Education), Academic Persistence
Costa, Stella F.; Diniz, Michael M. – Education and Information Technologies, 2022
The large rates of students' failure is a very frequent problem in undergraduate courses, being even more evident in exact sciences. Pointing out the reasons of such problem is a paramount research topic, though not an easy task. An alternative is to use Educational Data Mining techniques (EDM), which enables one to convert data from educational…
Descriptors: Prediction, Undergraduate Students, Mathematics Education, Models
Laura L. Beaton – Journal of Educational Technology Systems, 2025
Online quizzes and learning platforms provided by textbook publishers have become common components of undergraduate education. Here, I examine how participation in these formative assessments related to student course performance. Over multiple semesters, students completed either free online unlimited attempt quizzes or assignments from a…
Descriptors: Formative Evaluation, Computer Assisted Testing, Tests, Student Evaluation
Mamiya, Blain; Powell, Cynthia B.; Shelton, G. Robert; Dubrovskiy, Anton; Villalta-Cerdas, Adrian; Broadway, Susan; Weber, Rebecca; Mason, Diana – Journal of College Science Teaching, 2022
This article looks at the effects of environmental factors such as classification, residence location, and employment status of Hispanic students who unsuccessfully completed first-semester general chemistry (Chem I) at a Hispanic-Serving or emerging Hispanic-Serving Institution. Students' automaticity skills in arithmetic and quantitative…
Descriptors: Environmental Influences, At Risk Students, Hispanic American Students, Chemistry
Ajjawi, Rola; Dracup, Mary; Zacharias, Nadine; Bennett, Sue; Boud, David – Higher Education Research and Development, 2020
Academic failure is an important and personal event in the lives of university students, and the ways they make sense of experiences of failure matters for their persistence and future success. Academic failure contributes to attrition, yet the extent of this contribution and precipitating factors of failure are not well understood. To illuminate…
Descriptors: Academic Persistence, Academic Failure, Student Attitudes, Emotional Response

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