Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted Instructions
In recent years, individual learning path planning has become prevalent in online learning systems, while few studies have focused on teaching path planning for traditional classroom teaching. This paper proposes a target-oriented teaching path optimization scheme for cloud computing-assisted instru...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/18/9376 |
| _version_ | 1797491388706717696 |
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| author | Tengjie Yang Lin Zuo Xinduoji Yang Nianbo Liu |
| author_facet | Tengjie Yang Lin Zuo Xinduoji Yang Nianbo Liu |
| author_sort | Tengjie Yang |
| collection | DOAJ |
| description | In recent years, individual learning path planning has become prevalent in online learning systems, while few studies have focused on teaching path planning for traditional classroom teaching. This paper proposes a target-oriented teaching path optimization scheme for cloud computing-assisted instructions, in which a sequence of learning contents is arranged to ensure the maximum benefit for a given group of students. First, to evaluate the teaching performance, we investigate various student models and define some teaching objectives, including the pass rate, the excellence rate, the average score, and related constraints. Second, a new Deep Reinforcement Learning (DRL)-based teaching path planning method is proposed to tackle the learning path by maximizing a multi-objective target while satisfying all teaching constraints. It adopts a Proximal Policy Optimization (PPO) framework to find a model-free solution for achieving fast convergence and better optimality. Finally, extensive simulations with a variety of commonly used teaching methods show that our scheme provides nice performance and versatility over commonly used student models. |
| first_indexed | 2024-03-10T00:46:46Z |
| format | Article |
| id | doaj.art-1c3f6d22cdaa44de931c3bead52fbff7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:46:46Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1c3f6d22cdaa44de931c3bead52fbff72023-11-23T14:57:39ZengMDPI AGApplied Sciences2076-34172022-09-011218937610.3390/app12189376Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted InstructionsTengjie Yang0Lin Zuo1Xinduoji Yang2Nianbo Liu3School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaIn recent years, individual learning path planning has become prevalent in online learning systems, while few studies have focused on teaching path planning for traditional classroom teaching. This paper proposes a target-oriented teaching path optimization scheme for cloud computing-assisted instructions, in which a sequence of learning contents is arranged to ensure the maximum benefit for a given group of students. First, to evaluate the teaching performance, we investigate various student models and define some teaching objectives, including the pass rate, the excellence rate, the average score, and related constraints. Second, a new Deep Reinforcement Learning (DRL)-based teaching path planning method is proposed to tackle the learning path by maximizing a multi-objective target while satisfying all teaching constraints. It adopts a Proximal Policy Optimization (PPO) framework to find a model-free solution for achieving fast convergence and better optimality. Finally, extensive simulations with a variety of commonly used teaching methods show that our scheme provides nice performance and versatility over commonly used student models.https://www.mdpi.com/2076-3417/12/18/9376teaching path planningdeep reinforcement learningtarget-orientedproximal policy optimizationstudent model |
| spellingShingle | Tengjie Yang Lin Zuo Xinduoji Yang Nianbo Liu Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted Instructions Applied Sciences teaching path planning deep reinforcement learning target-oriented proximal policy optimization student model |
| title | Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted Instructions |
| title_full | Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted Instructions |
| title_fullStr | Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted Instructions |
| title_full_unstemmed | Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted Instructions |
| title_short | Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted Instructions |
| title_sort | target oriented teaching path planning with deep reinforcement learning for cloud computing assisted instructions |
| topic | teaching path planning deep reinforcement learning target-oriented proximal policy optimization student model |
| url | https://www.mdpi.com/2076-3417/12/18/9376 |
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