Toward Non-intrusive Assessment in Dialogue-Based Intelligent Tutoring Systems
| Autor: | Vasile Rus, Dan Stefanescu |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Knowledge assessment
Vocabulary Natural language interaction business.industry Computer science media_common.quotation_subject Knowledge level computer.software_genre Session (web analytics) Domain (software engineering) Artificial intelligence business computer Natural language processing media_common |
| Zdroj: | State-of-the-Art and Future Directions of Smart Learning ISBN: 9789812878663 |
| DOI: | 10.1007/978-981-287-868-7_26 |
| Popis: | This paper describes a study whose goal was to assess students’ prior knowledge level with respect to a target domain based solely on characteristics of the natural language interaction between students and a state-of-the-art conversational ITS. We report results on data collected from two conversational ITSs: a micro-adaptive-only ITS and a fully adaptive (micro- and macro-adaptive) ITS. Our models rely on both dialogue and session interaction features including time-on-task, student-generated content features (e.g., vocabulary size or domain-specific concept use), and pedagogy-related features (e.g., level of scaffolding measured as number of hints). Linear regression models were explored based on these features in order to predict students’ knowledge level, as measured with a multiple-choice pre-test, and yielded in the best cases an r = 0.949 and adjusted r-square = 0.878. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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