Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 0 |
Since 2006 (last 20 years) | 3 |
Descriptor
Models | 3 |
Predictor Variables | 3 |
Probability | 3 |
Skill Development | 3 |
Intelligent Tutoring Systems | 2 |
Problem Solving | 2 |
Achievement Rating | 1 |
Adolescent Development | 1 |
Advantaged | 1 |
At Risk Persons | 1 |
Biology | 1 |
More ▼ |
Author
Goldstein, Adam B. | 2 |
Baker, Ryan S. | 1 |
Baker, Ryan S. J. D. | 1 |
Gowda, Sujith M. | 1 |
Heffernan, Neil T. | 1 |
Hershkovitz, Arnon | 1 |
Howard, Kimberly | 1 |
Reeves, Richard V. | 1 |
Rossi, Lisa M. | 1 |
Publication Type
Reports - Research | 3 |
Journal Articles | 2 |
Education Level
Grade 6 | 1 |
Grade 7 | 1 |
Grade 8 | 1 |
High Schools | 1 |
Higher Education | 1 |
Middle Schools | 1 |
Postsecondary Education | 1 |
Audience
Location
Pennsylvania | 2 |
Massachusetts | 1 |
South Carolina | 1 |
Virginia | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Reeves, Richard V.; Howard, Kimberly – Center on Children and Families at Brookings, 2013
From an intergenerational perspective, the U.S. income distribution is sticky at both ends. Affluence and poverty are both partially inherited. Policy and research has focused on upward mobility, especially from the bottom. But relative intergenerational upward mobility is only possible with equivalent rates of downward mobility, where much less…
Descriptors: Advantaged, Cognitive Ability, Social Mobility, At Risk Persons
Baker, Ryan S.; Hershkovitz, Arnon; Rossi, Lisa M.; Goldstein, Adam B.; Gowda, Sujith M. – Journal of the Learning Sciences, 2013
We present a new method for analyzing a student's learning over time for a specific skill: analysis of the graph of the student's moment-by-moment learning over time. Moment-by-moment learning is calculated using a data-mined model that assesses the probability that a student learned a skill or concept at a specific time during learning (Baker,…
Descriptors: Learning Processes, Intelligent Tutoring Systems, Probability, Skill Development
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