标题: | XCS于教学系统之教授策略最佳化 Using Extended Classifier System for Pedagogical Tactics Optimization in Tutoring System |
作者: | 林轩达 Lin, Hsuan-Ta 萧子健 Hsiao, Tzu-Chien 生医工程研究所 |
关键字: | 智慧化教学系统;基因演算法为基础之增强式学习法;教学策略;Intelligent tutoring system;Genetic-based reinforcement learning;Tutorial tactic |
公开日期: | 2015 |
摘要: | 教学策略为学生与教授者(教学系统)互动过程中,由教授者在众多可能产生的教学动作中选择合适的教学动作来教导学生,在教学互动中,上述策略已经被视为一个不可或缺的教学行为模式。在先前的研究报告中已发现当学习的教材与素材在固定的条件下,学生会因教授者给予的不同教学策略影响其学习成效。先前研究报告使用增强式学习演算法让教学系统学习最佳化教学策略。然而,当此演算法碰到较困难或复杂的学习环境时,会限制其解决问题的能力。因此,本研究使用改良式的增强式学习法,系以基因演算法为基础之增强式学习法(GBRL),利用线上即时学习的方式来学习规则并动态给予学生最佳化的教学策略。XCS与增强式学习法相同点在于两者均会由未知的资料集合中萃取出有用的规则,而GBRL又可以利用基因演算法来针对规则进行繁衍并试着产生最佳化的规则。研究结果发现GBRL透过线上学习亦可产生最佳化教学策略,也证实以GBRL为设计基础的教学系统的确可以导入在真实世界的系统应用。 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 with large problems and hence were used in offline manners. Therefore, this study introduced a Genetic-Based Reinforcement Learning (GBRL) approach to induce tutorial tactics in an online-learning manner without basing on any pre-existing 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 the hypothesis about the capability of the GBRL method in inducing tutorial tactics. This suggests that the GBRL method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT070156710 http://hdl.handle.net/11536/125676 |
显示于类别: | Thesis |