Publication Date
| In 2025 | 0 |
| Since 2024 | 1 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 2 |
| Since 2006 (last 20 years) | 3 |
Descriptor
Source
| International Journal of… | 3 |
Author
| Arthur Bakker | 1 |
| Baker, Ryan S. J. D. | 1 |
| Enrique Garcia Moreno-Esteva | 1 |
| Goldstein, Adam B. | 1 |
| Heffernan, Neil T. | 1 |
| Lonneke Boels | 1 |
| Paul Drijvers | 1 |
| VanLehn, Kurt | 1 |
Publication Type
| Journal Articles | 3 |
| Reports - Research | 2 |
| Reports - Descriptive | 1 |
Education Level
| Grade 6 | 1 |
| Grade 7 | 1 |
| Grade 8 | 1 |
| High Schools | 1 |
| Middle Schools | 1 |
Audience
Location
| Massachusetts | 1 |
| Pennsylvania | 1 |
| South Carolina | 1 |
| Virginia | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkLonneke Boels; Enrique Garcia Moreno-Esteva; Arthur Bakker; Paul Drijvers – International Journal of Artificial Intelligence in Education, 2024
As a first step toward automatic feedback based on students' strategies for solving histogram tasks we investigated how strategy recognition can be automated based on students' gazes. A previous study showed how students' task-specific strategies can be inferred from their gazes. The research question addressed in the present article is how data…
Descriptors: Eye Movements, Learning Strategies, Problem Solving, Automation
VanLehn, Kurt – International Journal of Artificial Intelligence in Education, 2016
Although the Andes project produced many results over its 18 years of activity, this commentary focuses on its contributions to understanding how a goal-free user interface impacts the overall design and performance of a step-based tutoring system. Whereas a goal-aligned user interface displays relevant goals as blank boxes or empty locations that…
Descriptors: Computer Interfaces, Intelligent Tutoring Systems, Technology Uses in Education, Performance
Baker, Ryan S. J. D.; Goldstein, Adam B.; Heffernan, Neil T. – International Journal of Artificial Intelligence in Education, 2011
Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us exactly at which point a KC is learned. In this paper, we present a machine-learned model that assesses the probability that a student learned a KC at a…
Descriptors: Intelligent Tutoring Systems, Mastery Learning, Probability, Knowledge Level
