Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System

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...

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Autores principales: Kochmar, Ekaterina, Vu, Dung Do, Belfer, Robert, Gupta, Varun, Serban, Iulian Vlad, Pineau, Joelle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334734/
http://dx.doi.org/10.1007/978-3-030-52240-7_26
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author Kochmar, Ekaterina
Vu, Dung Do
Belfer, Robert
Gupta, Varun
Serban, Iulian Vlad
Pineau, Joelle
author_facet Kochmar, Ekaterina
Vu, Dung Do
Belfer, Robert
Gupta, Varun
Serban, Iulian Vlad
Pineau, Joelle
author_sort Kochmar, Ekaterina
collection PubMed
description 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.
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spelling pubmed-73347342020-07-06 Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System Kochmar, Ekaterina Vu, Dung Do Belfer, Robert Gupta, Varun Serban, Iulian Vlad Pineau, Joelle Artificial Intelligence in Education Article 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. 2020-06-10 /pmc/articles/PMC7334734/ http://dx.doi.org/10.1007/978-3-030-52240-7_26 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Kochmar, Ekaterina
Vu, Dung Do
Belfer, Robert
Gupta, Varun
Serban, Iulian Vlad
Pineau, Joelle
Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System
title Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System
title_full Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System
title_fullStr Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System
title_full_unstemmed Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System
title_short Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System
title_sort automated personalized feedback improves learning gains in an intelligent tutoring system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334734/
http://dx.doi.org/10.1007/978-3-030-52240-7_26
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