結合聊天機器人與推薦系統之閱讀學伴應用於國小閱讀

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姓名

鄭博晏(Bo-Yan Zheng) 查詢紙本館藏

畢業系所

網路學習科技研究所

論文名稱

結合聊天機器人與推薦系統之閱讀學伴應用於國小閱讀
(Implementation of a Reading Companion System Integrating Chatbot and Book Recommendations to Support Elementary Students′ Reading)

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至系統瀏覽論文 (2027-8-1以後開放)

摘要(中)

閱讀是獲得知識最根本方式，過往教師較難找尋符合學生難度書籍幫助學生跳脫閱讀舒適圈。而藉由數位科技導入，將閱讀學習歷程、學生個人化資訊及書籍資訊整合至數位平台中，學生可透過學習歷程檔案來監控自己，教師得以透過閱讀歷程檔案瞭解每位學生閱讀狀況。此外，學者提出「學習同伴」概念促使學生與同伴互動、詢問資訊，以吸引學生注意並促進學生參與。自我調整是學習者計劃、監控和標準化他們的學習過程，學生閱讀過程中，為了追求更符合自身程度或不同領域的書籍，可融合自我調整理論，以幫助學生對於書籍的選擇與規劃。
本研究結合「聊天機器人」與「推薦機制」搭配「自我調整」理論及「學習同伴」開發「閱讀學習同伴」系統，讓學生與閱讀學習同伴進行互動，其功能有查閱、借閱書籍、訂定閱讀清單、推薦書籍、搜尋自我和同儕閱讀歷程。本研究實驗對象為小學四、五年級共三十四位學生，實驗期間共為十二週，前六週為前後測及學生使用系統，透過使用閱讀學習同伴系統，收集學生系統各功能使用行為，第十二週進行延宕測驗，探討系統導入對於學生閱讀能力的持續性。本研究將學生依照自我調整能力分組，來瞭解自我調整能力高低差異。並用行為分析發掘不同組別學生系統上的潛在學習模式，以確切數據與資訊幫助教師掌握學生閱讀情形。
研究結果發現導入系統後，學生整體閱讀理解能力有提升趨勢且發現閱讀理解能力受到自我調整與先備閱讀能力影響；自我調整能力上整體學生維持不變，分組後發現自我調整能力高的學生更有目的進行閱讀資訊規劃與使用閱讀學伴。行為模式分析下發現均衡且有目的使用系統及接受學伴推薦書籍的學生，閱讀能力高於其他無目的且過度使用系統的學生。學生問卷回饋及教師訪談中得知，學生主要選書模式是：自己選擇、同儕借閱、老師推薦；教師整體表示透過系統更瞭解學生借閱情況，學生亦更瞭解自身與同儕借閱歷程。本研究之具體成果可供未來數位學習同伴結合書籍推薦與自我調整理論之研究者參考。

摘要(英)

Reading is the most fundamental way to gain knowledge. In the past, it was difficult for teachers to find books that matched students′ levels and helped them break out of their reading comfort zone. With digital technology, students can monitor themselves through their learning e-portfolio, and teachers can understand each student′s reading status. In addition, scholars have proposed the "learning companion" concept to prompt students to interact with companions and attract students′ attention and engagement. On the other hand, self-regulation is how learners plan, monitor, and standardize their learning. In the process of reading, students can integrate self-regulated theory to choose books that better match their level or different fields.
This study applies self-regulated theory to develop a reading companion system. The system′s functions include a chatbot for checking out and borrowing books, making reading lists, recommending books, and searching for self and peer-reading e-portfolio. The experiment was conducted for twelve weeks; the first six weeks were pre and post-tests. A delay test was conducted in the twelfth week to explore the system sustainability on students′ reading ability. Behavioral sequential analysis was executed to discover potential learning patterns in the system and accurate information to help teachers grasp students′ reading situations.
The study results indicate that the student′s reading comprehension ability was improved, and the reading comprehension ability was affected by self-regulated and prior reading ability. Moreover, the self-regulated ability of the students remained unchanged, and the students with higher self-regulated ability were found to be more purposeful in planning reading information and using reading companions. The behavioral pattern analysis revealed that students who used the system in a balanced and purposeful manner had higher reading ability than those who used the system purposelessly and excessively. The students′ questionnaire feedback and teacher interviews revealed that the students′ main book selection patterns were: self-selection, peer borrowing, and teacher recommendation. The teachers reported that they understood more about students reading history. The results of this study can be used as a valuable reference for future researchers of digital learning companions who combine book recommendations with self-regulated theory.

關鍵字(中)

★ 自我調整
★ 推薦系統
★ 數位學習同伴
★ 數位學習歷程
★ 閱讀

關鍵字(英)

★ Self-regulation
★ recommendation system
★ digital learning companion
★ learning e-portfolio
★ reading

論文目次

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 ix
表目錄 xi
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 2
1-4 名詞解釋 3
1-5 研究貢獻程度 3
二、文獻探討 5
2-1 閱讀 5
2-1-1 閱讀的定義 5
2-1-2 趣創者理論 6
2-1-3 閱讀之相關研究 7
2-2 聊天機器人相關文獻 8
2-2-1 聊天機器人的定義 8
2-2-2 聊天機器人的發展趨勢 10
2-2-3 聊天機器人的方法、種類、工具 11
2-2-4 聊天機器人在教育上的應用 13
2-3 推薦應用於教育 14
2-3-1 相關推薦機制 15
2-3-2 推薦用在教育之實例 17
2-4 自我調整學習與學習動機 18
2-4-1 自我調整學習 18
2-4-2 學習動機 20
三、研究方法 21
3-1 研究設計 21
3-2 研究對象 22
3-3 研究工具 22
3-3-1 國小閱讀標準化測驗 22
3-3-2 書籍等級深度分類 23
3-3-3 自我調整、動機、反思問卷 23
3-3-4 閱讀興趣問卷 24
3-3-5 系統使用情形問卷設計 25
3-3-6 系統使用行為定義編碼 25
3-4 實驗流程 26
3-5 資料收集與分析 28
3-5-1 信度分析 29
3-5-2 魏克生符號等級檢定 29
3-5-3 滯後序列分析 30
3-5-4 敘述性統計 30
3-5-5 獨立樣本T檢定 30
3-5-6 成對樣本T檢定 30
3-5-7 共變數分析 30
四、系統設計與實作 31
4-1 系統簡介 31
4-2 系統環境架構 31
4-3 系統功能介紹 32
4-3-1 系統設計概念 32
4-3-2 閱讀學伴 33
4-3-3 深廣書單、願望書單 35
4-3-4 搜尋書籍 37
4-3-5 書籍摘要檢視 37
4-3-6 班級深廣書單 38
4-4 閱讀歷程呈現 39
4-4-1 同學借閱書籍 40
4-4-2 同學借閱狀況 40
4-4-3 閱讀圖譜 41
4-5 教師端工具 42
4-5-1 歷史借閱紀錄 43
4-5-2 推薦學生書籍 44
4-5-3 深廣、願望書單 45
4-5-4 學生圖譜 47
4-5-5 學生深廣閱讀 47
五、研究結果 49
5-1 使用自我調整理論與閱讀學伴系統對學生自我調整能力及成效影響之探究 49
5-1-1 閱讀自我調整能力之影響 49
5-1-2 閱讀理解能力之影響 51
5-2 閱讀自我調整高低對學生系統使用及閱讀理解影響之探究 52
5-2-1 閱讀自我調整高低對閱讀理解之影響 52
5-2-2 閱讀自我調整高低對使用系統之閱讀資訊面向差異 54
5-2-3 閱讀自我調整高低對使用系統之學伴使用面向差異 55
5-2-4 系統相關、閱讀理解、閱讀自我調整相關聯 56
5-3 系統功能使用階層式分群分析 57
5-3-1 不同使用系統行為對閱讀理解之影響 59
5-3-2 系統功能行為序列流程及行為編碼 60
5-3-3 不同系統功能使用之行為序列分析 61
5-3-4 不同系統功能組別之行為序列轉換圖比較 61
5-4 學生閱讀情形問卷分析 66
5-4-1 閱讀態度分析 66
5-4-2 閱讀興趣分析 68
5-4-3 學生主要選擇書籍相關 70
5-5 研究後教師系統使用調查結果 70
5-5-1 教師認為學生使用成效評估 71
5-5-2 教師系統使用狀況 72
5-5-3 推薦學生書籍之功能探討 74
5-5-4 系統功能呈現與實用程度探討 74
5-5-5 學生端功能設計實用程度探討 75
5-5-6 推薦機制的導入看法 75
5-5-7 對系統建議及看法 76
六、討論 77
6-1 前後測及延宕測試探討 77
6-2 開放學習者模型與自我調整理論 79
6-3 閱讀學習同伴對學生之影響 80
6-3-1 閱讀學習同伴對學生自我調整之影響 80
6-3-2 閱讀學習同伴對閱讀理解能力之影響 81
6-4 自我調整階段與系統功能設計相關係 82
6-5 學生書籍借閱實際影響之探討 83
6-5-1 同儕影響學生實際書籍借閱 84
6-5-2 教師影響學生實際書籍借閱 85
七、結論 87
7-1 研究結論 87
7-1-1 使用閱讀學習同伴系統之後，整體學生閱讀理解能力有提升趨勢 87
7-1-2 使用閱讀學習同伴系統之後，整體學生自我調整能力維持不變 87
7-1-3 學生閱讀理解能力受到自我調整能力與先備閱讀能力影響 87
7-1-4 自我調整能力高的學生更有目的進行閱讀資訊規劃與使用閱讀學伴 88
7-1-5 使用頻率為均衡使用組之學生在閱讀理解能力上有較好表現 88
7-1-6 接受閱讀學伴推薦及目的性規劃系統使用模式之組別閱讀理解能力較高 89
7-1-7 學生使用系統時主要採用：自己選擇、同儕彼此借閱、老師推薦選書模式 89
7-1-8 教師透過系統可更瞭解學生借閱情況，學生亦更瞭解自身與同儕借閱歷程 90
7-2 研究限制 91
7-3 未來展望 91
參考文獻 93
中文文獻 93
英文文獻 93
附錄一、行為與社會科學研究倫理委員審查 102
附錄二、學童、家長、教師受測同意書 103
附錄三、動機、反思、自我調整問卷 111
附件四、閱讀興趣問卷 115
附錄五、系統使用問卷 116
附錄六、教師訪談問卷 118
附錄七、教師訪談紀錄表 121
附錄八、現場實際使用情況 123
附錄九、閱讀學伴組行為轉移調整後殘差表 124
附錄十、閱讀圖譜組行為轉移調整後殘差表 125
附錄十一、均衡使用組行為轉移調整後殘差表 126
附錄十二、頻繁使用組行為轉移調整後殘差表 127

參考文獻

中文文獻
蘇柏勳(2012)。對話代理人中問句補全及問答句對之自動產生〔未出版之碩士論文〕。
國立成功大學資訊工程學系。
魏彰村(2017)。運用爬蟲技術之主題導向即時通訊聊天機器人設計與實現:以籃球領域諮詢結合LINE APP實作為例〔未出版之碩士論文〕。國立中正大學通訊資訊數位學習。
楊雅嵐(2018)。對話式服務應用範圍與價值之探索性研究-以臺灣 Chatbot 業者為例〔未出版之碩士論文〕。國立中央大學資訊管理學系。
廖健智(2019)。國小社會科聊天機器人之學習成效研究〔未出版之碩士論文〕。南華大學資訊管理學系。
徐銘駿(2020)。具推薦書籍功能之閱讀島系統架構設計〔未出版之碩士論文〕。國立中央大學網路學習科技研究所。
鄭博晏、洪暉鈞(2021年3月25-26日)。結合自我調整功能之書籍閱讀數位同伴系統之設計初探。第十六屆台灣數位學習發展研討會TWELF 2021，宜蘭市，台灣。
鄭博晏、洪暉鈞、陳德懷(2022年5月28日-6月1日)。自我調整數位閱讀同伴系統建置與閱讀行為模式分析。第二十六屆全球華人計算機教育應用大會(GCCCE 2022)，新竹市，台灣。
謝志偉(2003)。自我調節學習理論之探究 [A Theortical Exploration of Self-Regulated Learning]。課程與教學，6(3)，147-168+181。https://doi.org/10.6384/ciq.200307.0147賴森堂、黃彥綸(2018年1月31日)。LINE通訊軟體結合 Chatbot 改善設備連線測試效率與品質。電腦稽核期刊，(37)，25-36。
英文文獻
Adam, N. L., Zulkafli, M. A., Soh, S. C., & Kamal, N. A. M. (2017, 16-17 Nov. 2017). Preliminary study on educational recommender system. 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e) https://doi.org/10.1109/IC3e.2017.8409245
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749. https://doi.org/10.1109/TKDE.2005.99
Akçayır, M., Akçayır, G., Pektaş, H. M., & Ocak, M. A. (2016). Augmented reality in science laboratories: The effects of augmented reality on university students’ laboratory skills and attitudes toward science laboratories. Computers in Human Behavior, 57, 334-342. https://doi.org/10.1016/j.chb.2015.12.054
Akçıl, U., & Arap, İ. (2009). The opinions of education faculty students on learning processes involving e-portfolios. Procedia-Social and Behavioral Sciences, 1(1), 395-400. https://doi.org/10.1016/j.sbspro.2009.01.071
Asarta, C. J., & Schmidt, J. R. (2013). Access Patterns of Online Materials in a Blended Course. Decision Sciences Journal of Innovative Education, 11(1), 107-123. https://doi.org/10.1111/j.1540-4609.2012.00366.x
Askov, E. N., & Fischbach, T. J. (1973). An investigation of primary pupils’ attitudes toward reading. The Journal of Experimental Education, 41(3), 1-7. https://doi.org/10.1080/00220973.1973.11011401
Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. Cambridge university press. https://doi.org/10.1017/CBO9780511527685
Bannert, M., Sonnenberg, C., Mengelkamp, C., & Pieger, E. (2015). Short- and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance. Computers in Human Behavior, 52, 293-306. https://doi.org/10.1016/j.chb.2015.05.038
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
Barragáns-Martínez, A. B., Costa-Montenegro, E., Burguillo, J. C., Rey-López, M., Mikic-Fonte, F. A., & Peleteiro, A. (2010). A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Information Sciences, 180(22), 4290-4311. https://doi.org/10.1016/j.ins.2010.07.024
Beck, A. M., & Katcher, A. H. (1996). Between pets and people: The importance of animal companionship. Purdue University Press.
Benitez, A. B., Zhong, D., Chang, S.-F., & Smith, J. R. (2001). MPEG-7 MDS content description tools and applications. International Conference on Computer Analysis of Images and Patterns (pp. 41-52). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44692-3_6
Bii, P. (2013). Chatbot technology: A possible means of unlocking student potential to learn how to learn. Educational Research, 4(2), 218-221. https://www.researchgate.net/publication/291813780
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
Boekaerts, M. (2001). Context sensitivity: Activated motivational beliefs, current concerns and emotional arousal. In Motivation in learning contexts: Theoretical advances and methodological implications. (pp. 17-32). Pergamon Press. https://www.researchgate.net/publication/232597700
Borchers, J. O. (2008). A pattern approach to interaction design. In Cognition, Communication and Interaction (pp. 114-131). Springer. https://doi.org/10.1007/978-1-84628-927-9_7
Bull, S. (2004). Supporting learning with open learner models. Planning, 29(14), 1. https://doi.org/10.1007/978-3-642-14363-2_15
Bull, S., & Kay, J. (2007). Student models that invite the learner in: The SMILI:() Open learner modelling framework. International Journal of Artificial Intelligence in Education, 17(2), 89-120. https://www.researchgate.net/publication/262317857
Burke, R. (2000). Knowledge-based recommender systems. Encyclopedia of library and information systems, 69(Supplement 32), 175-186. https://www.researchgate.net/publication/2418883
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4), 331-370. https://doi.org/10.1023/A:1021240730564
Burke, R. (2007). Hybrid web recommender systems. The adaptive web, 377-408. https://doi.org/10.1007/978-3-540-72079-9_12
Cai, S., Chiang, F.-K., Sun, Y., Lin, C., & Lee, J. J. (2017). Applications of augmented reality-based natural interactive learning in magnetic field instruction. Interactive Learning Environments, 25(6), 778-791. https://doi.org/10.1080/10494820.2016.1181094
Cerdán, R., Vidal-Abarca, E., Martínez, T., Gilabert, R., & Gil, L. (2009). Impact of question-answering tasks on search processes and reading comprehension. Learning and Instruction, 19(1), 13-27. https://doi.org/10.1016/j.learninstruc.2007.12.003
Chan, T.-W., & Baskin, A. B. (1990). Learning companion systems. Intelligent tutoring systems: At the crossroads of artificial intelligence and education, 1, 6-33.
Chan, T.-W., Looi, C.-K., Chang, B., Chen, W., Wong, L.-H., Wong, S. L., Yu, F.-Y., Mason, J., Liu, C.-C., & Shih, J.-L. (2019). IDC theory: creation and the creation loop. Research and Practice in Technology Enhanced Learning, 14(1), 1-29. https://doi.org/10.1186/s41039-019-0120-5
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(4), 435-461. https://doi.org/10.1007/s40692-018-0122-0
Chang, C.-C., & Tseng, K.-H. (2011). Using a web-based portfolio assessment system to elevate project-based learning performances. Interactive Learning Environments, 19(3), 211-230. https://doi.org/10.1080/03075079.2018.1510388
Chen, C.-H., & Law, V. (2016). Scaffolding individual and collaborative game-based learning in learning performance and intrinsic motivation. Computers in Human Behavior, 55, 1201-1212. https://doi.org/10.1016/j.chb.2015.03.010
Chen, S. Y. (2008). Who is the avid adolescent reader in Taiwan? The role of gender, family, and teacher. Journal of Adolescent & Adult Literacy, 52(3), 214-223. https://doi.org/10.1598/JAAL.52.3.4
Chien, T.-C., Chen, Z.-H., Ko, H.-W., Ku, Y.-M., & Chan, T.-W. (2015). My-Bookstore: using information technology to support children′s classroom reading and book recommendation. Journal of Educational Computing Research, 52(4), 455-474. https://doi.org/10.1177/0735633115571920
Cohen, P., West, S. G., & Aiken, L. S. (2014). Applied multiple regression/correlation analysis for the behavioral sciences. Psychology press. https://doi.org/10.4324/9780203774441
Corno, L., & Rohrkemper, M. (1985). The intrinsic motivation to learn in classrooms. Research on motivation in education, 2, 53-90. https://ro.uow.edu.au/jseem/vol2/iss1/6/
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
De Campos, L. M., Fernández-Luna, J. M., Huete, J. F., & Rueda-Morales, M. A. (2010). Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks. International journal of approximate reasoning, 51(7), 785-799. https://doi.org/10.1016/j.ijar.2010.04.001
De Pessemier, T., Dooms, S., & Martens, L. (2014). Context-aware recommendations through context and activity recognition in a mobile environment. Multimedia Tools and Applications, 72(3), 2925-2948. https://doi.org/10.1007/s11042-013-1582-x
Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic rewards and intrinsic motivation in education: Reconsidered once again. Review of educational research, 71(1), 1-27. https://doi.org/10.1006/ceps.1999.1020
Delen, E., Liew, J., & Willson, V. (2014). Effects of interactivity and instructional scaffolding on learning: Self-regulation in online video-based environments. Computers & Education, 78, 312-320. https://doi.org/10.1016/j.compedu.2014.06.018
Deschênes, M. (2020). Recommender systems to support learners’ Agency in a Learning Context: a systematic review. International Journal of Educational Technology in Higher Education, 17(1), 1-23. https://doi.org/10.1186/s41239-020-00219-w
Dillon, A., & Jobst, J. (2005). Multimedia learning with hypermedia. The Cambridge handbook of multimedia learning, 569-588. https://doi.org/10.1017/CBO9780511816819
Ekstrand, M. D., Riedl, J. T., & Konstan, J. A. (2011). Collaborative filtering recommender systems. Foundations and Trends® in Human–Computer Interaction, 4(2), 81-173. https://doi.org/10.1561/1100000009
Franchi, S., & Guzeldere, G. (1995). Constructions of the Mind: Artificial Intelligence and the Humanities. Stanford Humanities Review. https://books.google.com.tw/books?id=HI-nHAAACAAJ
George, D., & Mallery, P. (2019). IBM SPSS statistics 26 step by step: A simple guide and reference. Routledge.
Goda, Y., Yamada, M., Matsukawa, H., Hata, K., & Yasunami, S. (2014). Conversation with a chatbot before an online EFL group discussion and the effects on critical thinking. The Journal of Information and Systems in Education, 13(1), 1-7. https://doi.org/10.12937/ejsise.13.1
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70. https://doi.org/10.1145/138859.138867
Goodman, K. (1998). 談閱讀 (洪月女譯). 台北市: 心理. 原作 1996 年出版.
Greene, J. A., & Azevedo, R. (2007). Adolescents′ Use of Self-Regulatory Processes and Their Relation to Qualitative Mental Model Shifts While Using Hypermedia. Journal of Educational Computing Research, 36(2), 125-148. https://doi.org/10.2190/G7M1-2734-3JRR-8033
Guthrie, J. T., Hoa, L. W., Wigfield, A., Tonks, S. M., & Perencevich, K. C. (2005). From spark to fire: Can situational reading interest lead to long‐term reading motivation? Literacy Research and Instruction, 45(2), 91-117. https://doi.org/10.1080/19388070609558444
Guthrie, J. T., & Wigfield, A. (1997). Reading engagement: Motivating readers through integrated instruction. ERIC.
Hadwin, A. F., Oshige, M., Gress, C. L., & Winne, P. H. (2010). Innovative ways for using gStudy to orchestrate and research social aspects of self-regulated learning. Computers in Human Behavior, 26(5), 794-805. https://doi.org/10.1016/j.chb.2007.06.007
Han, X., Tian, X., Cheng, H. N. H., Chang, W. C., Liao, C. C. Y., & Liu, S. (2017, 27-29 June 2017). Identifying Students’ Test-Taking Behavioral Patterns in an Online Chinese Reading Assessment System. 2017 International Symposium on Educational Technology (ISET) (pp. 158-162). IEEE. https://doi.org/10.1109/ISET.2017.44
Harlen, W., & Deakin Crick, R. (2003). Testing and motivation for learning. Assessment in Education: principles, policy & practice, 10(2), 169-207. https://doi.org/10.1080/0969594032000121270
Heller, B., Proctor, M., Mah, D., Jewell, L., & Cheung, B. (2005). Freudbot: An Investigation of Chatbot Technology in Distance Education EdMedia + Innovate Learning 2005, Montreal, Canada. https://www.learntechlib.org/p/20691
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5-53. https://doi.org/10.1145/963770.963772
Ho, E., Bin, J., & Chang, J. (2012). Survey of middle school student learning: Saving the generation of unmotivated. Retrieved February, 3, 2018.
Händel, M., Wimmer, B., & Ziegler, A. (2020). E-portfolio use and its effects on exam performance–a field study. Studies in Higher Education, 45(2), 258-270. https://doi.org/10.1080/03075079.2018.1510388
Hou, H.-T., Sung, Y.-T., & Chang, K.-E. (2009). Exploring the behavioral patterns of an online knowledge-sharing discussion activity among teachers with problem-solving strategy. Teaching and Teacher Education, 25(1), 101-108. https://doi.org/10.1016/j.tate.2008.07.006
Hsieh, M.-Y., Chou, W.-K., & Li, K.-C. (2017). Building a mobile movie recommendation service by user rating and APP usage with linked data on Hadoop. Multimedia Tools and Applications, 76(3), 3383-3401. https://doi.org/10.1007/s11042-016-3833-0
Huang, H.-C., Tsai, Y.-H., & Huang, S.-H. (2015). The Relevant Factors in Promoting Reading Activities in Elementary Schools. International Journal of Evaluation and Research in Education, 4(2), 62-70. https://doi.org/10.11591/ijere.v4i2.4493
Hui, L., Jun, S., Zhang, S., & Yun, H. (2017, 22-25 Aug. 2017). Implementation of intelligent recommendation system for learning resources. 2017 12th International Conference on Computer Science and Education (ICCSE) (pp. 139-144). IEEE. https://doi.org/10.1201/9781003027799
Hunt Jr, L. C. (1967). Evaluation through teacher-pupil conferences. The evaluation of children’s reading achievement, 111-125.
Hwang, G.-J., Wang, S.-Y., & Lai, C.-L. (2021). Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics. Computers & Education, 160, 104031. https://doi.org/10.1016/j.compedu.2020.104031
Jackson, P. (1986). Introduction to expert systems.
Ji Won, Y. (2015). Examining the Effect of Academic Procrastination on Achievement Using LMS Data in e-Learning. Journal of Educational Technology & Society, 18(3), 64-74. http://www.jstor.org/stable/jeductechsoci.18.3.64
Järvelä, S. (2001). Shifting research on motivation and cognition to an integrated approach on learning and motivation in context. Motivation in learning contexts: Theoretical advances and methodological implications, 3-14. https://www.researchgate.net/publication/288029174
Karakaya, M. Ö., & Aytekin, T. (2018). Effective methods for increasing aggregate diversity in recommender systems. knowledge and Information Systems, 56(2), 355-372. https://doi.org/10.1007/s10115-017-1135-0
Khribi, M. K., Jemni, M., & Nasraoui, O. (2012). Automatic personalization in e-learning based on recommendation systems: An overview. Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers, 19-33. https://doi.org/10.4018/978-1-60960-842-2.ch002
Kirkegaard, C., Gulz, A., & Silvervarg, A. (2014). Introducing a challenging teachable agent. International Conference on Learning and Collaboration Technologies (pp. 53-62). Springer, Cham. https://doi.org/10.1007/978-3-319-07482-5_6
Kong, S.-C. (2016). A framework of curriculum design for computational thinking development in K-12 education. Journal of Computers in Education, 3(4), 377-394. https://doi.org/10.1007/s40692-016-0076-z
Kong, S.-C., Chiu, M. M., & Lai, M. (2018). A study of primary school students′ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178-189. https://doi.org/10.1016/j.compedu.2018.08.026
Konstan, J. A., & Riedl, J. (2012). Deconstructing recommender systems. IEEE Spectrum, 10, 1-7.
Krashen, S. D. (2004). The power of reading: Insights from the research: Insights from the research. ABC-CLIO.
Krulwich, B. (1997). Lifestyle finder: Intelligent user profiling using large-scale demographic data. AI magazine, 18(2), 37-37. https://doi.org/10.1609/aimag.v18i2.1292
Lalwani, T., Bhalotia, S., Pal, A., Rathod, V., & Bisen, S. (2018). Implementation of a Chatbot System using AI and NLP. International Journal of Innovative Research in Computer Science & Technology (IJIRCST) Volume-6, Issue-3. https://doi.org/10.2139/ssrn.3531782
Lan, Y.-F., Tsai, P.-W., Yang, S.-H., & Hung, C.-L. (2012). Comparing the social knowledge construction behavioral patterns of problem-based online asynchronous discussion in e/m-learning environments. Computers & Education, 59(4), 1122-1135. https://doi.org/10.1016/j.compedu.2012.05.004
Li, J., & Ye, Z. (2020). Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm. Complexity, 2020, 6619249. https://doi.org/10.1155/2020/6619249
Lilien, G. L., Kotler, P., & Moorthy, K. S. (1992). Marketing models (Vol. 803). Prentice-Hall Englewood Cliffs, NJ.
Lundberg, I., & Sterner, G. (2006). Reading, arithmetic, and task orientation—How are they related? Annals of dyslexia, 56(2), 361-377. https://doi.org/10.1007/s11881-006-0016-0
Müller, N. M., & Seufert, T. (2018). Effects of self-regulation prompts in hypermedia learning on learning performance and self-efficacy. Learning and Instruction, 58, 1-11. https://doi.org/10.1016/j.learninstruc.2018.04.011
Mansor, A. N., Rasul, M. S., Rauf, R. A. A., & Koh, B. L. (2013). Developing and Sustaining Reading Habit Among Teenagers. The Asia-Pacific Education Researcher, 22(4), 357-365. https://doi.org/10.1007/s40299-012-0017-1
McCracken, R. A., & McCracken, M. J. (1978). Modeling is the key to sustained silent reading. The Reading Teacher, 31(4), 406-408. https://www.jstor.org/stable/20194551
Meryem, G., Douzi, K., & Chantit, S. (2016, 19-20 Oct. 2016). Toward an E-orientation platform: Using hybrid recommendation systems. 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA) (pp. 1-6). IEEE. https://doi.org/10.1109/SITA.2016.7772305
Mullis, I. V., Martin, M. O., Foy, P., & Drucker, K. T. (2012). PIRLS 2011 international results in reading. ERIC.
Otebolaku, A. M., & Andrade, M. T. (2014). Context-Aware Media Recommendations. 2014 28th International Conference on Advanced Information Networking and Applications Workshops (pp. 191-196). IEEE. https://doi.org/10.1109/WAINA.2014.40
Panadero, E., Jonsson, A., & Botella, J. (2017). Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. Educational Research Review, 22, 74-98. https://doi.org/10.1016/j.edurev.2017.08.004
Pilgreen, J. L. (2000). The SSR handbook: How to organize and manage a sustained silent reading program. Boynton/Cook Publishers Portsmouth, NH.
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of educational psychology, 82(1), 33. https://doi.org/10.1037/0022-0663.82.1.33
Pintrich, P. R., & Zeidner, M. (2000). Handbook of self-regulation. Elsevier Science & Technology. https://doi.org/10.1016/B978-0-12-109890-2.X5027-6
Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people. Cambridge, UK, 10, 236605. https://www.researchgate.net/publication/37705092
Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58. https://doi.org/10.1145/245108.245121
Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: introduction and challenges. In Recommender systems handbook (pp. 1-34). Springer. https://doi.org/10.1007/978-1-4899-7637-6_1
Rich, E. (1979). User modeling via stereotypes. Cognitive science, 3(4), 329-354. https://doi.org/10.1016/S0364-0213(79)80012-9
Rivera, A. C., Tapia-Leon, M., & Lujan-Mora, S. (2018). Recommendation systems in education: A systematic mapping study. International Conference on Information Technology & Systems (pp. 937-947). Springer, Cham. https://doi.org/10.1007/978-3-319-73450-7_89
Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Shuster, K., & Smith, E. M. (2020). Recipes for building an open-domain chatbot. arXiv preprint arXiv:2004.13637.
Sackett, G. P. (1978). Observing behavior: I. Theory and applications in mental retardation.
Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender systems in e-commerce. Proceedings of the 1st ACM conference on Electronic commerce (pp. 158-166).
Schmitz, B., & Wiese, B. S. (2006). New perspectives for the evaluation of training sessions in self-regulated learning: Time-series analyses of diary data. Contemporary educational psychology, 31(1), 64-96. https://doi.org/10.1016/j.cedpsych.2005.02.002
Schwab, I., Kobsa, A., & Koychev, I. (2001). Learning user interests through positive examples using content analysis and collaborative filtering. Internal Memo, GMD, St. Augustin, Germany. https://www.researchgate.net/publication/2465963
Schwonke, R. (2015). Metacognitive load–Useful, or extraneous concept? Metacognitive and self-regulatory demands in computer-based learning. Journal of Educational Technology & Society, 18(4), 172-184. https://doi.org/http://www.jstor.org/stable/jeductechsoci.18.4.172
Siemens, G., Gašević, D., & Dawson, S. (2015). Preparing for the digital university: A review of the history and current state of distance, blended, and online learning. https://doi.org/10.13140/RG.2.1.3515.8483
Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151, 103862. https://doi.org/10.1016/j.compedu.2020.103862
Snow, R. E. (1989). Aptitude-treatment interaction as a framework for research on individual differences in learning. In Learning and individual differences: Advances in theory and research. (pp. 13-59). W H Freeman/Times Books/ Henry Holt & Co.
Stanovich, K. E. (2009). Matthew effects in reading: Some consequences of individual differences in the acquisition of literacy. Journal of education, 189(1-2), 23-55. https://doi.org/10.1177/0022057409189001-204
Sun, Z., Xie, K., & Anderman, L. H. (2018). The role of self-regulated learning in students′ success in flipped undergraduate math courses. The internet and higher education, 36, 41-53. https://doi.org/10.1016/j.iheduc.2017.09.003
Thorat, S. A., & Jadhav, V. (2020). A review on implementation issues of rule-based chatbot systems. Proceedings of the International Conference on Innovative Computing & Communications (ICICC). https://doi.org/10.2139/ssrn.3567047
Timms, M. J. (2016). Letting Artificial Intelligence in Education Out of the Box: Educational Cobots and Smart Classrooms. International Journal of Artificial Intelligence in Education, 26(2), 701-712. https://doi.org/10.1007/s40593-016-0095-y
Tohidi, H., & Jabbari, M. M. (2012). The effects of motivation in education. Procedia-Social and Behavioral Sciences, 31, 820-824. https://doi.org/10.1016/j.sbspro.2011.12.148
Tsai, C. C. (2004). Conceptions of learning science among high school students in Taiwan: A phenomenographic analysis. International Journal of Science Education, 26(14), 1733-1750. https://doi.org/10.1080/0950069042000230776
Turing, A. M. (2009). Computing Machinery and Intelligence. In R. Epstein, G. Roberts, & G. Beber (Eds.), Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer (pp. 23-65). Springer Netherlands. https://doi.org/10.1007/978-1-4020-6710-5_3
Urdaneta-Ponte, M. C., Mendez-Zorrilla, A., & Oleagordia-Ruiz, I. (2021). Recommendation Systems for Education: Systematic Review. Electronics, 10(14), 1611. https://doi.org/10.3390/electronics10141611
Van den Boom, G., Paas, F., & Van Merrienboer, J. J. (2007). Effects of elicited reflections combined with tutor or peer feedback on self-regulated learning and learning outcomes. Learning and Instruction, 17(5), 532-548. https://doi.org/10.1016/j.cedpsych.2005.02.002
Veenman, M. V. J. (2011). Alternative assessment of strategy use with self-report instruments: a discussion. Metacognition and Learning, 6(2), 205-211. https://doi.org/10.1007/s11409-011-9080-x
Venkatraman, S. (2017). A proposed business intelligent framework for recommender systems. Informatics (Vol. 4 No. 4, p. 40). MDPI.
Volet, S. (2001). Understanding learning and motivation in context: A multi-dimensional and multi-level cognitive-situative perspective. In Motivation in learning contexts: Theoretical advances and methodological implications. (pp. 57-82). Pergamon Press. http://researchrepository.murdoch.edu.au/id/eprint/8138
Wallace, R. S. (2009). The Anatomy of A.L.I.C.E. In R. Epstein, G. Roberts, & G. Beber (Eds.), Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer (pp. 181-210). Springer Netherlands. https://doi.org/10.1007/978-1-4020-6710-5_13
Wang, H., Lu, Z., Li, H., & Chen, E. (2013). A dataset for research on short-text conversations. Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 935-945).
Wang, J.-H., Hsu, S.-H., Chen, S. Y., Ko, H.-W., Ku, Y.-M., & Chan, T.-W. (2014). Effects of a mixed-mode peer response on student response behavior and writing performance. Journal of Educational Computing Research, 51(2), 233-256. https://doi.org/10.2190/EC.51.2.e
Wang, X., Rosenblum, D., & Wang, Y. (2012). Context-aware mobile music recommendation for daily activities. Proceedings of the 20th ACM international conference on Multimedia (pp. 99-108). https://doi.org/10.1145/2393347.2393368
Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45. https://doi.org/10.1145/365153.365168
Wilensky, R. (1987). The Berkeley UNIX consultant project. In Wissensbasierte Systeme (pp. 286-296). Springer. https://doi.org/10.1007/978-3-642-88719-2_25
Wilson, H. J., Daugherty, P., & Bianzino, N. (2017). The jobs that artificial intelligence will create. MIT Sloan Management Review, 58(4), 14. https://sloanreview.mit.edu/article/will-ai-create-as-many-jobs-as-it-eliminates/
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019). Supporting Self-Regulated Learning in Online Learning Environments and MOOCs: A Systematic Review. International Journal of Human–Computer Interaction, 35(4-5), 356-373. https://doi.org/10.1080/10447318.2018.1543084
Wong, L.-H., Chan, T.-W., Chen, W., Looi, C.-K., Chen, Z.-H., Liao, C. C. Y., King, R. B., & Wong, S. L. (2020). IDC theory: interest and the interest loop. Research and Practice in Technology Enhanced Learning, 15(1), 3. https://doi.org/10.1186/s41039-020-0123-2
Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-373594-2.X0001-9
Xiao, J., Wang, M., Jiang, B., & Li, J. (2018). A personalized recommendation system with combinational algorithm for online learning. Journal of Ambient Intelligence and Humanized Computing, 9(3), 667-677. https://doi.org/10.1007/s12652-017-0466-8
Yin, C., Shi, L., Sun, R., & Wang, J. (2020). Improved collaborative filtering recommendation algorithm based on differential privacy protection. The Journal of Supercomputing, 76(7), 5161-5174. https://doi.org/10.1007/s11227-019-02751-7
Zhang, H., Huang, T., Lv, Z., Liu, S., & Zhou, Z. (2018). MCRS: A course recommendation system for MOOCs. Multimedia Tools and Applications, 77(6), 7051-7069. https://doi.org/10.1007/s11042-017-4620-2
Zhou, X., Dong, D., Wu, H., Zhao, S., Yu, D., Tian, H., Liu, X., & Yan, R. (2016). Multi-view response selection for human-computer conversation. Proceedings of the 2016 conference on empirical methods in natural language processing (pp. 372-381).
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13-39). Elsevier. https://doi.org/10.1016/B978-012109890-2/50031-7
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into practice, 41(2), 64-70. https://doi.org/10.1207/s15430421tip4102_2
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American educational research journal, 45(1), 166-183. https://doi.org/10.3102/0002831207312909
Zimmerman, B. J., Bonner, S., & Kovach, R. (1996). Developing self-regulated learners: Beyond achievement to self-efficacy. American Psychological Association. https://doi.org/10.1037/10213-000
Zimmerman, B. J., & Campillo, M. (2003). Motivating self-regulated problem solvers. The psychology of problem solving, 233262. https://doi.org/10.1017/CBO9780511615771.009
Zimmerman, B. J., & Martinez-Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex, and giftedness to self-efficacy and strategy use. Journal of educational psychology, 82(1), 51. https://doi.org/10.1037/0022-0663.82.1.51

指導教授

洪暉鈞(Hui-Chun Hung)

審核日期

2022-7-26

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