Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy

Seounghun Kim, Woojin Kim, Hyeoncheol Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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 languageEnglish
Title of host publicationIntelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings
EditorsAlexandra I. Cristea, Christos Troussas
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030804206
Publication statusPublished - 2021
Event17th International Conference on Intelligent Tutoring Systems, ITS 2021 - Virtual, Online
Duration: 2021 Jun 72021 Jun 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12677 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Intelligent Tutoring Systems, ITS 2021
CityVirtual, Online


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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