探索學生學習策略並透過即時干預措施提升學生參與度與學習成效

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：3.147.13.117

姓名

黃博進(Bo-Jin Huang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

探索學生學習策略並透過即時干預措施提升學生參與度與學習成效
(Exploring Learning Strategies with Timely Intervention to Improve Students’ Engagement and Learning Outcome)

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 (2026-7-12以後開放)

摘要(中)

學習策略是學生和老師為了有效的實現學習目標而進行的學習活動。學生參與度是指學生在學習中的參與程度或感興趣程度，及他們與班級、機構、彼此之間的連結程度。學習策略與學生參與度都是影響學習成效的關鍵因素。在傳統上常透過學生填寫問卷來調查其學習策略與參與度，但這種方式較為耗費人力、時間且無法即時得知結果，其調查結果也容易局限在預定義的策略類別之中，除此之外，學生填寫問卷在回憶自己的學習行為時也容易感到不確定，降低問卷結果的考靠性。因此本研究期望透過學生於線上學習環境的學習軌跡，即時萃取出學生學習策略，並分析這些學習策略和參與度與學習成效的相關性，如此便能根據學生使用策略的情形來替代衡量學生參與度。我們還希望根據實驗結果，實際對學生進行干預輔導，從而改善學生的學習體驗，提升其學習成效與參與度。
本研究直接分析學生於線上學習環境所產生的學習軌跡，透過序列分群的方式從中萃取出六種學習策略。我們依序討論了六種學習策略中的序列動作以及行為意涵，並透過相關係數分析找出影響學生學習成效和參與度的關鍵學習策略。接著根據相關係數分析結果與學生使用各種策略的次數，設計學習行為干預活動於實驗組班級進行。最後我們比較實驗組與控制組在干預前後的學習策略使用情形、學習成效以及參與度的差異，來討論干預活動對學生的影響。
研究結果顯示，萃取出的六個學習策略與學習成效皆有不同程度的正相關性，而學生參與度的部分只有行為參與度與學習策略有正相關性。除此之外，實驗組班級的學生在經過干預活動過後，其學習策略、學習成效以及參與度的表現都顯著優於控制組班級，因此我們認為干預活動確實能改善學生的學習行為，進而提升學生的學習成效以及參與度。

摘要(英)

Learning strategies are learning activities carried out by students and teachers in order to effectively achieve their learning goals. Student engagement refers to the degree of participation or interest of students in learning, and the degree of connection between them and the class, institution, and each other. Learning strategies and student engagement are both key factors that affect learning effectiveness. Traditionally, the teacher asks students to fill out a questionnaire to investigate their learning strategies and engagement. However, it′s consuming much effort and time, and the results not timely enough. The results of the investigation are also easily limited to the predefined strategy categories. In addition, when filling out the questionnaire, students are also prone to feel uncertain when recalling their own learning behaviors, which reduces the reliability of the questionnaire results. Therefore, this study expects to use the learning logs of students in the online learning environment to extract student learning strategies in real time, and analyze the correlation of these learning strategies to student′s engagement and learning outcome, so that it can proxy measure students′ engagement based on the situation of students using strategies. We also hope that based on the results of the experiment, we can actually intervene in students′ learning behavior to improve their learning outcome and engagement.
This study analyzes the sequences generated by students′ learning logs in the online learning environment, and extracts six learning strategies through sequence clustering. We discussed the characteristics of these strategies, and identified the key learning strategies that affect students′ learning outcome and engagement through correlation coefficient analysis. Then, according to the results of the correlation coefficient analysis and the number of students using various strategies, we design learning behavior intervention and conduct them in the experimental group. Finally, we compare the differences in the use of learning strategies, learning outcome, and engagement between the experimental group and the control group before and after the intervention to discuss the impact of the intervention on students.
The results show that the six extracted learning strategies have varying degrees of positive correlation with learning outcome, and the behavioral engagement is the only one positively correlated with learning strategies among the three sections of engagement. In addition, after the intervention, the students in the experimental group have significantly better performance in learning strategies, learning outcome, and engagement than the control group. Therefore, we believe that the intervention can indeed improve the learning behavior of the students, thereby increasing the student’s learning outcome and engagement.

關鍵字(中)

★ 學習策略
★ 學生參與度
★ 干預
★ 學習成效
★ 序列分群

關鍵字(英)

★ Learning Strategy
★ Student Engagement
★ Intervention
★ Learning Outcome
★ Sequence Clustering

論文目次

摘要 i
ABSTRACT ii
目錄 iv
圖目錄 vi
表目錄 vii
一、緒論 1
1.1. 學習策略(Learning Strategy) 1
1.2. 學生參與度(Student Engagement) 1
二、文獻探討 3
2.1. 學習策略分析(Learning Strategy Analysis) 3
2.2. 學生參與度衡量(Measurement of Student Engagement) 4
2.3. 干預(Intervention) 5
三、研究方法與實驗設計 7
3.1. 研究對象 7
3.2. 實驗設計 7
3.3. 資料集 8
3.4. 參與度問卷 8
3.5. 序列生成 8
3.5.1. 分類事件類別 9
3.5.2. 合併相同章節的序列 9
3.5.3. 合併相同且連續發生的事件 11
3.5.4. 去除離群值 11
3.6. 序列分群 12
3.6.1. 聚合式階層分群法 12
3.6.1.1. 聚合式階層分群法之步驟 13
3.6.2. 評估結果 15
3.6.3. 序列分群結果 16
3.7. 相關性分析 17
3.8. 干預策略(Intervention strategies) 17
四、結果及討論 21
4.1. RQ1: 如何從Learning logs中找出學生的學習策略？ 21
4.1.1. 學習策略1: 課後複習(Review after class) 22
4.1.2. 學習策略2: 概念複習(Concept review) 23
4.1.3. 學習策略3: 課堂上學習(Learning in class) 24
4.1.4. 學習策略4: 課前預習(Preview before class) 25
4.1.5. 學習策略5: 考古題練習(Quiz practice) 27
4.1.6. 學習策略6: 程式撰寫練習(Coding exercise) 28
4.2. RQ2: 使用不同學習策略的學生其參與度是否有不同？ 29
4.3. RQ3: 使用不同學習策略是否會影響學生的學習成效？ 31
4.4. RQ4: 是否能透過即時提供學生學習策略干預，進而提升學生的參與度與學習成效？ 33
4.4.1. 實驗組班級內比較 33
4.4.1.1. 干預前後學生學習策略與學習成效比較 33
4.4.1.2. 干預前後學生分群比較 35
4.4.1.3. 干預前後學生參與度比較 36
4.4.2. 實驗組與控制組比較 36
4.4.2.1. 干預前實驗組與控制組學習策略比較 37
4.4.2.2. 干預前後實驗組與控制組學習成效比較 38
4.4.2.3. 干預前後實驗組與控制組參與度比較 38
五、結論 40
六、限制與未來研究 41
七、參考文獻 43
附錄 47
參與度問卷 47

參考文獻

Akçapinar, G., Chen, M.-R. A., Majumdar, R., Flanagan, B., & Ogata, H. (2020). Exploring student approaches to learning through sequence analysis of reading logs. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge,
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., & Benjamins, R. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115.
Axelson, R. D., & Flick, A. (2010). Defining student engagement. Change: The magazine of higher learning, 43(1), 38-43.
Balch, W. R. (1998). Practice versus review exams and final exam performance. Teaching of Psychology, 25(3), 181-185.
Beer, C., Clark, K., & Jones, D. (2010). Indicators of engagement. Proceedings ascilite Sydney, 2010, 75-85.
Blasiman, R. N., Dunlosky, J., & Rawson, K. A. (2017). The what, how much, and when of study strategies: comparing intended versus actual study behaviour. Memory, 25(6), 784-792.
Brink, A. G. (2013). The impact of pre-and post-lecture quizzes on performance in intermediate accounting II. Issues in Accounting Education, 28(3), 461-485.
Choi, S. P., Lam, S. S., Li, K. C., & Wong, B. T. (2018). Learning analytics at low cost: At-risk student prediction with clicker data and systematic proactive interventions. Journal of Educational Technology & Society, 21(2), 273-290.
Darr, C. W. (2012). Measuring student engagement: The development of a scale for formative use. In Handbook of research on student engagement (pp. 707-723). Springer.
Dixson, M. D. (2015). Measuring student engagement in the online course: The Online Student Engagement scale (OSE). Online Learning, 19(4), n4.
Douglas, I., & Alemanne, N. D. (2007). Measuring student participation and effort. International Conference on Cognition and Exploratory Learning in Digital Age, Algarve, Portugal,
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4-58.
Edwards, S. H., & Murali, K. P. (2017). CodeWorkout: short programming exercises with built-in data collection. Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education,
Elmaadaway, M. A. N. J. B. J. o. E. T. (2018). The effects of a flipped classroom approach on class engagement and skill performance in a blackboard course. British Journal of Educational Technology, 49(3), 479-491.
Espinoza, P., & Genna, G. M. (2018). Hi, I want to talk to you about your progress: A large course intervention for at-risk college students. Journal of College Student Retention: Research, Theory & Practice, 1521025118790054.
Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In Handbook of research on student engagement (pp. 763-782). Springer.
Fuchs, D., Mock, D., Morgan, P. L., & Young, C. L. (2003). Responsiveness‐to‐intervention: Definitions, evidence, and implications for the learning disabilities construct. Learning Disabilities Research & Practice, 18(3), 157-171.
Gabadinho, A., Ritschard, G., Studer, M., & Müller, N. S. (2009). Mining sequence data in R with the TraMineR package: A user’s guide. Geneva: Department of Econometrics and Laboratory of Demography, University of Geneva.
Jovanović, J., Gašević, D., Dawson, S., Pardo, A., Mirriahi, N. J. T. I., & Education, H. (2017). Learning analytics to unveil learning strategies in a flipped classroom. The Internet and Higher Education, 33(4), 74-85.
Kahu, E. R. (2013). Framing student engagement in higher education. Studies in higher education, 38(5), 758-773.
Kahu, E. R., & Nelson, K. (2018). Student engagement in the educational interface: understanding the mechanisms of student success. Higher education research & development, 37(1), 58-71.
Kang, C. H., Huang, Y. Q., Lu, H. T., Jong, B. S., & Yang, J. H. (2020). Using Sequence Clustering to Unveil Students’ Learning Strategies and Explore the Relationship with Cognitive Load. In The 28th International Conference on Computers in Education (ICCE), Online.
Karpicke, J. D., & Roediger III, H. L. (2007). Repeated retrieval during learning is the key to long-term retention. Journal of Memory and Language, 57(2), 151-162.
Lam, S.-f., Jimerson, S., Wong, B. P., Kikas, E., Shin, H., Veiga, F. H., Hatzichristou, C., Polychroni, F., Cefai, C., & Negovan, V. (2014). Understanding and measuring student engagement in school: The results of an international study from 12 countries. School Psychology Quarterly, 29(2), 213.
Lin, C.-H., Zhang, Y., & Zheng, B. (2017). The roles of learning strategies and motivation in online language learning: A structural equation modeling analysis. Computers & Education, 113, 75-85.
Lu, O. H., Huang, A. Y., Huang, J. C., Huang, C. S., & Yang, S. J. (2016). Early-Stage Engagement: Applying Big Data Analytics on Collaborative Learning Environment for Measuring Learners′ Engagement Rate. 2016 International Conference on Educational Innovation through Technology (EITT),
Lu, O. H., Huang, J. C., Huang, A. Y., & Yang, S. J. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220-234.
Ma, J., Han, X., Yang, J., & Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. The Internet and Higher Education, 24, 26-34.
Mackay, S., & Stockport, G. (2006). Blended learning, classroom and e-learning. The Business Review, Cambridge, 5(1), 82-88.
Murtagh, F. (1983). A survey of recent advances in hierarchical clustering algorithms. The computer journal, 26(4), 354-359.
Ogata, H., Oi, M., Mohri, K., Okubo, F., Shimada, A., Yamada, M., Wang, J., & Hirokawa, S. (2017). Learning analytics for e-book-based educational big data in higher education. In Smart sensors at the IoT frontier (pp. 327-350). Springer.
Reeve, J. (2012). A self-determination theory perspective on student engagement. In Handbook of research on student engagement (pp. 149-172). Springer.
Rovers, S. F., Stalmeijer, R. E., van Merriënboer, J. J., Savelberg, H. H., & De Bruin, A. B. (2018). How and why do students use learning strategies? A mixed methods study on learning strategies and desirable difficulties with effective strategy users. Frontiers in psychology, 9, 2501.
Sanjaya, W. (2019). Strategi pembelajaran berorientasi standar proses pendidikan.
Sasirekha, K., & Baby, P. (2013). Agglomerative hierarchical clustering algorithm-a. International Journal of Scientific and Research Publications, 83, 83.
Schellings, G. (2011). Applying learning strategy questionnaires: problems and possibilities. Metacognition and Learning, 6(2), 91-109.
Sáiz Manzanares, M. C., Marticorena Sánchez, R., García Osorio, C. I., & Díez-Pastor, J. F. (2017). How do B-Learning and learning patterns influence learning outcomes? Frontiers in psychology, 8, 745.
Stott, A., & Hobden, P. A. (2016). Effective learning: A case study of the learning strategies used by a gifted high achiever in learning science. Gifted Child Quarterly, 60(1), 63-74.
Trowler, V. (2010). Student engagement literature review. The higher education academy, 11(1), 1-15.
Tsai, T. P., Lin, J., Hou, J., Chen, Y., & Hsu, C. (2019). Preview Analytics of ePUB3 eBook-based Flipped Classes Using a Big Data Approach. Journal of Internet Technology, 20(7), 2129-2140.
Tsai, T. P., Lin, L. C., & Lin, J. (2019). A Study on the Preview Effectiveness of Learning Contents in ePUB3 eBook-Based Flip Blended Learning Models. International Journal of Mobile and Blended Learning (IJMBL), 11(2), 50-67.
Van Horne, S., Curran, M., Smith, A., VanBuren, J., Zahrieh, D., Larsen, R., & Miller, R. (2018). Facilitating student success in introductory chemistry with feedback in an online platform. Technology, Knowledge and Learning, 23(1), 21-40.
Weinstein, C. E., Husman, J., & Dierking, D. R. (2000). Self-regulation interventions with a focus on learning strategies. In Handbook of self-regulation (pp. 727-747). Elsevier.
Wong, B. T.-m., & Li, K. C. (2020). A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 7(1), 7-28.
Xiao, M., Zhou, J., & Xu, T. (2020). Research on the Influence of Synchronous Interactive Class on Student Online Learning Engagement. 2020 12th International Conference on Education Technology and Computers,
Yamada, M., Shimada, A., Okubo, F., Oi, M., Kojima, K., & Ogata, H. (2017). Learning analytics of the relationships among self-regulated learning, learning behaviors, and learning performance. Research and Practice in Technology Enhanced Learning, 12(1), 1-17.
Yang, C. C., Chen, I. Y., & Ogata, H. (2021). Toward Precision Education. Educational Technology & Society, 24(1), 152-163.
Yang, S. J. (2021). Guest Editorial: Precision Education-A New Challenge for AI in Education. Journal of Educational Technology & Society, 24(1).
Yeh, Y.-C., Kwok, O.-M., Chien, H.-Y., Sweany, N. W., Baek, E., & McIntosh, W. A. (2019). How College Students′ Achievement Goal Orientations Predict Their Expected Online Learning Outcome: The Mediation Roles of Self-Regulated Learning Strategies and Supportive Online Learning Behaviors. Online Learning, 23(4), 23-41.

指導教授

楊鎮華(Jhen-Hua Yang)

審核日期

2021-7-12

推文