TY - GEN
T1 - Learning Path Construction Using Reinforcement Learning and Bloom’s Taxonomy
AU - Kim, Seounghun
AU - Kim, Woojin
AU - Kim, Hyeoncheol
N1 - 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.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Bloom’s taxonomy
KW - Knowledge tracing
KW - Learning path construction
KW - MOOC
KW - Personalized learning
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85112264377&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-80421-3_29
DO - 10.1007/978-3-030-80421-3_29
M3 - Conference contribution
AN - SCOPUS:85112264377
SN - 9783030804206
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 267
EP - 278
BT - Intelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings
A2 - Cristea, Alexandra I.
A2 - Troussas, Christos
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Intelligent Tutoring Systems, ITS 2021
Y2 - 7 June 2021 through 11 June 2021
ER -