Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning
Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in stude...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/352895 |
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| author | Hsuan-Ta Lin Po-Ming Lee Tzu-Chien Hsiao |
| author_facet | Hsuan-Ta Lin Po-Ming Lee Tzu-Chien Hsiao |
| author_sort | Hsuan-Ta Lin |
| collection | DOAJ |
| description | Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students’ learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction. |
| format | Article |
| id | doaj-art-8ae574a133a64dbbbda349ceda5adc1e |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-8ae574a133a64dbbbda349ceda5adc1e2025-02-03T05:43:39ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/352895352895Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement LearningHsuan-Ta Lin0Po-Ming Lee1Tzu-Chien Hsiao2Institute of Biomedical Engineering, National Chiao Tung University, 1001 University Road, Hsinchu, TaiwanInstitute of Computer Science and Engineering, National Chiao Tung University, 1001 University Road, Hsinchu, TaiwanInstitute of Biomedical Engineering, National Chiao Tung University, 1001 University Road, Hsinchu, TaiwanTutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students’ learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.http://dx.doi.org/10.1155/2015/352895 |
| spellingShingle | Hsuan-Ta Lin Po-Ming Lee Tzu-Chien Hsiao Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning The Scientific World Journal |
| title | Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning |
| title_full | Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning |
| title_fullStr | Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning |
| title_full_unstemmed | Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning |
| title_short | Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning |
| title_sort | online pedagogical tutorial tactics optimization using genetic based reinforcement learning |
| url | http://dx.doi.org/10.1155/2015/352895 |
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