Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System
| Autor: | Joelle Pineau, Dung Do Vu, Varun Gupta, Iulian Vlad Serban, Robert Belfer, Ekaterina Kochmar |
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| Rok vydání: | 2020 |
| Předmět: |
business.industry
Human–computer interaction Computer science 020209 energy Deep learning 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Artificial intelligence Student learning business Intelligent tutoring system |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030522391 AIED (2) |
| DOI: | 10.1007/978-3-030-52240-7_26 |
| Popis: | We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account. We utilize state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints. Our model is used in Korbit (https://www.korbit.ai), a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback. |
| Databáze: | OpenAIRE |
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