XCS於教學系統之教授策略最佳化

Abstract

教學策略為學生與教授者(教學系統)互動過程中，由教授者在眾多可能產生的教學動作中選擇合適的教學動作來教導學生，在教學互動中，上述策略已經被視為一個不可或缺的教學行為模式。在先前的研究報告中已發現當學習的教材與素材在固定的條件下，學生會因教授者給予的不同教學策略影響其學習成效。先前研究報告使用增強式學習演算法讓教學系統學習最佳化教學策略。然而，當此演算法碰到較困難或複雜的學習環境時，會限制其解決問題的能力。因此，本研究使用改良式的增強式學習法，係以基因演算法為基礎之增強式學習法(GBRL)，利用線上即時學習的方式來學習規則並動態給予學生最佳化的教學策略。XCS與增強式學習法相同點在於兩者均會由未知的資料集合中萃取出有用的規則，而GBRL又可以利用基因演算法來針對規則進行繁衍並試著產生最佳化的規則。研究結果發現GBRL透過線上學習亦可產生最佳化教學策略，也證實以GBRL為設計基礎的教學系統的確可以導入在真實世界的系統應用。

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.