Abstract
Understanding answers to open-ended explanation questions is important in intelligent tutoring systems. Existing systems use natural language techniques in essay analysis, but revert to scripted interaction with short-answer questions during remediation, making adapting dialogue to individual students difficult. We describe a corpus study that shows that there is a relationship between the types of faulty answers and the remediation strategies that tutors use; that human tutors respond differently to different kinds of correct answers; and that re-stating correct answers is associated with improved learning. We describe a design for a diagnoser based on this study that supports remediation in open-ended questions and provides an analysis of natural language answers that enables adaptive generation of tutorial feedback for both correct and faulty answers
| Original language | English |
|---|---|
| Title of host publication | Twenty-First International Florida Artificial Intelligence Research Society Conference |
| Editors | David Wilson, H. Chad Lane |
| Place of Publication | Coconut Grove, Florida, USA |
| Publisher | AAAI Press |
| Pages | 403-408 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-57735-365-2 |
| Publication status | Published - May 2008 |
| Event | Twenty-First International Florida Artificial Intelligence Research Society Conference - Coconut Grove, Florida, United States Duration: 15 May 2008 → 17 May 2008 |
Conference
| Conference | Twenty-First International Florida Artificial Intelligence Research Society Conference |
|---|---|
| Country/Territory | United States |
| City | Coconut Grove, Florida |
| Period | 15/05/08 → 17/05/08 |