Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 2 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Algorithms | 2 |
| Artificial Intelligence | 2 |
| Prediction | 2 |
| Reliability | 2 |
| Cheating | 1 |
| Classification | 1 |
| Coding | 1 |
| Deception | 1 |
| Electronic Mail | 1 |
| Evaluation Methods | 1 |
| Intelligent Tutoring Systems | 1 |
| More ▼ | |
Author
| Baker, Ryan S. | 1 |
| Brandt, Steffen | 1 |
| Fancsali, Stephen | 1 |
| Hutt, Stephen | 1 |
| Levin, Nathan | 1 |
| Ludwig, Sabrina | 1 |
| Mayer, Christian W. F. | 1 |
| Nasiar, Nidhi | 1 |
Publication Type
| Reports - Research | 2 |
| Journal Articles | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textLevin, Nathan; Baker, Ryan S.; Nasiar, Nidhi; Fancsali, Stephen; Hutt, Stephen – International Educational Data Mining Society, 2022
Research into "gaming the system" behavior in intelligent tutoring systems (ITS) has been around for almost two decades, and detection has been developed for many ITSs. Machine learning models can detect this behavior in both real-time and in historical data. However, intelligent tutoring system designs often change over time, in terms…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Models, Cheating
Direct linkMayer, Christian W. F.; Ludwig, Sabrina; Brandt, Steffen – Journal of Research on Technology in Education, 2023
This study investigates the potential of automated classification using prompt-based learning approaches with transformer models (large language models trained in an unsupervised manner) for a domain-specific classification task. Prompt-based learning with zero or few shots has the potential to (1) make use of artificial intelligence without…
Descriptors: Prompting, Classification, Artificial Intelligence, Natural Language Processing
