政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/147038

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/147038

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题名:	開發與評估教育聊天機器人：以與課程相關的內容即時支援非資訊領域大學生解決程式設計問題 Development and Evaluation of an Educational Chatbot: Providing Real-Time and Contextual Support for Non-IT University Students Facing Programming Problems
作者:	林昱辰 Lin, Yu-Chen
贡献者:	江玥慧 Chiang, Yueh-Hui 林昱辰 Lin, Yu-Chen
关键词:	聊天機器人 GPT-4 詞嵌入餘弦相似性 ChatBot GPT-4 Word Embedding Cosine Similarity
日期:	2023
上传时间:	2023-09-01 15:25:48 (UTC+8)
摘要:	隨著科技發展，國高中課綱將資訊科技納入必修課程中，希望培養學生的邏輯能力和運算思維。然而，由於課綱修改前後的學生存在學識斷層，進入大學後程式設計程度參差不齊，使得教師在設計個人化教學內容和學習資源方面面臨挑戰；學生們常因害羞或擔心同儕評價而不敢向老師或助教提問。教育聊天機器人的開發可以為學生提供個人化的學習支援，減輕教師和助教的工作負擔，提供學生便利的學習資源的同時給予了較低壓力的環境，讓他們更自在地提問和尋找解答。本研究開發的聊天機器人適用的教學場域為講授基礎Python程式設計觀念的資訊通識課程。研究中使用詞嵌入技術透過餘弦相似度選出與使用者的訊息相近的課程投影片內容來輔助聊天機器人，讓使用者與聊天機器人的對話能夠聚焦於課程討論。 With the development of technology, information technology has been included in the curriculum of junior and senior high schools, aiming to cultivate students` logical reasoning and computational thinking skills. However, due to the disparity in students` knowledge before and after the curriculum revision, there is a significant variation in their programming abilities when they enter university. This poses a challenge for teachers in designing personalized teaching content and learning resources. Additionally, students often hesitate to ask questions of their teachers or teaching assistants due to shyness or concerns about peer evaluation. The development of an educational chatbot can provide personalized learning support to students, alleviate the workload of teachers and teaching assistants, and offer students convenient learning resources in a low-pressure environment. This enables them to feel more comfortable asking questions and seeking answers. The chatbot developed in this research is designed for the educational field of teaching fundamental Python programming concepts in an introductory information technology course. In the research, word embedding techniques are used, and cosine similarity is employed to select course slide content that closely matches the user`s input. This assists the chatbot in focusing on course discussions during interactions with users.
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S. and Dean, J., “Efficient Estimation of Word Representations in Vector Space,” International Conference on Learning Representations, Scottsdale, Arizona, 2013. [21] Mikolov, T. et al., “Distributed Representations of Words and Phrases and their Compositionality,” Advances in Neural Information Processing Systems, Stateline, America, 2013, pp. 3111-3119. [22] Nss, “An intuitive understanding of word embeddings: From count vectors to word2vec,” Internet: https://www.analyticsvidhya.com/blog/2017/06/word-embeddings-count-word2veec/, accessed May 9, 2023. [23] OpenAI, “Models,” Internet: https://platform.openai.com/docs/models/overview, accessed May 9, 2023. [24] Palatucci, M., Pomerleau, D. A., Hinton, G. E. and Mitchell, T. M., “Zero-shot learning with semantic output codes,” Advances in Neural Information Processing Systems, Vancouver, Canada, 2009, pp. 1410-1418. 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描述:	碩士國立政治大學資訊科學系 110753163
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0110753163
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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