Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 6 |
Since 2006 (last 20 years) | 6 |
Descriptor
Decision Making | 6 |
Intelligent Tutoring Systems | 6 |
Markov Processes | 6 |
Teaching Methods | 5 |
Models | 4 |
Problem Solving | 4 |
Data Analysis | 3 |
Learning Processes | 3 |
Reinforcement | 3 |
Artificial Intelligence | 2 |
College Students | 2 |
More ▼ |
Source
International Educational… | 3 |
Journal of Educational Data… | 1 |
Journal of Educational and… | 1 |
ProQuest LLC | 1 |
Author
Chi, Min | 2 |
Barnes, Tiffany | 1 |
Barnes, Tiffany, Ed. | 1 |
Chang, Hua-hua | 1 |
Chi, Min, Ed. | 1 |
Clement, Benjamin | 1 |
Feng, Junchen | 1 |
Feng, Mingyu, Ed. | 1 |
Li, Xiao | 1 |
Lopes, Manuel | 1 |
Lynch, Collin F. | 1 |
More ▼ |
Publication Type
Reports - Research | 4 |
Journal Articles | 2 |
Speeches/Meeting Papers | 2 |
Collected Works - Proceedings | 1 |
Dissertations/Theses -… | 1 |
Education Level
Higher Education | 2 |
Postsecondary Education | 2 |
High Schools | 1 |
Junior High Schools | 1 |
Middle Schools | 1 |
Secondary Education | 1 |
Audience
Location
North Carolina | 2 |
Afghanistan | 1 |
Illinois (Chicago) | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Li, Xiao; Xu, Hanchen; Zhang, Jinming; Chang, Hua-hua – Journal of Educational and Behavioral Statistics, 2023
The adaptive learning problem concerns how to create an individualized learning plan (also referred to as a learning policy) that chooses the most appropriate learning materials based on a learner's latent traits. In this article, we study an important yet less-addressed adaptive learning problem--one that assumes continuous latent traits.…
Descriptors: Learning Processes, Models, Algorithms, Individualized Instruction
Clement, Benjamin; Oudeyer, Pierre-Yves; Lopes, Manuel – International Educational Data Mining Society, 2016
Online planning of good teaching sequences has the potential to provide a truly personalized teaching experience with a huge impact on the motivation and learning of students. In this work we compare two main approaches to achieve such a goal, POMDPs that can find an optimal long-term path, and Multi-armed bandits that optimize policies locally…
Descriptors: Intelligent Tutoring Systems, Markov Processes, Models, Teaching Methods
Feng, Junchen – ProQuest LLC, 2017
The future of education is human expertise and artificial intelligence working in conjunction, a revolution that will change the education as we know it. The Intelligent Tutoring System is a key component of this future. A quantitative measurement of efficacies of practice to heterogeneous learners is the cornerstone of building an effective…
Descriptors: Intelligent Tutoring Systems, Learning Processes, Bayesian Statistics, Models
Shen, Shitian; Mostafavi, Behrooz; Barnes, Tiffany; Chi, Min – Journal of Educational Data Mining, 2018
An important goal in the design and development of Intelligent Tutoring Systems (ITSs) is to have a system that adaptively reacts to students' behavior in the short term and effectively improves their learning performance in the long term. Inducing effective pedagogical strategies that accomplish this goal is an essential challenge. To address…
Descriptors: Teaching Methods, Markov Processes, Decision Making, Rewards
Zhou, Guojing; Wang, Jianxun; Lynch, Collin F.; Chi, Min – International Educational Data Mining Society, 2017
In this study, we applied decision trees (DT) to extract a compact set of pedagogical decision-making rules from an original "full" set of 3,702 Reinforcement Learning (RL)- induced rules, referred to as the DT-RL rules and Full-RL rules respectively. We then evaluated the effectiveness of the two rule sets against a baseline Random…
Descriptors: Learning Theories, Teaching Methods, Decision Making, Intelligent Tutoring Systems
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction