結合生成式人工智慧與4F動態回顧循環理論於國小閱讀學習同伴系統的應用與成效評估

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：56

、訪客IP：18.188.246.157

姓名

李曼綾(Man-Ling Lee) 查詢紙本館藏

畢業系所

網路學習科技研究所

論文名稱

結合生成式人工智慧與4F動態回顧循環理論於國小閱讀學習同伴系統的應用與成效評估
(Application and Effectiveness Evaluation of a Reading Learning Companion System that Integrates Generative AI with the 4Fs Active Reviewing Cycle Theory in Primary School)

相關論文

★ 基於間隔效應與知識追蹤之適性化學習演算法系統設計與應用：以多益英語學習為例	★ 結合社會調節學習平台與教中學課程設計以增進大學生視覺化資料分析能力與調節學習
★ 以深度知識追蹤模型應用於程式學習系統	★ 結合聊天機器人與推薦系統之閱讀學伴應用於國小閱讀
★ 視覺化閱讀歷程系統於國小身教式持續安靜閱讀之應用	★ 基於文本型程式編寫紀錄之自我調節儀表板於程式設計學習成效探究
★ 結合重新設計之翻轉教室模型與視覺化分析系統對於程式設計學習之影響	★ 結合視覺化儀表板與合作腳本輔助VR共創活動以探討國小學童之學習行為、情感與認知參與
★ 結合視覺化儀表板之專案管理平台於在職學生專案能力與資料分析學習之影響	★ 專題導向學習與調節學習儀表板應用於資料視覺化在職課程
★ 整合預測分析與學習儀表板以提升準時畢業率：以印尼高等教育為例	★ 結合生成式人工智慧之探究式學習同伴系統以增進研究生資料視覺化素養能力
★ 應用指數平滑法實現短期學習成效預測與學習歷程儀表板系統建置	★ 應用生成式模型輔助問題生成學習系統於國小社會課程之研究

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 (2029-8-1以後開放)

摘要(中)

在台灣，身教式持續安靜閱讀(MSSR)已廣泛實施，但由於大師生比，學生在閱讀速度以及閱讀興趣上大不相同，教師難以對每位學生的閱讀成效進行有效評估。為解決這個問題，本研究結合生成式人工智慧(GenAI)聊天機器人，以及4F動態回顧循環 (Active Reviewing Cycle)理論，開發閱讀學習同伴平台，以OpenAI的Assistants API串接，建立聊書機器人做為學生的閱讀學習同伴。此閱讀學習同伴透過檢索學生所閱讀的書籍內容以及根據學生所輸入的回應，提出4F :事實、發現、感覺及未來問題，從不同角度引導學生進行書籍內容的反思、回憶及延伸，為學生提供一個互動式的閱讀體驗，從而增強學生的閱讀理解能力。
為了調查生成式AI閱讀同伴的影響及評估這種方法的有效性，本研究於台灣北部某非學校型態實驗教育機構進行為期八週的實驗，共收集了37名國小學生的閱讀理解前後測、問卷以及系統日誌資料。並建立學生的閱讀聊書互動歷程分析儀表板，以視覺化圖表方式為教師及學生提供聊書互動過程中的行為量化數據，如:聊書次數、與學習同伴互動的聊書來回數，以及在互動過程中所觸發的模組(事實、未來等)序列等資訊，為教師提供一個可以即時了解班級中所有學生聊書互動之工具。透過這種方式學生不但能即時的與學習同伴進行聊書獲得即時的反饋，也能透過觀察閱讀歷程分析儀表板上的資訊，來提升自己的閱讀及聊書策略。
研究結果顯示，使用AI聊天機器人作為閱讀學習同伴可以顯著提高學生的閱讀理解能力。然而，就學生的動機而言，結果並沒有顯示出顯著的進步。在培養學生動機的能力方面，仍需進一步的策略和方法的探索。本研究所開發之閱讀互動歷程分析儀表板也為學生及教師提供了即時的反饋，教師能夠透過4F問題之回答，監控學生的閱讀聊書狀況，從而進行更有效的教學策略調整，也為學生提供一個觀察自己或同儕聊書互動的自我監控工具，以更好的調整和提升自己的聊書互動行為。
綜上所述，本研究證實生成式AI聊天機器人結合4F問題在閱讀教育領域，特別是在提高國小學生閱讀理解能力方面的潛力，閱讀互動歷程儀表板也為教育者提供了一個有效的工具來監控和促進學生的閱讀進展。未來的研究可望通過改進生成式AI技術和互動設計，進一步拓展生成式AI在教育領域的應用，特別是在提升學生的動機及自我調整能力方面，使生成式AI成為更有效的學習同伴。

摘要(英)

In Taiwan, the implementation of Modeled Sustained Silent Reading (MSSR) in primary schools is widespread. However, due to the high student-teacher ratio, students vary significantly in their reading speeds and interests, making it challenging for teachers to effectively assess each student′s reading outcomes. To address this issue, the study incorporates Generative Artificial Intelligence (GenAI) chatbot and the 4Fs Active Reviewing Cycle theory to develop a Reading Companion Platform. Leveraged by OpenAI′s Assistants API, the platform establishes a Book Chatbot as a reading companion for students. This companion engages students by retrieving the content of books they read and generating questions based on the 4Fs questions: Facts, Findings, Feelings, and Future, thus guiding students through reflection, recall, and extension of the book content from different perspectives, providing an interactive reading experience that enhances reading comprehension.
To investigate the impact and assess the effectiveness of the GenAI reading companion, an eight-week experiment was conducted in a primary school in northern Taiwan, involving 37 primary students. The study collected data on reading comprehension pre- and post-tests, questionnaires, and system logs. A Reading Portfolio Dashboard for analyzing student-book talk interactions was also developed, providing visualized data charts to teachers and students. These charts displayed quantitative data such as the number of chat sessions, the number of interactions with the reading companion, and the sequence of triggered modules (Fact, Future, etc.) during the interactions, offering teachers a real-time tool to understand all students′ book talk interactions within the class. Through this method, students could not only talk about books with their reading companion for immediate feedback but also enhance their reading and book talking strategies by observing the data on the Reading Portfolio Dashboard.
The results indicated that using an AI chatbot as a reading companion significantly improved students′ reading comprehension. However, there were no significant improvements in students′ motivation. Further exploration of strategies and methods is needed to cultivate students′ motivational capabilities. The Reading Portfolio Dashboard developed in this study provided students and teachers with immediate feedback. Teachers were able to monitor the students′ interactions during book talk interactions through responses to the 4F questions, enabling them to adjust teaching strategies more effectively. Additionally, the dashboard offered students a self-monitoring tool to observe their own or their peers′ interactions in book talk interactions, helping them improve and fine-tune their engagement in these discussions.
In conclusion, this study confirms the potential of Generative AI chatbots in the field of reading education, especially in enhancing primary students′ reading comprehension. It also provides educators with an efficient tool to track and support students′ reading development. Future research could broaden the application of Generative AI in education by enhancing AI technologies and interactive designs, particularly in boosting students′ motivation and self-regulation skills, thus making Generative AI a more valuable learning companion.

關鍵字(中)

★ 生成式人工智慧
★ 4F動態回顧循環
★ 聊天機器人
★ 學習同伴

關鍵字(英)

★ Generative Artificial Intelligence
★ 4Fs Active Reviewing Cycle
★ Chatbot
★ Reading Companion

論文目次

摘要 i
Abstract iii
目錄 vii
圖目錄 xi
表目錄 xiii
一、緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究問題 3
1-4 名詞解釋 3
二、文獻回顧 5
2-1 聊天機器人與生成式人工智慧 5
2-1-1 聊天機器人 5
2-1-2 教育聊天機器人 5
2-1-3 生成式人工智慧 6
2-2 閱讀相關理論 7
2-2-1 閱讀理解 7
2-2-2 廣泛閱讀 8
2-2-3 對話式閱讀 9
2-3 興趣驅動創造者理論與身教式持續安靜閱讀 10
2-3-1 興趣驅動創造者理論 10
2-3-2 身教式持續安靜閱讀 11
2-4 學習歷程及學習分析儀表板 12
2-4-1 學習歷程 12
2-4-2 學習分析儀表板 12
三、研究方法 14
3-1 研究對象 14
3-2 研究流程 15
3-3 研究工具 16
3-3-1 閱讀理解能力測驗 16
3-3-2 動機、自我調整、反思問卷 17
3-3-3 互動情境興趣問卷 18
3-3-4 聊書及系統使用問卷 18
3-3-5 閱讀互動歷程儀表板 19
3-3-6 書籍深度分級標準 19
3-4 實驗工具 20
3-5 資料分析 23
3-5-1 信度分析 23
3-5-2 常態檢定 24
3-5-3 敘述性統計 25
3-5-4 成對樣本T檢定 25
3-5-5 共變異數分析 25
3-5-6 變異數分析與LSD事後比較 26
3-5-7 滯後序列分析 26
3-5-8 Cohen′s Kappa 26
四、系統設計與實作 28
4-1 系統環境架構 28
4-2 系統處理流程 29
4-3 系統功能介紹 33
4-3-1 瀏覽書籍列表 34
4-3-2 聊書機器人 36
4-3-3 聊書互動紀錄檢視 36
4-3-4 個人閱讀互動歷程分析儀表板 39
4-4 教師端工具 41
4-4-1 書籍討論主題 42
4-4-2 學生聊天互動紀錄 43
4-4-3 班級閱讀互動歷程分析儀表板 46
五、研究結果 49
5-1 閱讀理解能力 49
5-2 動機、反思、自我調整問卷 50
5-3 互動情境興趣 55
5-4 聊書及系統使用分析 57
5-4-1 學生在使用系統上之困難與挑戰 57
5-4-2 學生對於系統未來設計之看法 58
5-4-3 學生對於儀表板成效評估 59
5-4-4 學生對於聊書對象之偏好 60
5-5 教師系統使用調查 61
5-5-1 教師對於學生使用成效評估 61
5-5-2 教師系統使用情況評估 63
5-5-3 教師對於學生聊書紀錄之呈現方式之探討 66
5-5-4 教師對於儀表板有效性之探討 66
5-5-5 系統功能對於學生之效用探討 67
5-5-6 系統設計結合聊書機器人及儀表板之探討 67
5-5-7 系統對學生閱讀聊書興趣和理解能力的影響 68
5-6 聊書互動歷程分析 69
5-6-1 互動次數之行為分析 69
5-6-2 學生與機器人聊書之投入程度 70
5-6-3 學生與機器人聊書之模組觸發序列 71
5-7 學生行為對其態度及閱讀理解之影響 78
5-7-1 聊書互動行為對學生閱讀理解之影響 78
5-7-2 聊書互動行為對學生互動情境之影響 80
5-7-3 互動中模組觸發頻率分析 82
六、討論 87
6-1 閱讀學習同伴之影響 87
6-1-1 閱讀學習同伴對學生閱讀理解之影響 87
6-1-2 閱讀理解能力與動機之影響 87
6-1-3 生成式AI做為學習同伴之影響 90
6-2 聊書互動問題模組觸發 90
6-2-1 學生模組觸發分析 90
6-2-2 4F問題模組觸發分析 94
七、結論 96
7-1 研究結論 96
7-1-1 機器人閱讀學習同伴提升學生閱讀理解能力 96
7-1-2 學生閱讀聊書動機在使用後顯著降低 97
7-1-3 不同聊書行為對於學生的閱讀理解進步沒有顯著影響 97
7-1-4 儀表板幫助學生反思自己與同儕之聊書互動情形 98
7-1-5 儀表板有效幫助教師了解學生之閱讀情況 98
7-1-6 閱讀學習同伴解決教師與所有學生同時互動之限制 98
7-2 研究限制 99
7-3 未來展望 100
參考文獻 102
附件一、行為與社會科學研究倫理委員審查 108
附件二、知情同意書 109
附件三、閱讀理解能力測驗前測題目 117
附件四、閱讀理解能力測驗後測題目 122
附件五、動機、自我調整、反思問卷 127
附件六、互動情境興趣問卷 130
附件七、聊書及系統使用問卷 133
附件八、教師問卷 134
附件九、教師問卷紀錄表 137
附件十、學生實際使用情況 139
附件十一、模組觸發殘差表 140

參考文獻

徐銘駿 (2020)。具推薦書籍功能之閱讀島架構設計。〔未出版之碩士論文〕。國立中央大學。
曾干育&李世昌 (2023)。以認知特性為基礎之餐旅職場倫理課程設計與實施。管理實務與理論研究, 17(3), 80-94。https://doi.org/10.29916/JMPP.202312_17(3).0006
許愛玲 (2020)。國小學童口腔保健素養教育介入效果研究。〔碩士論文〕。臺灣師範大學。
吳訂宜&王就貞 (2017)。身教式持續安靜晨讀對國小四年級學童閱讀理解能力與閱讀態度影響之研究。科學與人文研究, 4(2), 86-102。https://doi.org/10.6535/JSH2017014203
唐寓琳 (2021)。身教式持續安靜閱讀對於國小五年級學童閱讀動機與閱讀習慣影響之研究。〔未出版之碩士論文〕。政治大學。
韓景琪 (2017)。從身教式持續安靜閱讀看學校推行晨讀活動對國小學童閱讀態度之影響-以桃園市某國小為例。〔碩士論文〕。健行科技大學。
陳德懷 (2016)。明日閱讀：明日主題學習的基礎。天下雜誌。
Aguilar, S. J., Karabenick, S. A., Teasley, S. D., & Baek, C. (2021). Associations between learning analytics dashboard exposure and motivation and self-regulated learning. Computers & Education, 162, 104085. https://doi.org/10.1016/j.compedu.2020.104085
Aljohani, N. R., & Davis, H. C. (2013). Learning analytics and formative assessment to provide immediate detailed feedback using a student centered mobile dashboard. 2013 Seventh international conference on next generation mobile apps, services and technologies,
Annamalai, N., Ab Rashid, R., Hashmi, U. M., Mohamed, M., Alqaryouti, M. H., & Sadeq, A. E. (2023). Using chatbots for English language learning in higher education. Computers and Education: Artificial Intelligence, 5, 100153. https://doi.org/10.1016/j.caeai.2023.100153
Arnold, N. (2009). Online extensive reading for advanced foreign language learners: An evaluation study. Foreign Language Annals, 42(2), 340-366. https://doi.org/10.1111/j.1944-9720.2009.01024.x
Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. https://digitalcommons.uri.edu/cba_facpubs/548
Ayedoun, E., Hayashi, Y., & Seta, K. (2015). A conversational agent to encourage willingness to communicate in the context of English as a foreign language. Procedia Computer Science, 60, 1433-1442. https://doi.org/10.1016/j.procs.2015.08.219
Büyükduman, İ., & Şirin, S. (2010). Learning portfolio (LP) to enhance constructivism and student autonomy. Procedia-Social and Behavioral Sciences, 3, 55-61. https://doi.org/10.1016/j.sbspro.2010.07.012
Bansal, H., & Khan, R. (2018). A review paper on human computer interaction. Int. J. Adv. Res. Comput. Sci. Softw. Eng, 8(4), 53. https://doi.org/10.23956/ijarcsse.v8i4.630
Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S.-L. (2009). Measuring self-regulation in online and blended learning environments. The internet and higher education, 12(1), 1-6. https://doi.org/10.1016/j.iheduc.2008.10.005
Beaudry, J., Consigli, A., Clark, C., & Robinson, K. J. (2019). Getting ready for adult healthcare: designing a chatbot to coach adolescents with special health needs through the transitions of care. Journal of pediatric nursing, 49, 85-91. https://doi.org/10.1016/j.pedn.2019.09.004
Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach′s alpha. Bmj, 314(7080), 572. https://doi.org/10.1136/bmj.314.7080.572
Chan, T.-W. (1988). Studying with the prince: The computer as a learning companion. Proc. of ITS 88.
Chan, T.-W., Looi, C.-K., Chen, W., Wong, L.-H., Chang, B., Liao, C. C., Cheng, H., Chen, Z.-H., Liu, C.-C., & Kong, S.-C. (2018). Interest-driven creator theory: Towards a theory of learning design for Asia in the twenty-first century. Journal of Computers in Education, 5, 435-461. https://doi.org/10.1007/s40692-018-0122-0
Chang, C.-Y., Kuo, S.-Y., & Hwang, G.-H. (2022). Chatbot-facilitated Nursing Education. Educational Technology & Society, 25(1), 15-27. https://www.jstor.org/stable/48647027
Chang, C. C. (2001). A study on the evaluation and effectiveness analysis of web‐based learning portfolio (WBLP). British Journal of Educational Technology, 32(4), 435-458. https://doi.org/10.1111/1467-8535.00212
Chang, C. Y., Hwang, G. J., & Gau, M. L. (2022). Promoting students′ learning achievement and self‐efficacy: A mobile chatbot approach for nursing training. British Journal of Educational Technology, 53(1), 171-188. https://doi.org/10.1111/bjet.13158
Charleer, S., Moere, A. V., Klerkx, J., Verbert, K., & De Laet, T. (2017). Learning analytics dashboards to support adviser-student dialogue. IEEE Transactions on Learning Technologies, 11(3), 389-399. https://doi.org/10.1109/TLT.2017.2720670
Chen, A., Darst, P. W., & Pangrazi, R. P. (1999). What constitutes situational interest? Validating a construct in physical education. Measurement in physical education and exercise science, 3(3), 157-XXX. https://doi.org/10.1207/s15327841mpee0303_3
Chen, H.-L., Vicki Widarso, G., & Sutrisno, H. (2020). A chatbot for learning Chinese: Learning achievement and technology acceptance. Journal of Educational Computing Research, 58(6), 1161-1189. https://doi.org/10.1177/0735633120929622
Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587-595. https://doi.org/10.1016/j.jbusres.2018.10.004
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46. https://doi.org/10.1177/001316446002000104
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
Danaei, D., Jamali, H. R., Mansourian, Y., & Rastegarpour, H. (2020). Comparing reading comprehension between children reading augmented reality and print storybooks. Computers & Education, 153, 103900. https://doi.org/10.1016/j.compedu.2020.103900
Dargue, B., & Biddle, E. (2014). Just Enough Fidelity in Student and Expert Modeling for ITS: Making the Practice Practical. Foundations of Augmented Cognition. Advancing Human Performance and Decision-Making through Adaptive Systems: 8th International Conference, AC 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014. Proceedings 8,
Day, R., & Bamford, J. (2002). Top ten principles for teaching extensive reading. http://hdl.handle.net/10125/66761
Doty, D. E., Popplewell, S. R., & Byers, G. O. (2001). Interactive CD-ROM storybooks and young readers’ reading comprehension. Journal of Research on Computing in Education, 33(4), 374-384. https://doi.org/10.1080/08886504.2001.10782322
Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., & Baah, P. K. (2022). The impact of a virtual teaching assistant (chatbot) on students′ learning in Ghanaian higher education. International Journal of Educational Technology in Higher Education, 19(1), 1-19. https://doi.org/10.1186/s41239-022-00362-6
George, D., & Mallery, P. (2019). IBM SPSS statistics 26 step by step: A simple guide and reference. Routledge. https://doi.org/10.4324/9780429056765
Grabe, W., & Stoller, F. L. (2019). Teaching and researching reading. Routledge.
Greenaway, R. (2018). The Active Reviewing Cycle| Reviewing Skills Tutorial. Reviewing. co. uk. http://reviewing.co.uk/learning-cycle/index.htm
Grové, C. (2021). Co-developing a mental health and wellbeing chatbot with and for young people. Frontiers in psychiatry, 11, 606041. https://doi.org/10.3389/fpsyt.2020.606041
Han, Z., Battaglia, F., Udaiyar, A., Fooks, A., & Terlecky, S. R. (2023). An explorative assessment of ChatGPT as an aid in medical education: Use it with caution. MedRxiv, 2023.2002. 2013.23285879. https://doi.org/10.1101/2023.02.13.23285879
Hapsari, I. P., & Wu, T.-T. (2022). AI Chatbots learning model in English speaking skill: Alleviating speaking anxiety, boosting enjoyment, and fostering critical thinking. International Conference on Innovative Technologies and Learning,
Hidayati, M., Inderawati, R., & Loeneto, B. (2020). The correlations among critical thinking skills, critical reading skills and reading comprehension. English Review: Journal of English Education, 9(1), 69-80. https://doi.org/10.25134/erjee.v9i1.3780
Huh, S. (2023). Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study. J Educ Eval Health Prof, 20(1). https://doi.org/10.3352/jeehp.2023.20.1
Hwang, G.-J., & Chen, N.-S. (2023). Editorial Position Paper. Educational Technology & Society, 26(2). https://doi.org/10.30191/ETS.202304_26(2).0014
Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H. (2023). Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(2), 100115. https://doi.org/10.1016/j.tbench.2023.100115
Jeon, J. (2021). Chatbot-assisted dynamic assessment (CA-DA) for L2 vocabulary learning and diagnosis. Computer Assisted Language Learning, 1-27. https://doi.org/10.1080/09588221.2021.1987272
Jovanovic, M., & Campbell, M. (2022). Generative artificial intelligence: Trends and prospects. Computer, 55(10), 107-112. https://doi.org/10.1109/MC.2022.3192720
Kılıçkaya, F. (2020). Using a chatbot, Replika, to practice writing through conversations in L2 English: A Case study. In New Technological applications for foreign and second language learning and teaching (pp. 221-238). IGI Global. https://doi.org/10.4018/978-1-7998-2591-3.ch011
Kim, J., Jo, I.-H., & Park, Y. (2016). Effects of learning analytics dashboard: analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pacific Education Review, 17, 13-24. https://doi.org/10.1007/s12564-015-9403-8
Krashen, S. (1982). Principles and practice in second language acquisition.
Lee, D., & Yeo, S. (2022). Developing an AI-based chatbot for practicing responsive teaching in mathematics. Computers & Education, 191, 104646. https://doi.org/10.1016/j.compedu.2022.104646
Lee, Y.-F., Hwang, G.-J., & Chen, P.-Y. (2022). Impacts of an AI-based chabot on college students’ after-class review, academic performance, self-efficacy, learning attitude, and motivation. Educational technology research and development, 70(5), 1843-1865. https://doi.org/10.1007/s11423-022-10142-8
Liew, T. W., Mat Zin, N. A., & Sahari, N. (2017). Exploring the affective, motivational and cognitive effects of pedagogical agent enthusiasm in a multimedia learning environment. Human-centric Computing and Information Sciences, 7(1), 1-21. https://doi.org/10.1186/s13673-017-0089-2
Lin, C.-C., Lin, V., Liu, G.-Z., Kou, X., Kulikova, A., & Lin, W. (2020). Mobile-assisted reading development: a review from the Activity Theory perspective. Computer Assisted Language Learning, 33(8), 833-864. https://doi.org/10.1080/09588221.2019.1594919
Liu, C.-C., Liao, M.-G., Chang, C.-H., & Lin, H.-M. (2022). An analysis of children’interaction with an AI chatbot and its impact on their interest in reading. Computers & Education, 189, 104576. https://doi.org/10.1016/j.compedu.2022.104576
Lu, O. H., Huang, A. Y., Huang, J. C., Lin, A. J., Ogata, H., & Yang, S. J. (2018). Applying learning analytics for the early prediction of Students′ academic performance in blended learning. Journal of Educational Technology & Society, 21(2), 220-232. https://www.jstor.org/stable/26388400
Lynch, L., Fawcett, A. J., & Nicolson, R. I. (2000). Computer‐assisted reading intervention in a secondary school: an evaluation study. British Journal of Educational Technology, 31(4), 333-348. https://doi.org/10.1111/1467-8535.00166
Mayer, R. E. (1996). Learning strategies for making sense out of expository text: The SOI model for guiding three cognitive processes in knowledge construction. Educational psychology review, 8, 357-371. https://doi.org/10.1007/BF01463939
McCracken, R. A. (1971). Initiating sustained silent reading. Journal of Reading, 14(8), 521-583. https://www.jstor.org/stable/40009700
McQuillan, J. (2019). Forced pleasure reading may get you neither: comment on milliner (2017). Language and Language Teaching, 8(1), 18-20.
Ngai, E. W., Lee, M. C., Luo, M., Chan, P. S., & Liang, T. (2021). An intelligent knowledge-based chatbot for customer service. Electronic Commerce Research and Applications, 50, 101098. https://doi.org/10.1016/j.elerap.2021.101098
Pardos, Z. A., & Bhandari, S. (2023). Learning gain differences between ChatGPT and human tutor generated algebra hints. arXiv preprint arXiv:2302.06871. https://doi.org/10.48550/arXiv.2302.06871
Pintrich, P. R., Smith, D. A., García, T., & McKEACHIE, W. J. (1991). The motivated strategies for learning questionnaire (MSLQ). Ann Arbor, MI: NCRIPTAL, The University of Michigan.
Qadir, J. (2023). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. 2023 IEEE Global Engineering Education Conference (EDUCON),
Schmitt, N. (2008). Instructed second language vocabulary learning. Language teaching research, 12(3), 329-363. https://doi.org/10.1177/1362168808089921
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior, 107, 105512. https://doi.org/10.1016/j.chb.2018.05.004
Selamat, M. A., & Windasari, N. A. (2021). Chatbot for SMEs: Integrating customer and business owner perspectives. Technology in Society, 66, 101685. https://doi.org/10.1016/j.techsoc.2021.101685
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30.
Snow, C. (2002). Reading for understanding: Toward an R&D program in reading comprehension. Rand Corporation.
Somadayo, S., Slamet, S. Y., Nurkamto, J., & Suwandi, S. (2013). The Effect of Learning Model Drta (Directed Reading Thingking Activity) Toward Students’ Reading Comprehension Ability Seeing from Their Reading Interest. Journal of Education and Practice, 4(8), 115-122.
Valle, N., Antonenko, P., Valle, D., Sommer, M., Huggins-Manley, A. C., Dawson, K., Kim, D., & Baiser, B. (2021). Predict or describe? How learning analytics dashboard design influences motivation and statistics anxiety in an online statistics course. Educational technology research and development, 69(3), 1405-1431. https://doi.org/10.1007/s11423-021-09998-z
Verleger, M., & Pembridge, J. (2018). A pilot study integrating an AI-driven chatbot in an introductory programming course. 2018 IEEE frontiers in education conference (FIE),
Vázquez-Cano, E., Mengual-Andrés, S., & López-Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18(1), 1-20. https://doi.org/10.1186/s41239-021-00269-8
Wen, Y., & Song, Y. (2021). Learning analytics for collaborative language learning in classrooms. Educational Technology & Society, 24(1), 1-15. https://www.jstor.org/stable/10.2307/26977853
Winkler, R., & Söllner, M. (2018). Unleashing the potential of chatbots in education: A state-of-the-art analysis. Academy of Management Proceedings,
Xu, Y., Aubele, J., Vigil, V., Bustamante, A. S., Kim, Y. S., & Warschauer, M. (2022). Dialogue with a conversational agent promotes children’s story comprehension via enhancing engagement. Child Development, 93(2), e149-e167. https://doi.org/10.1111/cdev.13708
Xu, Y., Wang, D., Collins, P., Lee, H., & Warschauer, M. (2021). Same benefits, different communication patterns: Comparing Children′s reading with a conversational agent vs. a human partner. Computers & Education, 161, 104059. https://doi.org/10.1016/j.compedu.2020.104059
Yan, D. (2023). Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation. Education and Information Technologies, 1-25. https://doi.org/10.1007/s10639-023-11742-4
Yang, H., Kim, H., Lee, J. H., & Shin, D. (2022). Implementation of an AI chatbot as an English conversation partner in EFL speaking classes. ReCALL, 1-17. https://doi.org/10.1017/S0958344022000039
Yang, Y., Majumdar, R., Li, H., Flanagan, B., & Ogata, H. (2022). Design of a learning dashboard to enhance reading outcomes and self-directed learning behaviors in out-of-class extensive reading. Interactive Learning Environments, 1-18. https://doi.org/10.1080/10494820.2022.2101126
Yilmaz, R., & Yilmaz, F. G. K. (2023). The effect of generative artificial intelligence (AI)-based tool use on students′ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 100147. https://doi.org/10.1016/j.caeai.2023.100147
Yin, J., Goh, T.-T., Yang, B., & Xiaobin, Y. (2021). Conversation technology with micro-learning: The impact of chatbot-based learning on students’ learning motivation and performance. Journal of Educational Computing Research, 59(1), 154-177. https://doi.org/10.1177/0735633120952067
Yousef, A. M. F., & Khatiry, A. R. (2021). Cognitive versus behavioral learning analytics dashboards for supporting learner’s awareness, reflection, and learning process. Interactive Learning Environments, 1-17. https://doi.org/10.1080/10494820.2021.2009881
Zamecnik, A., Kovanović, V., Grossmann, G., Joksimović, S., Jolliffe, G., Gibson, D., & Pardo, A. (2022). Team interactions with learning analytics dashboards. Computers & Education, 185, 104514. https://doi.org/10.1016/j.compedu.2022.104514
Zhang, J.-H., Zou, L.-c., Miao, J.-j., Zhang, Y.-X., Hwang, G.-J., & Zhu, Y. (2020). An individualized intervention approach to improving university students’ learning performance and interactive behaviors in a blended learning environment. Interactive Learning Environments, 28(2), 231-245. https://doi.org/10.1080/10494820.2019.1636078
Zhou, N., & Yadav, A. (2017). Effects of multimedia story reading and questioning on preschoolers’ vocabulary learning, story comprehension and reading engagement. Educational technology research and development, 65, 1523-1545. https://doi.org/10.1007/s11423-017-9533-2

指導教授

洪暉鈞(Hui-Chun Hung)

審核日期

2024-7-27

推文