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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题名:	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
作者:	鄭博晏;Zheng, Bo-Yan
贡献者:	網路學習科技研究所
关键词:	自我調整;推薦系統;數位學習同伴;數位學習歷程;閱讀;Self-regulation;recommendation system;digital learning companion;learning e-portfolio;reading
日期:	2022-07-26
上传时间:	2022-10-04 12:07:04 (UTC+8)
出版者:	國立中央大學
摘要:	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。 ;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.
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