Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 3 |
Descriptor
Artificial Intelligence | 3 |
Coding | 2 |
Computer Science Education | 2 |
Decision Making | 2 |
Teaching Methods | 2 |
Assignments | 1 |
Classification | 1 |
Data Analysis | 1 |
Error Correction | 1 |
Evaluation Methods | 1 |
Goodness of Fit | 1 |
More ▼ |
Author
Barnes, Tiffany | 3 |
Chi, Min | 3 |
Shi, Yang | 2 |
Maniktala, Mehak | 1 |
Mao, Ye | 1 |
Price, Thomas | 1 |
Price, Thomas W. | 1 |
Sanz Ausin, Markel | 1 |
Schmucker, Robin | 1 |
Publication Type
Reports - Research | 3 |
Speeches/Meeting Papers | 2 |
Journal Articles | 1 |
Education Level
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Location
Virginia | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Sanz Ausin, Markel; Maniktala, Mehak; Barnes, Tiffany; Chi, Min – International Journal of Artificial Intelligence in Education, 2023
While Reinforcement learning (RL), especially Deep RL (DRL), has shown outstanding performance in video games, little evidence has shown that DRL can be successfully applied to human-centric tasks where the ultimate RL goal is to make the "human-agent interactions" productive and fruitful. In real-life, complex, human-centric tasks, such…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Teaching Methods, Learning Activities
Shi, Yang; Mao, Ye; Barnes, Tiffany; Chi, Min; Price, Thomas W. – International Educational Data Mining Society, 2021
Automatically detecting bugs in student program code is critical to enable formative feedback to help students pinpoint errors and resolve them. Deep learning models especially code2vec and ASTNN have shown great success for "large-scale" code classification. It is not clear, however, whether they can be effectively used for bug…
Descriptors: Artificial Intelligence, Program Effectiveness, Coding, Computer Science Education
Shi, Yang; Schmucker, Robin; Chi, Min; Barnes, Tiffany; Price, Thomas – International Educational Data Mining Society, 2023
Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1)…
Descriptors: Computer Science Education, Data Analysis, Programming, Coding