Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 6 |
Since 2006 (last 20 years) | 9 |
Descriptor
Accuracy | 9 |
Classification | 9 |
Gender Differences | 9 |
Models | 4 |
Prediction | 4 |
Academic Achievement | 2 |
Age Differences | 2 |
Algorithms | 2 |
Artificial Intelligence | 2 |
Comparative Analysis | 2 |
Dropouts | 2 |
More ▼ |
Source
Author
Belfield, Clive | 1 |
Cascallar, Eduardo C. | 1 |
Cómbita, Lina M. | 1 |
DeVore, Seth | 1 |
Finn, Kevin J. | 1 |
Hewagallage, Dona | 1 |
Jamiu Adekunle Idowu | 1 |
Kaj, Mónika | 1 |
Khatibi, Toktam | 1 |
McKenzie, Karen | 1 |
Miller, Paul | 1 |
More ▼ |
Publication Type
Journal Articles | 8 |
Reports - Research | 7 |
Information Analyses | 1 |
Reports - Evaluative | 1 |
Education Level
Higher Education | 4 |
Postsecondary Education | 4 |
Two Year Colleges | 1 |
Audience
Location
Hungary | 1 |
Iran | 1 |
North Carolina | 1 |
Laws, Policies, & Programs
Assessments and Surveys
Kaufman Brief Intelligence… | 1 |
What Works Clearinghouse Rating
Jamiu Adekunle Idowu – International Journal of Artificial Intelligence in Education, 2024
This systematic literature review investigates the fairness of machine learning algorithms in educational settings, focusing on recent studies and their proposed solutions to address biases. Applications analyzed include student dropout prediction, performance prediction, forum post classification, and recommender systems. We identify common…
Descriptors: Algorithms, Dropouts, Prediction, Academic Achievement
Musso, Mariel F.; Cómbita, Lina M.; Cascallar, Eduardo C.; Rueda, M. Rosario – Mind, Brain, and Education, 2022
The objective of this research was to develop robust predictive models of the gains in working memory (WM) and fluid intelligence (Gf) following executive attention training in children, using genetic markers, gender, and age variables. We explore the influence of genetic variables on individual differences in susceptibility to intervention.…
Descriptors: Genetics, Artificial Intelligence, Gender Differences, Age Differences
Parhizkar, Amirmohammad; Tejeddin, Golnaz; Khatibi, Toktam – Education and Information Technologies, 2023
Increasing productivity in educational systems is of great importance. Researchers are keen to predict the academic performance of students; this is done to enhance the overall productivity of educational system by effectively identifying students whose performance is below average. This universal concern has been combined with data science…
Descriptors: Algorithms, Grade Point Average, Interdisciplinary Approach, Prediction
Morgan, Shae D. – Journal of Speech, Language, and Hearing Research, 2019
Purpose: Emotion classification for auditory stimuli typically employs 1 of 2 approaches (discrete categories or emotional dimensions). This work presents a new emotional speech set, compares these 2 classification methods for emotional speech stimuli, and emphasizes the need to consider the entire communication model (i.e., the talker, message,…
Descriptors: Emotional Response, Classification, Speech Communication, Comparative Analysis
Tabatabaee-Yazdi, Mona – SAGE Open, 2020
The Hierarchical Diagnostic Classification Model (HDCM) reflects on the sequences of the presentation of the essential materials and attributes to answer the items of a test correctly. In this study, a foreign language reading comprehension test was analyzed employing HDCM and the generalized deterministic-input, noisy and gate (G-DINA) model to…
Descriptors: Diagnostic Tests, Classification, Models, Reading Comprehension
Yang, Jie; DeVore, Seth; Hewagallage, Dona; Miller, Paul; Ryan, Qing X.; Stewart, John – Physical Review Physics Education Research, 2020
Machine learning algorithms have recently been used to predict students' performance in an introductory physics class. The prediction model classified students as those likely to receive an A or B or students likely to receive a grade of C, D, F or withdraw from the class. Early prediction could better allow the direction of educational…
Descriptors: Artificial Intelligence, Man Machine Systems, Identification, At Risk Students
Saint-Maurice, Pedro F.; Welk, Gregory J.; Finn, Kevin J.; Kaj, Mónika – Research Quarterly for Exercise and Sport, 2015
Purpose: The purpose of this article was to evaluate the validity of the Progressive Aerobic Cardiovascular and Endurance Run (PACER) test in a sample of Hungarian youth. Method: Approximately 500 participants (aged 10-18 years old) were randomly selected across Hungary to complete both laboratory (maximal treadmill protocol) and field assessments…
Descriptors: Prediction, Foreign Countries, Physical Fitness, Physical Activities
Murray, Aja Louise; McKenzie, Karen – Journal of Intellectual & Developmental Disability, 2014
Background: Outcomes for people with an intellectual disability (ID) may differ depending on the severity of the condition. This study evaluated whether a tool used to screen for the presence of ID could also give an early indication of severity, in order to help inform future support needs. Methods: Multicategory receiver operating characteristic…
Descriptors: Accuracy, Learning Disabilities, Screening Tests, Classification
Zeidenberg, Matthew; Scott, Marc; Belfield, Clive – Center for Analysis of Postsecondary Education and Employment, 2015
Of the copious research on the labor market returns to college, very little has adequately modeled the pathways of non-completers or compared their outcomes with those of award holders. In this paper, we present a novel method for linking non-completers with completers according to their program of study. This method allows us to calculate the…
Descriptors: Community Colleges, Outcomes of Education, Education Work Relationship, Labor Market