Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 3 |
Descriptor
Grade 8 | 3 |
Learning Analytics | 3 |
Models | 3 |
Prediction | 3 |
Competition | 2 |
Middle School Students | 2 |
Accuracy | 1 |
Active Learning | 1 |
Bayesian Statistics | 1 |
Behavior Problems | 1 |
Case Studies | 1 |
More ▼ |
Author
Baker, Ryan S. J. d. | 1 |
Bosch, Nigel | 1 |
Gobert, Janice | 1 |
Hershkovitz, Arnon | 1 |
Levin, Nathan A. | 1 |
Sao Pedro, Michael | 1 |
Wixon, Michael | 1 |
Publication Type
Reports - Research | 3 |
Journal Articles | 2 |
Education Level
Elementary Education | 3 |
Grade 8 | 3 |
Junior High Schools | 3 |
Middle Schools | 3 |
Secondary Education | 3 |
Grade 4 | 1 |
Intermediate Grades | 1 |
Audience
Location
Massachusetts | 1 |
Laws, Policies, & Programs
Assessments and Surveys
National Assessment of… | 1 |
Patterns of Adaptive Learning… | 1 |
What Works Clearinghouse Rating
Levin, Nathan A. – Journal of Educational Data Mining, 2021
The Big Data for Education Spoke of the NSF Northeast Big Data Innovation Hub and ETS co-sponsored an educational data mining competition in which contestants were asked to predict efficient time use on the NAEP 8th grade mathematics computer-based assessment, based on the log file of a student's actions on a prior portion of the assessment. In…
Descriptors: Learning Analytics, Data Collection, Competition, Prediction
Bosch, Nigel – Journal of Educational Data Mining, 2021
Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…
Descriptors: Accuracy, Learning Analytics, Models, National Competency Tests
Hershkovitz, Arnon; Baker, Ryan S. J. d.; Gobert, Janice; Wixon, Michael; Sao Pedro, Michael – Grantee Submission, 2013
In recent years, an increasing number of analyses in Learning Analytics and Educational Data Mining (EDM) have adopted a "Discovery with Models" approach, where an existing model is used as a key component in a new EDM/analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its…
Descriptors: Learning Analytics, Models, Learning Processes, Case Studies