Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 1 |
Descriptor
| Artificial Intelligence | 1 |
| Classification | 1 |
| Coding | 1 |
| Holistic Approach | 1 |
| Learning Processes | 1 |
| Logical Thinking | 1 |
| Man Machine Systems | 1 |
| Models | 1 |
| Physiology | 1 |
| Responses | 1 |
| Science Instruction | 1 |
| More ▼ | |
Author
| Cerchiara, Jack A. | 1 |
| Doherty, Jennifer H. | 1 |
| Haudek, Kevin C. | 1 |
| Jescovitch, Lauren N. | 1 |
| Merrill, John | 1 |
| Scott, Emily E. | 1 |
| Urban-Lurain, Mark | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkJescovitch, Lauren N.; Scott, Emily E.; Cerchiara, Jack A.; Merrill, John; Urban-Lurain, Mark; Doherty, Jennifer H.; Haudek, Kevin C. – Journal of Science Education and Technology, 2021
We systematically compared two coding approaches to generate training datasets for machine learning (ML): (1) a holistic approach based on learning progression levels; and (2) a dichotomous, analytic approach of multiple concepts in student reasoning, deconstructed from holistic rubrics. We evaluated four constructed response assessment items for…
Descriptors: Science Instruction, Coding, Artificial Intelligence, Man Machine Systems
