博碩士論文 109552010 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:18 、訪客IP:3.236.146.28
姓名 林吳憲(WU-SIAN LIN)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 應用關聯規則與機器學習探索閱讀行為與學生學習成效關係
(Applying association rules and machine learning to explore the relationship between reading behavior and student academic performance)
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 (2027-7-19以後開放)
摘要(中) 隨著網路與資訊科技的持續發展,線上數位學習越來越盛行,學生學習的管道不再侷限於面對面的實體課程。同時,2020受到COVID-19新冠肺炎疫情的影響,使得人們學習需求與學習的行為有了非常大幅度的改變。因疫情的影響使人們需保持社交距離的政策,讓世界各國對線上數位學習的重視程度有明顯的提升。常見的線上數位學習平台有Coursera、Moocs、edX,及本論文研究的BookRoll線上電子書學習平台。

而BookRoll線上電子書學習平台是由日本京都大學所開發的一套電子書閱讀系統,不僅支援行動學習,也提供了書籤(Bookmark)、標記(Marker)、備忘錄(Memo)等相關功能等,其系統能夠記錄學生的所有閱讀行為,藉此蒐集學生完整的學習日誌(Log)。同時能搭配複習系統、概念評量系統、程式評量系統來評估學生的學習狀況。

本論文研究將使用學生於BookRoll線上電子書學習平台上的閱讀行為紀錄、複習、評量系統上的答題紀錄作為數據基礎。透過資料前處理提取特徵出來,並先使用統計方法,來分析學生閱讀行為與學習成效之間的關聯性。接著利用關聯分析來探索閱讀行為與學生學習成效的關係,最後,使用機器學習分類演算法對學生的學習成效進行預測分類,並比較不同的分類演算法的預測成效。
摘要(英) With the continuous development of the Internet and information technology, online digital learning has become more and more popular, and the way students learn is no longer limited to physical courses. Besides, society was affected by the COVID-19 pandemic in 2020, which made people′s learning needs and learning behaviors have changed significantly. Due to the pandemic, social distancing is a new policy that has apparently increased the importance of digital learning around the world. Common online digital learning platforms include Coursera, Moocs, edX, and the BookRoll, the online e-book learning platform studied in this paper.

The BookRoll online e-book learning platform is a reading system developed by Kyoto University in Japan. It not only supports active learning but also provides Bookmark, Marker, Memo and other functions. The system collects students’ complete learning logs by recording all the reading behaviors of students. Moreover, it can be used with a review system, concept evaluation system, and program evaluation system to evaluate students′ learning situations.

Students′ reading behavior records, review records, and answer records on the evaluation system on the BookRoll online e-book learning platform will be applied as the data basis in this research. Features are extracted and statistical methods are used to analyze the correlation between students′ reading behavior and learning outcomes. Next, analyzing the relationship between reading behavior and students′ learning outcomes by the association rule. Lastly, using machine learning classification algorithms to predict and classify students′ learning outcomes and compare the predicted performances of different classification algorithms.
關鍵字(中) ★ 線上數位學習
★ 電子書
★ 資料探勘
★ 關聯規則
★ 機器學習
★ 決策樹
★ 隨機森林
★ 極限梯度提升
★ 邏輯斯迴歸
★ 支援向量機
★ 單純貝氏
★ k鄰近演算法
關鍵字(英) ★ Online digital learning
★ e-book
★ Data Mining
★ Association Rule
★ Machine Learning
★ Decision Tree
★ Random Forest
★ XGboost
★ Logistic Regression
★ Support Vector Machine
★ Naive Bayes
★ k-Nearest Neighbors
論文目次 摘要 i
ABSTRACT ii
致謝 iv
目錄 v
圖目錄 vii
表目錄 viii
一、 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 3
二、 文獻探討 4
2.1 電子書系統與閱讀行為 5
2.2 線上學習行為相關研究 5
2.3 資料探勘 7
2.3.1 資料探勘的定義 7
2.3.2 資料探勘的功能 7
2.3.3 資料探勘的流程 8
2.4 探索性因素分析 10
2.4.1 主成份分析 10
2.5 關聯規則 11
2.6 機器學習分類演算法 13
2.6.1 決策樹(Decision Tree) 15
2.6.2 隨機森林(Random Forest) 15
2.6.3 極限梯度提升(XGboost) 16
2.6.4 邏輯斯迴歸(Logistic Regression) 16
2.6.5 支援向量機(Support Vector Machine) - SVC 17
2.6.6 支援向量機(Support Vector Machine) - Linear SVC 17
2.6.7 單純貝氏(Naive Bayes , NB) 17
2.6.8 K鄰近演算法(K-Nearest Neighbors , KNN) 17
2.7 機器學習模型評估 18
2.7.1 機器學習模型的評估方法 18
2.7.2 機器學習模型的評估指標 19
三、 研究內容與方法 21
3.1 系統架構 21
3.2 研究平台 22
3.3 課程描述 22
3.4 資料集描述 23
3.4.1 BookRoll Log各欄位定義 25
3.5 資料前處理 26
3.5.1 電子書閱讀動作編碼 26
3.5.2 關聯規則分析(Association Rule)特徵離散化 28
3.5.3 機器學習(Machine Learning)成績離散分群 34
四、 研究結果與討論 35
4.1 實驗環境 35
4.2 探索性因素分析 35
4.3 探討學生閱讀行為與學習成效之關聯(使用T檢定) 37
4.4 探討學生於複習/評量系統與學習成效之關聯(使用T檢定) 40
4.5 探討閱讀行為特徵與學習成效之關聯(Spearman) 42
4.6 探討複習/評量系統特徵與學習成效之關聯(Spearman) 44
4.7 關聯分析結果(基於學生學期總成績) 45
4.7.1 關聯分析結果進行T檢定 49
4.8 機器學習分類演算法預測成效比較 54
五、 結論與未來研究 60
參考文獻 61
參考文獻 Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD international conference on Management of data,
Ahuja, R., Chug, A., Gupta, S., Ahuja, P., & Kohli, S. (2020). Classification and clustering algorithms of machine learning with their applications. In Nature-inspired computation in data mining and machine learning (pp. 225-248). Springer.
Al-Shehri, H., Al-Qarni, A., Al-Saati, L., Batoaq, A., Badukhen, H., Alrashed, S., Alhiyafi, J., & Olatunji, S. O. (2017). Student performance prediction using support vector machine and k-nearest neighbor. 2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE),
Arroyo, I., Murray, T., Woolf, B. P., & Beal, C. (2004). Inferring unobservable learning variables from students’ help seeking behavior. International Conference on Intelligent Tutoring Systems,
Basha, S. A. H. (2018). Study of Education Patterns in Rural and Urban India using Association Rule Mining: Implementation. In: Volume.
Baturay, M. H. (2015). An overview of the world of MOOCs. Procedia-Social and Behavioral Sciences, 174, 427-433.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Chatfield, C. (2000). Time-series forecasting. Chapman and Hall/CRC.
Chen, C.-H., & Su, C.-Y. (2019). Using the BookRoll e-book system to promote self-regulated learning, self-efficacy and academic achievement for university students. Journal of Educational Technology & Society, 22(4), 33-46.
Chen, M.-S., Han, J., & Yu, P. S. (1996). Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and data Engineering, 8(6), 866-883.
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining,
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., & Chen, K. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4), 1-4.
Christie, M., & Ferdos, F. (2004). The mutual impact of educational and information technologies: Building a pedagogy of e-learning. Journal of Information Technology Impact, 4(1), 15-26.
Cobb, S. C. (2009). Social presence and online learning: A current view from a research perspective. Journal of Interactive Online Learning, 8(3).
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Damez, M., Dang, T. H., Marsala, C., & Bouchon-Meunier, B. (2005). Fuzzy decision tree for user modeling from human-computer interactions. Proceedings of the 5th International Conference on Human System Learning, ICHSL,
Dass, R. (2009). Using Association Rule Mining for Behavioral Analysis of School Students: A Case from India. 2009 42nd Hawaii International Conference on System Sciences,
Derouin, R. E., Fritzsche, B. A., & Salas, E. (2005). E-learning in organizations. Journal of management, 31(6), 920-940.
El-Halees, A. M. (2009). Mining students data to analyze e-Learning behavior: A Case Study.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37-37.
Flanagan, B., Chen, W., & Ogata, H. (2018). Joint activity on learner performance prediction using the BookRoll dataset. International Conference on Computers in Education (ICCE2018),
Flanagan, B., & Ogata, H. (2017). Integration of learning analytics research and production systems while protecting privacy. The 25th International Conference on Computers in Education, Christchurch, New Zealand,
Freund, R. J., Wilson, W. J., & Sa, P. (2006). Regression analysis. Elsevier.
Goethals, B. (2003). Survey on frequent pattern mining. Univ. of Helsinki, 19, 840-852.
Harrell, I. L., & Bower, B. L. (2011). Student characteristics that predict persistence in community college online courses. American Journal of Distance Education, 25(3), 178-191.
Hawkins, D. M. (2004). The problem of overfitting. Journal of chemical information and computer sciences, 44(1), 1-12.
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.
Howard, E., Meehan, M., & Parnell, A. (2018). Contrasting prediction methods for early warning systems at undergraduate level. The Internet and Higher Education, 37, 66-75.
Hrastinski, S. (2009). A theory of online learning as online participation. Computers & Education, 52(1), 78-82.
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.
Hwang, G.-J., Chu, H.-C., & Yin, C. (2017). Objectives, methodologies and research issues of learning analytics. In (Vol. 25, pp. 143-146): Taylor & Francis.
Ifinedo, P., Pyke, J., & Anwar, A. (2018). Business undergraduates’ perceived use outcomes of Moodle in a blended learning environment: The roles of usability factors and external support. Telematics and Informatics, 35(1), 93-102.
John, G. H., & Langley, P. (2013). Estimating continuous distributions in Bayesian classifiers. arXiv preprint arXiv:1302.4964.
Jou, M., Tennyson, R. D., Wang, J., & Huang, S.-Y. (2016). A study on the usability of E-books and APP in engineering courses: A case study on mechanical drawing. Computers & Education, 92, 181-193.
Kabakchieva, D. (2012). Student performance prediction by using data mining classification algorithms. International journal of computer science and management research, 1(4), 686-690.
Ketui, N., Wisomka, W., & Homjun, K. (2019). Association Rule Mining with Permutation for Estimating Students Performance and Its Smart Education System. Journal of Computers, 30(2), 93-102.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai,
Lai, K., & Cerpa, N. (2001). Support vs. confidence in association rule algorithms. Proceedings of the OPTIMA Conference, Curicó,
Lemay, D. J., & Doleck, T. (2020). Grade prediction of weekly assignments in MOOCS: mining video-viewing behavior. Education and Information Technologies, 25(2), 1333-1342.
Liu, Q., Wu, L., & Lu, J. (2008). Research of Study Early-Warning Application Based on Association Mining. 2008 International Symposium on Knowledge Acquisition and Modeling,
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.
Luque, A., Carrasco, A., Martín, A., & de Las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216-231.
McNicholas, P. D., Murphy, T. B., & O’Regan, M. (2008). Standardising the lift of an association rule. Computational Statistics & Data Analysis, 52(10), 4712-4721.
Menzies, T., & Hu, Y. (2003). Data mining for very busy people. Computer, 36(11), 22-29.
Mohammed, M., Khan, M. B., & Bashier, E. B. M. (2016). Machine learning: algorithms and applications. Crc Press.
Oladipupo, O., & Oyelade, O. (2010). Knowledge discovery from students’ result repository: association rule mining approach. International Journal of Computer Science and Security, 4(2), 199-207.
Onrubia, J., & Engel, A. (2009). Strategies for collaborative writing and phases of knowledge construction in CSCL environments. Computers & Education, 53(4), 1256-1265.
Parack, S., Zahid, Z., & Merchant, F. (2012). Application of data mining in educational databases for predicting academic trends and patterns. 2012 IEEE international conference on technology enhanced education (ICTEE),
Pardos, Z. A., Heffernan, N. T., Anderson, B., & Heffernan, C. L. (2007). The effect of model granularity on student performance prediction using Bayesian networks. International Conference on User Modeling,
Polikar, R. (2012). Ensemble learning. In Ensemble machine learning (pp. 1-34). Springer.
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.
Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-validation. Encyclopedia of database systems, 5, 532-538.
Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., & Ventura, S. (2013). Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, 21(1), 135-146.
Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618.
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 1-21.
Schapire, R. E., & Singer, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine learning, 37(3), 297-336.
Su, A. Y., Yang, S. J., Hwang, W.-Y., & Zhang, J. (2010). A Web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments. Computers & Education, 55(2), 752-766.
Swan, K. (2003). Learning effectiveness online: What the research tells us. Elements of quality online education, practice and direction, 4(1), 13-47.
Thai-Nghe, N., Busche, A., & Schmidt-Thieme, L. (2009). Improving academic performance prediction by dealing with class imbalance. 2009 Ninth international conference on intelligent systems design and applications,
Wang, D., & Xu, J. (2020). Principal component analysis in the local differential privacy model. Theoretical computer science, 809, 296-312.
Wang, J., & Zhang, Y. (2019). Clustering study of student groups based on analysis of online learning behavior. Proceedings of the 2019 International Conference on Modern Educational Technology,
Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9), 2839-2846.
Xu, X., Wang, J., Peng, H., & Wu, R. (2019). Prediction of academic performance associated with internet usage behaviors using machine learning algorithms. Computers in Human Behavior, 98, 166-173.
Yadav, S., & Shukla, S. (2016). Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. 2016 IEEE 6th International conference on advanced computing (IACC),
Yang, C., & Hsieh, T. C. (2013). Regional differences of online learning behavior patterns. The Electronic Library.
Zakaria, S. A., Muhamad, W. Z. A. W., & Azziz, N. H. A. (2018). Analyzing undergraduate students’ performance in engineering statistics course using educational data mining: Case study in UniMAP. AIP Conference Proceedings,
Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and data Engineering, 12(3), 372-390.
Zhang, M.-L., & Zhou, Z.-H. (2007). ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7), 2038-2048.
Zhu, L., Li, Y., & Li, X. (2009). Research on Early-Warning Model of Students′ Academic Records Based on Association Rules. 2009 WRI World Congress on Computer Science and Information Engineering,
指導教授 楊鎮華(Stephen J.H. Yang) 審核日期 2022-8-13
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