博碩士論文 108552013 詳細資訊




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:11 、訪客IP:3.236.65.63
姓名 李蘊庭(Yun-Ting Lee)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 使用詞向量透過無監督學習分群的學習序列聚類方法
(A Sequence clustering by using word vector for learning logs)
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 (2027-1-26以後開放)
摘要(中) 隨著2019 年Covid-19 新冠肺炎疫情爆發,居家隔離或者是遠距工作已然
成為了不少人的日常,傳統的教學方式也因此次疫情受到了衝擊,此時大規模開放線上課程(MOOC)更彰顯出遠距教學與數位學習的重要。近年來人工智慧相關技術的發展以及各式各樣新穎的數據分析方法的誕生,推薦系統以及成效預測已經成為了一個重要的研究方向。大規模開放式線上課程 (Massive Open Online Courses,MOOCs)是現今不斷擴展的數位學習方式,將課程透過網路發送給學習者學習,這種線上學習的行為自主性強且不受時間和地點限制,對於有富有學習動機的人來說是絕佳的學習資源。
本研究使用由日本京都大學開發的BookRoll 線上電子書學習系統搭配國
立中央大學所開發的複習與答題系統,根據學生們使用教材學習的行為紀錄
(Log)經過文本轉換後透過無監督學習方式進行學習策略的歸納,並探討學習行為與學習成效之間的關聯性。
本文探討學生的學習行為足跡以此來了解學生對學習成效較佳的活動或行
為,提供參考進而改善學生們的學習成效。我們透過Bookroll 平台與各複習系統上所收集的學習歷程進行處理,生成學生們的學習動作序列並歸納出學習策略。本研究希望能從中找出學習策略與學習成效的關聯性,提供老師輔導學生的參考。我們使用中央大學109 學年度下學期的Python 程式設計課程在混合式教學場景中的學習歷程與成果來分析學生的學習策略,發現學生在Bookroll 平台上所留下的學習活動資料確實可以透過無監督學習的方式使用分群演算法來萃取其學習策略。
研究發現,學生們使用BookRoll 及其他練習系統的足跡使用基於神經網
路的文本表示方法後透過無監督學習所歸納出的所有學習策略皆與學習成效都達到顯著正相關,且使用基於神經網路的文本表示法運算速度非常快速,未來可應用於學習預警或推薦機制實現精準的教學干預。
摘要(英) With the outbreak of the Covid-19 in 2019, home quarantine or remote work has become a daily life for many people.
Traditional teaching methods have also been affected by the epidemic.
At this time, Massive Open Online Courses (MOOCs) have highlights the importance of distance teaching and digital learning.
With the development of artificial intelligence-related technologies and the birth of various novel data analysis methods, recommendation systems and performance prediction have become an important research
direction.
Massive Open Online Courses (MOOCs) are digital learning methods that have been expanding in recent years. Courses are sent to learners through the Internet.
This kind of online learning is highly autonomous and independent of time and location, and this is an excellent learning resource for those who are motivated to learn.
It′s a very good learning resource for people with learning
motivation.
This research uses the BookRoll online e-book learning system developed by Kyoto University in Japan and the review and answer system developed by National Center University to analyze learning strategies based on the action logs of students using the Bookroll, and study the effects between learning behavior and learning
effectiveness.
This study explores the learning behavior logs of students to understand the activities or behaviors of students with better learning effectiveness, and provides teachers as a reference for counseling, thereby improving students′ learning effectiveness.
We process the learning process data collected on the Bookroll platform and each practice system including Cloze, Short-Ans, OJ, Viscode and Assessment, and analyze the learning logs of students, summarize the learning strategies of students.
This research hopes to find out the correlation between learning actions and learning effectiveness, and provide teachers with references for tutoring students.
We use the learning logs of the Python programming course in the second semester of the 109 academic year of National Central University in the mixed teaching scenario to analyze the learning strategy using learning logs left by the students on the Bookroll can
be summarized by the clustering algorithm Its learning strategy.
The study found that all learning strategies summarized by students using BookRoll and other practice systems logs can using unsupervised learning clustering are significantly related to learning effectiveness, and can be used in learning early warning or recommendation mechanisms to achieve precise teaching interventions in the future.
關鍵字(中) ★ 行為序列
★ 無監督學習
★ 序列聚類
★ 詞嵌入
★ 文本表示
★ MOOCs
關鍵字(英) ★ Activity sequence
★ Unsupervised Learning
★ Sequence Clustering
★ Word Embedding
★ Word Represent
★ MOOCs
論文目次 目 錄
摘 要 I
ABSTRACT III
誌 謝 VI
目 錄 VII
一、研究動機 1
二、研究目的 2
三、研究架構 5
四、研究方法 5
數據來源 6
動作特徵提取 7
序列化 9
序列分群 11
最佳分群數量 13
分群演算法介紹 15
分割式分群法:k-means 15
機率模型分群法:高斯混合模型(Gaussian Mixture Model, GMM) 16
聚合式階層演算法(Agglomerative hierarchical clustering) 16
序列的表示法 17
基於字串的文本表示方法 17
基於神經網路的文本表示方法 18
Word2vec 18
Doc2vec 19
評估指標 21
五、研究結果 22
FEATURE-BASED 分類類別 23
LOG-BASED 分類類別 28
六、結論與討論 34
限制LIMITATION 43
參考文獻 REFERENCE 44
參考文獻 [1]. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016). Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265-283).
[2]. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111-3119).
[3]. Le, Q., & Mikolov, T. (2014, June). Distributed representations of sentences and documents. In International conference on machine learning (pp. 1188-1196). PMLR.
[4]. Niwattanakul, S., Singthongchai, J., Naenudorn, E., & Wanapu, S. (2013, March). Using of Jaccard coefficient for keywords similarity. In Proceedings of the international multiconference of engineers and computer scientists (Vol. 1, No. 6, pp. 380-384).
[5]. Halkidi, M., & Vazirgiannis, M. (2001, November). Clustering validity assessment: Finding the optimal partitioning of a data set. In Proceedings 2001 IEEE international conference on data mining (pp.
187-194). IEEE. [6]. Halkidi, M., & Vazirgiannis, M. (2001, November). Clustering validity assessment: Finding the optimal partitioning of a data set. In Proceedings 2001 IEEE international conference on data mining (pp.187-194). IEEE.
[7]. Kang, C. H., Huang, Y. Q., Lu, H. T., Jong, B. S., & Yang, J. H. (2020, November). Using Sequence Clustering to Unveil Students’ Learning Strategies and Explore the Relationship with Cognitive Load. In In The 28th International Conference on Computers in Education
(ICCE), Online.
[8]. Reynolds, D. A. (2009). Gaussian mixture models. Encyclopedia of biometrics, 741(659-663).
[9]. Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.
[10]. Day, W. H., & Edelsbrunner, H. (1984). Efficient algorithms for agglomerative hierarchical clustering methods. Journal of classification, 1(1), 7-24.
[11]. Faber, V. (1994). Clustering and the continuous k-means algorithm. Los Alamos Science, 22(138144.21), 67.
指導教授 楊鎮華(Stephen J.H. Yang) 審核日期 2022-1-19
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明