博碩士論文 103522111 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:10 、訪客IP:3.238.174.50
姓名 黃俊堂(Jyun-Tang Huang)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 透過適時介入輔導提升磨課師課程之完課率
(Increasing MOOCs completion rate with timely intervention)
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 ( 永不開放)
摘要(中) 近年來,大規模開放式線上課程(Massive Open Online Course, MOOCs)在教育領域已逐漸成為主流趨勢,MOOCs在台灣稱為磨課師。在磨課師課程中低完課率為極需要突破的瓶頸問題,因此,有效提升完課率的機制持續受到研究學者高度關注。
相關文獻指出針對高風險學生進行介入輔導可以提升續讀率及完課率。然而,在介入輔導前需提早並精準預測高風險學生,方能針對高風險學生進行適時介入輔導。因此,本篇論文實作預警系統,提早並精準預測高風險學生,提供課程團隊預警清單。
本論文之預警系統主要根據學生進入課程時間來定義學習週次,並收集當週所有學習活動的相關資訊,達到提早並精準預測下週高風險學生的目的。實驗結果顯示,使用邏輯迴歸分析所建立下週高風險學生預測模型,其精準度達77%。
摘要(英) In recent years, Massive Open Online Courses(MOOCs)have gradually become a mainstream. However, in MOOCs, the issue of low completion rates is a big problem, developing effective mechanisms has been regarded as an important research.
According to the research, it indicated that timely intervention for at-risk students could increase retention rates and completion rates. Nevertheless, predicting for at-risk students has to be precise and in advance of interventions. In this paper, we implemented a warning system and provided a list of at-risk students for the course teams.
The predictor model uses each learners’ first learning as the first day of the week. Finally, the result shows that the precision rate of the predictive model is up to 77%
關鍵字(中) ★ 磨課師
★ 完課率
★ 介入輔導
★ 教育資料探勘
關鍵字(英)
論文目次 摘要 i
ABSTRACT ii
圖目錄 iv
表格目錄 v
一、 緒論 1
二、 文獻探討 3
2.1 磨課師(MOOCs) 3
2.2 學習干預(Intervention) 3
三、 系統設計 5
3.1 開發環境 6
3.2 系統架構 8
3.3 資料收集 9
3.4 資料儲存 11
3.5 資料萃取與分析 12
3.5.1 缺席的定義 12
3.5.2 資料處理(資料清理及特徵擷取) 12
3.5.3 訓練資料集 17
3.5.4 演算法介紹 18
3.5.5 評估模型 20
3.6 資訊應用 22
3.6.1 資料的統計分析功能 22
3.6.2 學習活動統計分析功能 24
3.6.3 影片瀏覽的分析功能 26
3.6.4 缺席預警功能 27
四、 結果及討論 29
五、 結論及未來方向 32
參考文獻 33
參考文獻 Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38.
Akçapınar, G. (2015). How automated feedback through text mining changes plagiaristic behavior in online assignments. Computers & Education, 87, 123-130.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Ho, T. K. (1998). The random subspace method for constructing decision forests. Pattern Analysis and Machine Intelligence, IEEE Transactions on,20(8), 832-844.
Chorianopoulos, K., Giannakos, M. N., Chrisochoides, N., & Reed, S. (2014, July). Open Service for Video Learning Analytics. In Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on (pp. 28-30). IEEE.
Clow, D. (2013, April). MOOCs and the funnel of participation. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 185-189). ACM.
Creed-Dikeogu, G., & Clark, C. (2013). Are you MOOC-ing yet? A review for academic libraries. Kansas Library Association College and University Libraries Section Proceedings, 3(1), 9-13.
De Boer, J., Kommers, P. A., & De Brock, B. (2011). Using learning styles and viewing styles in streaming video. Computers & Education, 56(3), 727-735.
Halawa, S., Greene, D., & Mitchell, J. (2014). Dropout prediction in MOOCs using learner activity features. Experiences and best practices in and around MOOCs, 7.
Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45-58.
Hu, Y. H., Lo, C. L., & Shih, S. P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior,36, 469-478.
Jordan, K. (2014). Initial trends in enrolment and completion of massive open online courses. The International Review of Research in Open and Distributed Learning, 15(1).
Khalil, H., & Ebner, M. (2014, February). MOOCs completion rates and possible methods to improve retention-A literature review. In World Conference on Educational Multimedia, Hypermedia and Telecommunications (No. 1, pp. 1305-1313).
Kleftodimos, A., & Evangelidis, G. (2014, July). Exploring student viewing behaviors in online educational videos. In Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on (pp. 367-369). IEEE.
Kloft, M., Stiehler, F., Zheng, Z., & Pinkwart, N. (2014, October). Predicting MOOC dropout over weeks using machine learning methods. In Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs (pp. 60-65).
Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., & Miller, R. C. (2014, March). Understanding in-video dropouts and interaction peaks inonline lecture videos. In Proceedings of the first ACM conference on Learning@ scale conference (pp. 31-40). ACM.
Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90-97.
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53(3), 950-965.
Mason, L., Junyent, A. A., & Tornatora, M. C. (2014). Epistemic evaluation and comprehension of web-source information on controversial science-related topics: Effects of a short-term instructional intervention. Computers & Education, 76, 143-157.
McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2010). The MOOC model for digital practice.
Shi, C., Fu, S., Chen, Q., & Qu, H. (2014, October). VisMOOC: Visualizing video clickstream data from massive open online courses. In Visual Analytics Science and Technology (VAST), 2014 IEEE Conference on (pp. 277-278). IEEE.
Ullrich, C., Shen, R., & Xie, W. (2013, July). Analyzing student viewing patterns in lecture videos. In Advanced Learning Technologies (ICALT), 2013 IEEE 13th International Conference on (pp. 115-117). IEEE.
Sinha, T., Jermann, P., Li, N., & Dillenbourg, P. (2014). Your click decides your fate: Inferring information processing and attrition behavior from mooc video clickstream interactions. arXiv preprint arXiv:1407.7131.
Voss, B. D. (2013). Massive open online courses (MOOCs): A primer for university and college board members. AGB Association of Governing Boards of Universities and Colleges.
Yuan, L., Powell, S., & CETIS, J. (2013). MOOCs and open education: Implications for higher education.
Zheng, S., Rosson, M. B., Shih, P. C., & Carroll, J. M. (2015, February). Understanding student motivation, behaviors and perceptions in MOOCs. InProceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 1882-1895). ACM.
指導教授 楊鎮華 審核日期 2016-7-13
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