Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System

Ekaterina Kochmar; Dung Do Vu; Robert Belfer; Varun Gupta; Iulian Vlad Serban; Joelle Pineau

doi:10.1007/978-3-030-52240-7_26

Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System

Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Varun Gupta, Iulian Vlad Serban, Joelle Pineau

Department of Computer Science

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

33 Citations (SciVal)

Abstract

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.

Original language	English
Title of host publication	AIED 2020: Artificial Intelligence in Education
Editors	I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, E. Millan
Publisher	Springer
Pages	140-146
Number of pages	7
ISBN (Electronic)	978-3-030-52240-7
ISBN (Print)	978-3-030-52239-1
DOIs	https://doi.org/10.1007/978-3-030-52240-7_26
Publication status	E-pub ahead of print - 30 Jun 2020
Event	AIED 2020: The 2020 conference on Artificial Intelligence in Education - Virtual, Ifrane, Morocco Duration: 6 Jul 2020 → 10 Jul 2020 https://aied2020.nees.com.br/#/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer, Cham.
Volume	12164

Conference

Conference	AIED 2020
Abbreviated title	AIED 2020
Country/Territory	Morocco
City	Ifrane
Period	6/07/20 → 10/07/20
Internet address	https://aied2020.nees.com.br/#/

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10.1007/978-3-030-52240-7_26

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Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2020). Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System. In I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millan (Eds.), AIED 2020: Artificial Intelligence in Education (pp. 140-146). (Lecture Notes in Computer Science; Vol. 12164). Springer. Advance online publication. https://doi.org/10.1007/978-3-030-52240-7_26

Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System. / Kochmar, Ekaterina; Vu, Dung Do; Belfer, Robert et al.
AIED 2020: Artificial Intelligence in Education. ed. / I. Bittencourt; M. Cukurova; K. Muldner; R. Luckin; E. Millan. Springer, 2020. p. 140-146 (Lecture Notes in Computer Science; Vol. 12164).

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Kochmar, E, Vu, DD, Belfer, R, Gupta, V, Serban, IV & Pineau, J 2020, Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System. in I Bittencourt, M Cukurova, K Muldner, R Luckin & E Millan (eds), AIED 2020: Artificial Intelligence in Education. Lecture Notes in Computer Science, vol. 12164, Springer, pp. 140-146, AIED 2020, Ifrane, Morocco, 6/07/20. https://doi.org/10.1007/978-3-030-52240-7_26

Kochmar E, Vu DD, Belfer R, Gupta V, Serban IV, Pineau J. Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System. In Bittencourt I, Cukurova M, Muldner K, Luckin R, Millan E, editors, AIED 2020: Artificial Intelligence in Education. Springer. 2020. p. 140-146. (Lecture Notes in Computer Science). Epub 2020 Jun 30. doi: 10.1007/978-3-030-52240-7_26

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abstract = "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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AB - 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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Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System

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