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

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113393/144380 (79%)
Visitors : 51237704 Online Users : 949

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/147038

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/147038

Title:	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
Authors:	林昱辰 Lin, Yu-Chen
Contributors:	江玥慧 Chiang, Yueh-Hui 林昱辰 Lin, Yu-Chen
Keywords:	聊天機器人 GPT-4 詞嵌入餘弦相似性 ChatBot GPT-4 Word Embedding Cosine Similarity
Date:	2023
Issue Date:	2023-09-01 15:25:48 (UTC+8)
Abstract:	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.
Reference:	[1] 徐文鈺，〈影響大學生課堂主動發言的因素〉，當代教育研究季刊，第 21 卷，第 4 期，頁 41-80，2013 年 12 月。 [2] Almeida, F. and Xexéo G., “Word Embeddings: A Survey,” Master`s thesis, Federal University of Rio de Janeiro, Computer and Systems Engineering Program, 2019. [3] Atwood J., and Spolsky J., “Stack Overflow,” Internet: https://stackoverflow.com /, accessed July 23, 2023. [4] Baroni, M., Dinu, G., and Kruszewski, G., “Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors,” Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, Maryland, 2014. doi:10.3115/v1/p14-1023. [5] Brown, T. B. et al., “Language models are few-shot learners,” Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, pp. 1877-1901. [6] Church, K. and Hanks, P., “Word Association Norms, Mutual Information, and Lexicography,” Proceedings of lhe 27th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 1990, pp. 76-83. [7] Cho, K. et al., “Learning phrase representations using RNN encoder–decoder for statistical machine translation,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 1724-1734. doi:10.3115/v1/d14-1179. [8] Colby, K., Artificial Paranoia: A Computer Simulation of Paranoid Process, New York: Pergamon Press, 1975. [9] Devlin, J., Chang, M.-W., Lee, K. and Toutanova, K., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Minnesota, 2019, pp. 4171-4186. doi: 10.18653/V1/N19-1423. [10] Fielding, R., “Architectural Styles and the Design of Network-based Software Architectures,” Doctoral Dissertation, University of California, Irvine, 2000. [11] fxsjy, “Jieba,” Internet: https://github.com/fxsjy/jieba, accessed July 24, 2023. [12] Goel, A. K., and Polepeddi, L., “Jill Watson: A Virtual Teaching Assistant for Online Education,” Learning Engineering for Online Education, 1st. New York:Routledge, 2016. doi: 10.4324/9781351186193-7. [13] Goldberg, Y. and Graeme, H., “Neural Network Methods for Natural Language Processing,” Toronto, Ontario: Morgan & Claypool Publishers, ISBN: 978-3-031-02165-7, 2017. [14] Hsu, H.-H., and Huang, N.-F., “Xiao-Shih: A self-enriched question answering bot with machine learning on Chinese-based moocs,” IEEE Transactions on Learning Technologies, vol. 15, no. 2, pp. 223-237, Mar. 2022. doi:10.1109/tlt.2022.3162572. [15] Hussain, S., Sianaki, O. A., and Ababneh, N., “A survey on conversational agents/Chatbots classification and Design Techniques,” Advances in Intelligent Systems and Computing, vol. 927. Switzerland:Springer, 2019, pp. 946-956. doi:10.1007/978-3-030-15035-8_93. [16] Jones, K., “Astatistical interpretation of term speciﬁcity and its application in retrieval,” Journal of Documentation, Vol. 28 No. 1, pp. 11-21, 1972. [17] Li, F.-F., Fergus, R. and Perona, P., “One-shot learning of object categories,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, Piscataway:IEEE Computer Society, Apr. 2006. doi: 10.1109/TPAMI.2006.79. [18] LINE, “LINE Developers” Internet: https://developers.line.biz/ , accessed May 9, 2023. [19] Manning, C., Raghavan, P. and Schütze, H., Introduction to Information Retrieval. Cambridge, U.K.: Cambridge Univ. Press, 2008. [20] Mikolov, T., Chen, K., Corrado, G. 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. [25] Radford, A., Sutskever, I., Salimans, T., and Narasimhan, K., “Improving language understanding by generative pre-training,” 2018. [26] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. and Sutskever, I., “Language models are unsupervised multitask learners,” 2019. [27] Raffel, C. et al, “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”, The Journal of Machine Learning Research, vol. 21, no. 140, pp. 5485-5551, May 2020. [28] Robertson, S. and Zaragoza, H., “The probabilistic relevance framework: BM25 and beyond,” Foundations Trends Inf. Retrieval, vol. 3, no. 4, pp. 333–389, Dec. 2009, doi: 10.1561/1500000019. [29] Ulstad, S. O., Halvari, H., Sørebø, Ø., and Deci, E. L., “Motivational predictors of learning strategies, participation, exertion, and performance in Physical Education: A randomized controlled trial,” Motivation and Emotion, vol. 42, no. 4, pp. 497-512, 2018. doi:10.1007/s11031-018-9694-2. [30] Vaswani, A. et al., “Attention is all you need,” Advances in Neural Information Processing Systems, Long Beach, California, 2017, pp. 6000-6010. [31] Wales, J. and Sanger, L., “Wikipedia” Internet: https://www.wikipedia.org/ , accessed May 9, 2023. [32] Wallace, R., “A.L.I.C.E - Artificial Intelligence Foundation,” Internet: http://www.alicebot.org, accessed June 1, 2023. [33] Weizenbaum, J., “ELIZA--A Computer Program For the Study of Natural Language Communication Between Man and Machine,” Communications of the ACM, vol. 9, no. 1, pp. 36–45, 1966. doi:10.1145/365153.365168
Description:	碩士國立政治大學資訊科學系 110753163
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0110753163
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

Files in This Item:

File	Description	Size	Format
316301.pdf		3711Kb	Adobe PDF2	0	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback