Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy

Seounghun Kim; Woojin Kim; Hyeoncheol Kim

doi:10.1007/978-3-030-80421-3_29

Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy

Seounghun Kim, Woojin Kim, Hyeoncheol Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Massive Open Online Courses (MOOC) often face low course retention rates due to lack of adaptability. We consider the personalized recommendation of learning content units to improve the learning experience, thus increasing retention rates. We propose a deep learning-based learning path construction model for personalized learning, based on knowledge tracing and reinforcement learning. We first trace a student’s knowledge using a deep learning-based knowledge tracing model to estimate its current knowledge state. Then, we adopt a deep reinforcement learning approach and use a student simulator to train a policy for exercise recommendation. During the recommendation process, we incorporate Bloom’s taxonomy’s cognitive level to enhance the recommendation quality. We evaluate our model through a user study and verify its usefulness as a learning tool that supports effective learning.

Original language	English
Title of host publication	Intelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings
Editors	Alexandra I. Cristea, Christos Troussas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	267-278
Number of pages	12
ISBN (Print)	9783030804206
DOIs	https://doi.org/10.1007/978-3-030-80421-3_29
Publication status	Published - 2021
Event	17th International Conference on Intelligent Tutoring Systems, ITS 2021 - Virtual, Online Duration: 2021 Jun 7 → 2021 Jun 11

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12677 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Intelligent Tutoring Systems, ITS 2021
City	Virtual, Online
Period	21/6/7 → 21/6/11

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques).

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Bloom’s taxonomy
Knowledge tracing
Learning path construction
MOOC
Personalized learning
Reinforcement learning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-80421-3_29

Cite this

Kim, S., Kim, W., & Kim, H. (2021). Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings (pp. 267-278). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12677 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-80421-3_29

Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy. / Kim, Seounghun; Kim, Woojin; Kim, Hyeoncheol.
Intelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings. ed. / Alexandra I. Cristea; Christos Troussas. Springer Science and Business Media Deutschland GmbH, 2021. p. 267-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12677 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, S, Kim, W & Kim, H 2021, Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy. in AI Cristea & C Troussas (eds), Intelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12677 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 267-278, 17th International Conference on Intelligent Tutoring Systems, ITS 2021, Virtual, Online, 21/6/7. https://doi.org/10.1007/978-3-030-80421-3_29

Kim S, Kim W, Kim H. Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy. In Cristea AI, Troussas C, editors, Intelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 267-278. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-80421-3_29

Kim, Seounghun ; Kim, Woojin ; Kim, Hyeoncheol. / Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy. Intelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings. editor / Alexandra I. Cristea ; Christos Troussas. Springer Science and Business Media Deutschland GmbH, 2021. pp. 267-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Massive Open Online Courses (MOOC) often face low course retention rates due to lack of adaptability. We consider the personalized recommendation of learning content units to improve the learning experience, thus increasing retention rates. We propose a deep learning-based learning path construction model for personalized learning, based on knowledge tracing and reinforcement learning. We first trace a student{\textquoteright}s knowledge using a deep learning-based knowledge tracing model to estimate its current knowledge state. Then, we adopt a deep reinforcement learning approach and use a student simulator to train a policy for exercise recommendation. During the recommendation process, we incorporate Bloom{\textquoteright}s taxonomy{\textquoteright}s cognitive level to enhance the recommendation quality. We evaluate our model through a user study and verify its usefulness as a learning tool that supports effective learning.",

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Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy

Abstract

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Keywords

ASJC Scopus subject areas

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