Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 7 |
Descriptor
| Bayesian Statistics | 7 |
| Mathematics Instruction | 7 |
| Prediction | 7 |
| Models | 4 |
| Correlation | 3 |
| Foreign Countries | 3 |
| Intelligent Tutoring Systems | 3 |
| Mathematics Achievement | 3 |
| Statistical Analysis | 3 |
| Artificial Intelligence | 2 |
| Automation | 2 |
| More ▼ | |
Source
| International Educational… | 3 |
| British Journal of… | 1 |
| Developmental Psychology | 1 |
| International Journal of… | 1 |
| Journal of Higher Education… | 1 |
Author
| Barnes, Tiffany | 1 |
| Barnes, Tiffany, Ed. | 1 |
| Baschera, Gian-Marco | 1 |
| Bekele, Rahel | 1 |
| Busetto, Alberto Giovanni | 1 |
| Chi, Min | 1 |
| Evans, Terry | 1 |
| Geary, David C. | 1 |
| Gross, Markus | 1 |
| Hershkovitz, Arnon, Ed. | 1 |
| Hu, Xiangen, Ed. | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 5 |
| Journal Articles | 4 |
| Speeches/Meeting Papers | 2 |
| Collected Works - Proceedings | 1 |
| Reports - Evaluative | 1 |
Education Level
| Higher Education | 3 |
| Postsecondary Education | 3 |
| Secondary Education | 3 |
| Elementary Secondary Education | 2 |
| Junior High Schools | 2 |
| Middle Schools | 2 |
| Early Childhood Education | 1 |
| Elementary Education | 1 |
| High Schools | 1 |
Audience
Laws, Policies, & Programs
| Elementary and Secondary… | 1 |
Assessments and Surveys
| Wechsler Preschool and… | 1 |
What Works Clearinghouse Rating
Mao, Ye; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2020
Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and…
Descriptors: Time, Models, Artificial Intelligence, Bayesian Statistics
Pittayachawan, Siddhi; Macauley, Peter; Evans, Terry – Journal of Higher Education Policy and Management, 2016
This article reports how statistical analyses of PhD thesis records can reveal future research capacities for disciplines beyond their primary fields. The previous research showed that most theses contributed to and/or used methodologies from more than one discipline. In Australia, there was a concern for declining mathematical teaching and…
Descriptors: Databases, Doctoral Dissertations, Foreign Countries, Statistical Analysis
Khajah, Mohammad; Lindsey, Robert V.; Mozer, Michael C. – International Educational Data Mining Society, 2016
In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better.…
Descriptors: Bayesian Statistics, Data Analysis, Prediction, Intelligent Tutoring Systems
Geary, David C.; vanMarle, Kristy – Developmental Psychology, 2016
At the beginning of preschool (M = 46 months of age), 197 (94 boys) children were administered tasks that assessed a suite of nonsymbolic and symbolic quantitative competencies as well as their executive functions, verbal and nonverbal intelligence, preliteracy skills, and their parents' education level. The children's mathematics achievement was…
Descriptors: Young Children, Mathematics, Mathematics Achievement, Mathematics Education
Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Baschera, Gian-Marco; Kohn, Juliane; Kucian, Karin; von Aster, Michael; Gross, Markus – International Journal of Artificial Intelligence in Education, 2013
This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic…
Descriptors: Mathematics Instruction, Children, Computer Assisted Instruction, Educational Technology
Bekele, Rahel; McPherson, Maggie – British Journal of Educational Technology, 2011
This research work presents a Bayesian Performance Prediction Model that was created in order to determine the strength of personality traits in predicting the level of mathematics performance of high school students in Addis Ababa. It is an automated tool that can be used to collect information from students for the purpose of effective group…
Descriptors: Foreign Countries, Personality Traits, Mathematics Education, Prediction
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use

Peer reviewed
Direct link
