A Study On Two Hint-level Policies in Conversational Intelligent Tutoring Systems
| Autor: | Elizabeth Gire, Vasile Rus, Rajendra Banjade, Donald R. Franceschetti, Nobal B. Niraula |
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| Rok vydání: | 2016 |
| Předmět: |
Computer science
Order (business) Human–computer interaction 05 social sciences Selection strategy 0202 electrical engineering electronic engineering information engineering 050301 education 020201 artificial intelligence & image processing Context (language use) 02 engineering and technology 0503 education Experimental data analysis |
| Zdroj: | Innovations in Smart Learning ISBN: 9789811024184 |
| DOI: | 10.1007/978-981-10-2419-1_24 |
| Popis: | In this work, we compared two hint-level instructional strategies, minimum scaffolding vs. maximum scaffolding, in the context of conversational intelligent tutoring systems (ITSs). The two strategies are called policies because they have a clear bias, as detailed in the paper. To this end, we conducted a randomized controlled trial experiment with two conditions corresponding to two versions of the same underlying state-of-the-art conversational ITS, i.e. DeepTutor. Each version implemented one of the two hint-level strategies. Experimental data analysis revealed that pre-post learning gains were significant in both conditions. We also learned that, in general, students need more than just a minimally informative hint in order to infer the next steps in the solution to a challenging problem; this is the case in the context of a problem selection strategy that picks challenging problems for students to work on. |
| Databáze: | OpenAIRE |
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