PyChatBot：結合ChatGPT與LineBot的ChatBot用於提升學生總結寫作能力

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：31

、訪客IP：3.23.60.252

姓名

顏若軒(Ruo-Xuan Yen) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

PyChatBot：結合ChatGPT與LineBot的ChatBot用於提升學生總結寫作能力
(PyChatBot: ChatBot combining ChatGPT and LineBot is used to improve students′ summary writing ability)

相關論文

★ 應用智慧分類法提升文章發佈效率於一企業之知識分享平台	★ 家庭智能管控之研究與實作
★ 開放式監控影像管理系統之搜尋機制設計及驗證	★ 資料探勘應用於呆滯料預警機制之建立
★ 探討問題解決模式下的學習行為分析	★ 資訊系統與電子簽核流程之總管理資訊系統
★ 製造執行系統應用於半導體機台停機通知分析處理	★ Apple Pay支付於iOS平台上之研究與實作
★ 應用集群分析探究學習模式對學習成效之影響	★ 應用序列探勘分析影片瀏覽模式對學習成效的影響
★ 一個以服務品質為基礎的網際服務選擇最佳化方法	★ 維基百科知識推薦系統對於使用e-Portfolio的學習者滿意度調查
★ 學生的學習動機、網路自我效能與系統滿意度之探討-以e-Portfolio為例	★ 藉由在第二人生內使用自動對話代理人來改善英文學習成效
★ 合作式資訊搜尋對於學生個人網路搜尋能力與策略之影響	★ 數位註記對學習者在線上學習環境中反思等級之影響

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

本研究探討了使用聊天機器人以提升學生總結寫作能力，並分析學生的學習行為與學習成效間的關聯性。聊天機器人可以提供學習者即時的幫助，節省等待助教或教師回復的時間，從而提高學習效率。此外，聊天機器人也可以通過一對一的交談方式提供個性化學習。然而，在教育領域中，大部分對聊天機器人的評估都是使用學生問卷，很少有對學生的學習行為進行分析。
在本研究中，我們開發了一個名為PyChatBot的聊天機器人，作為學生的課後輔助學習工具。除了提供總結寫作反饋外，它還作為學生和助教之間的交流平台。在學生提問時，PyChatBot會自動抓取學生在線上解題環境中的作答紀錄，並發送給助教，以幫助助教更好地理解學生的問題。本研究旨在通過使用PyChatBot收集學生的學習歷程，並進行總結寫作反饋活動，探討基於LIME的總結寫作反饋對學生總結寫作能力的影響，以及總結寫作分數與程式概念和程式實作學習成效的相關性。此外，我們還將探討在使用聊天機器人時，哪些學習行為與學習成效有關。在總結寫作反饋的生成方面，我們使用了ChatGPT來輔助教師生成反饋所需的相關教材，並使用SBERT代替傳統的LSA，以減輕教師的負擔。
實驗結果表明，使用結合ChatGPT與LineBot的聊天機器人能有效地提升學生總結寫作能力，且評估出的總結寫作分數與學生的程式概念分數存在顯著的相關性，在學習分析的方面，我們發現學生的學習成效與使用聊天機器人各個功能的次數無關，只與使用的時間有所相關。

摘要(英)

This study aims to investigate the effectiveness of utilizing chatbots to enhance students′ summary writing skills and to analyze the relationship between students′ learning behavior and their learning outcome. Chatbots offer immediate support to learners, reducing the wait time for assistance and improving overall learning efficiency. Moreover, chatbots provide personalized learning experiences through one-on-one interactions.
In this research, we developed PyChatBot as a tool for students′ post-class learning assistance. Alongside providing feedback on summary writing, PyChatBot serves as a platform for communication between students and teaching assistants. The study focuses on utilizing PyChatBot to gather students′ learning processes and conduct summary writing feedback activities. It explores the impact of LIME-based summary writing feedback on students′ summarization abilities, as well as the correlation between summary writing scores and learning outcomes. Additionally, the study aims to identify the learning behaviors that relate to learning outcomes when using chatbots.
To generate summary writing feedback, we employed ChatGPT to assist teachers in producing relevant instructional materials and substituted traditional LSA with SBERT to alleviate the workload for teachers.
Experimental results indicate that incorporating a chatbot that combines ChatGPT and LineBot effectively enhances students′ summary writing skills. There is a significant correlation between summary writing scores and students′ programming concept scores . Regarding learning analytics, the study found that students′ learning outcomes are unrelated to the frequency of using specific chatbot functions but rather depend on the duration of usage.

關鍵字(中)

★ 聊天機器人
★ 總結寫作反饋
★ 學習分析
★ LIME

關鍵字(英)

★ ChatBot in Education
★ Summary Writing Feedback
★ Learning Analytics
★ LIME

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 vi
圖目錄 viii
表目錄 x
1 緒論 1
2 文獻探討 4
2.1 聊天機器人在教育上的應用(Chatbot in Education) 4
2.2 總結寫作反饋(Summary Writing Feedback) 5
2.3 學習分析(Learning Analytics) 7
3 PyChatBot介紹 9
3.1 PyChatBot功能介紹 9
3.2 PyChatBot收集資料介紹 14
4 方法論 15
4.1 參與者 15
4.2 學習環境介紹 16
4.3 實驗設計與課程活動 19
4.4 基於LIME的總結寫作反饋 21
4.4.1 總結寫作評分方式 22
4.4.2 總結寫作反饋內容 24
4.4.3 總結範例和關鍵字提示自動生成 27
5 研究結果 31
5.1 總結寫作能力提升效果分析 31
5.2 總結寫作分數與學習成效的相關性分析 33
5.3 學習行為分析 37
5.3.1 分析學習行為統計資料 37
5.3.2 分析學習行為與學習成效間的相關性 40
5.3.3 分析不同分數群的學習行為差異 42
5.4 學生反饋 44
6 結果與討論 46
7 限制與未來研究 47
附錄 49
附錄1. 使用ChatGPT輔助產生教材總結與關鍵字及其提示的範例 49
參考文獻 53

參考文獻

Akçapınar, G., Hasnine, M. N., Majumdar, R., Flanagan, B., & Ogata, H. (2019). Using learning analytics to detect off-task reading behaviors in class. Companion Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK′19),
Atif, A., Jha, M., Richards, D., & Bilgin, A. A. (2021). Artificial Intelligence (AI)-enabled remote learning and teaching using Pedagogical Conversational Agents and Learning Analytics. In Intelligent systems and learning data analytics in online education (pp. 3-29). Elsevier.
Benedetto, L., & Cremonesi, P. (2019). Rexy, a configurable application for building virtual teaching assistants. Human-Computer Interaction–INTERACT 2019: 17th IFIP TC 13 International Conference, Paphos, Cyprus, September 2–6, 2019, Proceedings, Part II 17,
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., & Askell, A. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
Budianto, A., Setyosari, P., Kuswandi, D., & Ulfa, S. (2022). Summaries writing to enhance reading comprehension: Systematic literature review from 2014 to 2021. Eurasian Journal of Applied Linguistics, 8(1), 149-161.
Capuano, N., Caballé, S., Conesa, J., & Greco, A. (2021). Attention-based hierarchical recurrent neural networks for MOOC forum posts analysis. Journal of Ambient Intelligence and Humanized Computing, 12, 9977-9989.
Chandrashekara, A. A., Talluri, R. K. M., Sivarathri, S. S., Mitra, R., Calyam, P., Kee, K., & Nair, S. (2018). Fuzzy-based conversational recommender for data-intensive science gateway applications. 2018 IEEE International Conference on Big Data (Big Data),
Clow, D. (2012). The learning analytics cycle: closing the loop effectively. Proceedings of the 2nd international conference on learning analytics and knowledge,
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683-695.
Crockett, K., Latham, A., Mclean, D., & O′Shea, J. (2013). A fuzzy model for predicting learning styles using behavioral cues in an conversational intelligent tutoring system. 2013 IEEE international conference on fuzzy systems (FUZZ-IEEE),
Duke, N. K., & Pearson, P. D. (2009). Effective practices for developing reading comprehension. Journal of education, 189(1-2), 107-122.
Dumais, S. T. (2004). Latent semantic analysis. Annu. Rev. Inf. Sci. Technol., 38(1), 188-230.
Engwall, O. (2012). Analysis of and feedback on phonetic features in pronunciation training with a virtual teacher. Computer Assisted Language Learning, 25(1), 37-64.
Flanagan, B., & Ogata, H. (2017). Integration of learning analytics research and production systems while protecting privacy. The 25th International Conference on Computers in Education, Christchurch, New Zealand,
Hayashi, Y. (2016). Lexical network analysis on an online explanation task: Effects of affect and embodiment of a pedagogical agent. IEICE TRANSACTIONS on Information and Systems, 99(6), 1455-1461.
Huang, A. Y., Lu, O. H., Huang, J. C., Yin, C., & Yang, S. J. (2020). Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interactive Learning Environments, 28(2), 206-230.
Hume, A., Cernuzzi, L., Zarza, J. L., Bison, I., & Gatica-Perez, D. (2022). Analysis of the Big-Five personality traits in the Chatbot “UC-Paraguay”. CLEI electronic journal, 25(2).
Hwang, G.-J., & Chang, C.-Y. (2021). A review of opportunities and challenges of chatbots in education. Interactive Learning Environments, 1-14.
Jiang, Z., Xu, F. F., Araki, J., & Neubig, G. (2020). How can we know what language models know? Transactions of the Association for Computational Linguistics, 8, 423-438.
Khalil, M., & Ebner, M. (2015). Learning analytics: principles and constraints. EdMedia+ Innovate Learning,
Kitchakarn, O. (2012). Using blogs to improve students’ summary writing abilities. Turkish Online Journal of Distance Education, 13(4), 209-219.
Kurnia, A., Lim, A., & Cheang, B. (2001). Online judge. Computers & Education, 36(4), 299-315.
Li, J., Ling, L., & Tan, C. W. (2021). Blending peer instruction with just-in-time teaching: jointly optimal task scheduling with feedback for classroom flipping. Proceedings of the Eighth ACM Conference on Learning@ Scale,
Lim, M. S., Ho, S.-B., & Chai, I. (2021). Design and functionality of a university academic advisor chatbot as an early intervention to improve students’ academic performance. In Computational Science and Technology: 7th ICCST 2020, Pattaya, Thailand, 29–30 August, 2020 (pp. 167-178). Springer.
Lin, C.-J., & Mubarok, H. (2021). Learning analytics for investigating the mind map-guided AI chatbot approach in an EFL flipped speaking classroom. Educational Technology & Society, 24(4), 16-35.
Lu, O. H., Huang, A. Y., Huang, J. C., Huang, C. S., & Yang, S. J. (2016). Early-Stage Engagement: Applying Big Data Analytics on Collaborative Learning Environment for Measuring Learners′ Engagement Rate. 2016 International Conference on Educational Innovation through Technology (EITT),
Maldonado-Mahauad, J., Pérez-Sanagustín, M., Carvallo-Vega, J., Narvaez, E., & Calle, M. (2022). Miranda: A Chatbot for Supporting Self-regulated Learning. Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption: 17th European Conference on Technology Enhanced Learning, EC-TEL 2022, Toulouse, France, September 12–16, 2022, Proceedings,
Ocheja, P., Flanagan, B., Ueda, H., & Ogata, H. (2019). Managing lifelong learning records through blockchain. Research and Practice in Technology Enhanced Learning, 14(1), 1-19.
Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033.
Pérez, J. Q., Daradoumis, T., & Puig, J. M. M. (2020). Rediscovering the use of chatbots in education: A systematic literature review. Computer Applications in Engineering Education, 28(6), 1549-1565.
Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). " Why should i trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining,
Sasaki, T., & Masada, T. (2022). Sentence-BERT Distinguishes Good and Bad Essays in Cross-prompt Automated Essay Scoring. 2022 IEEE International Conference on Data Mining Workshops (ICDMW),
Shin, D., Kim, H., Lee, J. H., & Yang, H. (2021). Exploring the use of an artificial intelligence chatbot as second language conversation partners. Korean Journal of English Language and Linguistics, 21, 375-391.
Song, D., Rice, M., & Oh, E. Y. (2019). Participation in online courses and interaction with a virtual agent. International Review of Research in Open and Distributed Learning, 20(1).
Sung, Y.-T., Liao, C.-N., Chang, T.-H., Chen, C.-L., & Chang, K.-E. (2016). The effect of online summary assessment and feedback system on the summary writing on 6th graders: The LSA-based technique. Computers & Education, 95, 1-18.
Tegos, S., Tsiatsos, T., Psathas, G., & Demetriadis, S. (2021). Towards a learning analytics dashboard for collaborative conversational agent activities in moocs. Internet of Things, Infrastructures and Mobile Applications: Proceedings of the 13th IMCL Conference 13,
Vekaria, K., Calyam, P., Sivarathri, S. S., Wang, S., Zhang, Y., Pandey, A., Chen, C., Xu, D., Joshi, T., & Nair, S. (2021). Recommender‐as‐a‐service with chatbot guided domain‐science knowledge discovery in a science gateway. Concurrency and Computation: Practice and Experience, 33(19), e6080.
Vijayakumar, B., Höhn, S., & Schommer, C. (2019). Quizbot: Exploring formative feedback with conversational interfaces. Technology Enhanced Assessment: 21st International Conference, TEA 2018, Amsterdam, The Netherlands, December 10–11, 2018, Revised Selected Papers 21,
Wade-Stein, D., & Kintsch, E. (2004). Summary Street: Interactive computer support for writing. Cognition and instruction, 22(3), 333-362.
Wong, B. T.-m., & Li, K. C. (2020). A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 7(1), 7-28.
Yang, Y.-F. (2016). Transforming and constructing academic knowledge through online peer feedback in summary writing. Computer Assisted Language Learning, 29(4), 683-702.

指導教授

楊鎮華(Jhen-Hua Yang)

審核日期

2023-7-5

推文