自我解釋(Self-explanation)意指學習者針對問題解決或是某事物本身所作的相關推論或解釋，以建立相關的領域概念。已有許多研究證明自我解釋策略可以有效幫助學習者釐清概念、整合新舊知識。而若搭配問答的方式引導學習者進行自我解釋，則更可以提升自我解釋之成效。然而也有進一步研究指出，學生在進行自我解釋的過程中可能會產生錯誤的解釋，致使學習者因此建構錯誤的概念。因此施教者應適時給予相關的教學回饋，及時來導正學習者之概念。而C++程式語言為大學工程類科系的熱門必修學科，其複雜之概念經常讓學習者在學習的過程中產生概念的混淆及迷思，進而阻礙其學習。
為提升自我解釋及教學回饋之成效，本研究選用開放式問答的自我解釋學習策略，學習者進行自我解釋時以自然語言輸入他們的想法與推論，系統再使用自然語言處理(NLP)技術針對學習者所輸入之自我解釋字串進行語意分析，藉此分析學習者對於該解釋處之概念理解程度及是否存在概念迷思(Misconceptions)，並適時給予相關引導教材作為回饋，盼能即時導正學習者的領域概念。為了瞭解本研究方法的可行性，本研究以C++程式設計領域為例，開發一具有自我解釋學習策略之C++線上程式撰寫系統。實驗設計上，本研究徵求30位修習過C++程式設計課程的大學部學生使用本研究所開發之系統進行實際的程式上機練習，並蒐集學習者所有的程式編寫歷程，藉此計算本系統學習者自我解釋字串語意分析之準確率。實驗結果發現本研究所開發之系統針對學習者自我解釋正確與否的判斷準確率達91.8%，而C++程式概念迷思教材的推薦準確率達88.1%。

Self-explanation gives an explicit expression on what is your thought or inference on the domain concepts of an entity generated by an activity you are engaging in. Many researches have proven that self-explanation strategy is an effective learning strategy that can help learners clarify their concepts and merge them with their knowledge. Moreover, an open-question based self-explanation strategy, which asks learners to give their self-explanation sentences to the questions, is more efficient for learning. However, studies have pointed out shortcomings of open question based self-explanation strategy, i.e. without proper prior knowledge, a user’s self-explanation may generate errors and result in constructing error concepts if proper guidance were not given. Error concepts surely will become obstacles in further knowledge constructions. It is important to give teaching material feedbacks to learners if their self-explanations have misconceptions. On the other hand, C++ programming language is the must course for most engineering students. However, its complicated programming concepts usually make student lost and confused.
This study uses C++ programming language as the subject and constructs an online programming system embedded with self-explanation strategy that gives users instant feedbacks to correct their possible misconceptions. The self-explanation strategy uses open questions, so that it can ask programmer to give their self-explanations on their thoughts and inferences by their nature language. The system uses natural language processing technology for semantic analysis on self-explanation sentences and, according to the result, gives proper feedbacks to learners to revise their misconceptions. The final evaluation experiment recruited 30 college students who used this system for actual C++ programming. The system records all students’ programming activities, analyzes their self-explanations for misconceptions, and gives proper feedbacks for students. Verified manually by programming experts, the system reaches an average of 91.8% accuracy of misconception identification in self-explanation sentence processing and reaches an average of 88.1% accuracy in teaching material recommendation for the misconceptions.

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