Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 1 |
Descriptor
| Access to Information | 1 |
| College Students | 1 |
| Computer Science | 1 |
| Cues | 1 |
| Data Collection | 1 |
| Educational Technology | 1 |
| Foreign Countries | 1 |
| Graphs | 1 |
| Inferences | 1 |
| Intelligent Tutoring Systems | 1 |
| Prediction | 1 |
| More ▼ | |
Source
| Journal of Educational Data… | 1 |
Author
| Barnes, Tiffany | 1 |
| Gross, Sebastian | 1 |
| Hammer, Barbara | 1 |
| Paassen, Benjamin | 1 |
| Pinkwart, Niels | 1 |
| Price, Thomas William | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
| Germany | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textPaassen, Benjamin; Hammer, Barbara; Price, Thomas William; Barnes, Tiffany; Gross, Sebastian; Pinkwart, Niels – Journal of Educational Data Mining, 2018
Intelligent tutoring systems can support students in solving multi-step tasks by providing hints regarding what to do next. However, engineering such next-step hints manually or via an expert model becomes infeasible if the space of possible states is too large. Therefore, several approaches have emerged to infer next-step hints automatically,…
Descriptors: Intelligent Tutoring Systems, Cues, Educational Technology, Technology Uses in Education
