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題名 基於序列探勘之網路科學探究學習歷程分析
Learning Process Analysis Based on Sequential Pattern Mining in a Web-based Inquiry Science Environment
作者 王文芳
貢獻者 陳志銘
王文芳
關鍵詞 探究式教學
學習歷程紀錄
學習歷程分析
序列探勘
序列分析
日期 2016
上傳時間 2-Aug-2016 17:55:30 (UTC+8)
摘要 探究式教學法常被應用於科學學習活動中，有助於學生理解科學本質與推理過程，探究式學習歷程對於掌握影響探究式學習成效因素具有其重要意義，但是教師難以完全掌握學生的探究式學習歷程。因此，若能有目的、精確且真實的紀錄學習者在科學探究學習平台上的學習行為，將能更加全面性的掌控影響探究式學習成效的原因。

本研究以CWISE為輔助學習者進行科學探究學習之平台，並於平台中發展xAPI學習歷程記錄器模組，詳細的紀錄學生之學習歷程，以即時收集學生在科學探究學習過程之學習歷程資料，除了分析影響探究學習成效的個人因素與學習歷程行為外，並搭配序列探勘方法(sequential pattern mining)及序列分析(lag sequential analysis)，探討不同學習成效、探究能力、科學態度學習者之探究學習成效、整體學習時間與探究式模擬實驗學習時間，以及探究學習歷程是否具有顯著轉移與差異。

研究結果顯示：(1)探究能力越高者其探究學習成效越佳；(2)體驗探究模擬實驗活動時間越長，其探究能力與學習成效越佳；(3) 基於序列探勘，高低不同學習成效學習者在探究學習之整體課程瀏覽順序無顯著差異，均是依照課程設計的探究式學習流程順序進行學習；(4)基於序列分析，高學習成效與高探究能力學習者在進行浮力單元模擬實驗後，會再次調整先前設立之假說，而低學習成效與低探究能力學習者，則欠缺此一關鍵的探究學習行為；(5)探究模擬實驗活動有助於提高其探究能力與學習成效。最後，本研究依據研究結果針對探究式課程的課程設計提出建議，並提出未來研究方向。
參考文獻 吳武典、林幸台、王振德、郭靜姿修訂（1999）。基本人格量表。台北縣：心理出版社。
林奇賢、黃耿鐘（2007）。數位學習歷程檔案系統在網路學習環境中的角色與意義。理工研究學報，41（2），43–64。
林敏慧、陳美樺、管怡婷、郭榮學、陳慶帆（2001年6月）。網路教學與傳統教學之差異與融合分析。2001全球華人計算機教育應用大會，國立中央大學。
林緯倫, & 連韻文。(2001)。如何能發現隱藏的規則？從科學資優生表現的特色，探索提升規則發現能力的方法。科學教育學刊9(3)，197–217。
紀秋雲（2013）。資訊科技融入教學對國小高年級學童學習成效之研究-以新北市某國小為例。未出版之碩士論文。銘傳大學教育研究所碩士在職專班，桃園市。
徐慶雲(2008)。實施探究式科學闖關遊戲提升國小學童科學學習成就之行動研究。未出版之碩士論文。國立屏東教育大學數理教育研究所論文，屏東市。
黃月怡（2011）。應用分類技術於線上學習之研究。未出版之碩士論文。國立中正大學資訊管理學系暨研究所，嘉義縣。
陳文森（2003）。非同步網路教學學習路徑的研究。未出版之碩士論文。國立高雄師範大學資訊教育研究所，高雄市。
教育部（2008）。國民中小學九年一貫課程綱要自然與生活科技學習領域。台北：教育部。
許金山（2007）。混合式數位學習歷程及成效之分析。生活科技教育月刊，39（1），66–84。
黃秋瑞（2003）。以科學史教材協助高中教師瞭解克卜勒定律概念發展之效益研究。未出版之碩士論文。國立臺灣師範大學科學教育研究所，台北市。
黃毓琪（2007）。IT及STS探究式教學對國小學童科學解釋能力之影響。未出版之碩士論文。國立屏東教育大學，屏東市。
莊嘉坤（1995）。國小學生科學態度潛在類別的分析研究。國立屏東師院學報，8，111-136。
楊建民（2010）。探究式教學法與講述式教學法在國小Scratch程式教學學習成效之研究。未出版之碩士論文。國立屏東教育大學資訊科學系，屏東市。
劉宏文（2001）。高中學生進行開放式科學探究活動之個案研究。未出版之碩士論文。國立彰化師範大學科學教育研究所，彰化市。
蘇懿生、黃台珠(1998)。對科學的態度-ㄧ個有待研究的問題。科學教育月刊，215，2-13。
Abdi, A. (2014). The Effect of Inquiry-Based Learning Method on Students’ Academic Achievement in Science Course. Universal Journal of Educational Research, 2(1), 37–41.
ADL Net. (2015, December 8). xAPI-Dashboard. Retrieved December 8, 2015, from https://github.com/adlnet/xAPI-Dashboard
ADL. (2014). Sharable Content Object Reference Model (SCORM)2004.Retrieved October 15, 2015, from http://www.eife-l.org/publications/standards/elearning-standard/scormoverview/english_release
Advanced Distributed Learning. (2015, October 15). Advanced Distributed Learning. Retrieved October 15, 2015, from http://www.adlnet.org/
Agrawal, R., & Srikant, R. (1995, March). Mining Sequential Patterns. In Proceedings of the Eleventh International Conference on Data Engineering (pp. 3-14). Washington, DC, USA: IEEE Computer Society.
Bakeman, R., Deckner, D. F., & Quera, V. (2005). Analysis of behavioral streams. In D. M. Teti (Ed.), Handbook of research methods in developmental science (pp. 394–420). Oxford, UK: Blackwell Publishers.
Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., & Koller, D. (2014). Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming. Journal of the Learning Sciences, 23(4), 561-599. doi:10.1080/10508406.2014.954750
Buffler, A., Allie, S., & Lubben, F. (2001). The development of first year physics students’ ideas about measurement in terms of point and set paradigms. International Journal of Science Education, 23(11), 1137-1156. doi:10.1080/09500690110039567
Burch, C. B. (1999). Inside the Portfolio Experience: The Student’s Perspective. English Education, 32(1), 34-49.
Cavallo, A. M. L., Potter, W. H., & Rozman, M. (2004). Gender Differences in Learning Constructs, Shifts in Learning Constructs, and Their Relationship to Course Achievement in a Structured Inquiry, Yearlong College Physics Course for Life Science Majors. School Science and Mathematics, 104(6), 288–300. doi:10.1111/j.1949-8594.2004.tb18000.x
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331. doi:10.1504/IJTEL.2012.051815
Chen, C.-M., & Chang, C.-C. (2014). Mining learning social networks for cooperative learning with appropriate learning partners in a problem-based learning environment. Interactive Learning Environments, 22(1), 97-124. doi:10.1080/10494820.2011.641677
Chen, C.-M., & Chen, M.-C. (2009). Mobile formative assessment tool based on data mining techniques for supporting web-based learning. Computers & Education, 52(1), 256-273. doi:10.1016/j.compedu.2008.08.005
Chen, C.-M., Hsieh, Y.-L., & Hsu, S.-H. (2007). Mining learner profile utilizing association rule for web-based learning diagnosis. Expert Systems with Applications, 33(1), 6-22. doi:10.1016/j.eswa.2006.04.025
Chen, C-M. & Lin, S-T.(2014) Assessing effects of information architecture of digital libraries on supporting e-learning: A case study on the Digital Library of Nature & Culture. Computers & Education, 75(1) 92-102.
Chen, C.-M., Wang, J.-Y., Chen, Y.-T., & Wu, J.-H. (2014). Forecasting reading anxiety for promoting English-language reading performance based on reading annotation behavior. Interactive Learning Environments, 1-25. doi:10.1080/10494820.2014.917107
Chen, C.-T., & She, H.-C. (2014). The Effectiveness of Scientific Inquiry with/without Integration of Scientific Reasoning. International Journal of Science and Mathematics Education, 13(1), 1–20. doi:10.1007/s10763-013-9508-7
Chiang, T.H.C, Yang, S.J.H. & Hwang, G.J. (2014) ‘Students’ online interactive patterns in augmented reality-based inquiry activities’, Computers & Education, Vol. 78, pp.97–108.
Cropley, A. J., & Page, K. (2002). Creativity in Education and Learning—A Guide for Teachers and Educators. Long Range Planning, 35, 199-200.
Duval, E. (2011). Attention Please!: Learning Analytics for Visualization and Recommendation. Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 9–17). New York, USA doi:10.1145/2090116.2090118
Faghihi, U., Fournier-Viger, P., Nkambou, R., & Poirier, P. (2009). A Generic Episodic Learning Model Implemented in a Cognitive Agent by Means of Temporal Pattern Mining. Next-Generation Applied Intelligence (pp. 545-555). Springer Berlin Heidelberg.
Fortenbacher, A., Beuster, L., Elkina, M., Kappe, L., Merceron, A., Pursian, A., … Wenzlaff, B. (2013). LeMo: A learning analytics application focussing on user path analysis and interactive visualization. 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS) (Vol. 02, pp. 748-753). doi:10.1109/IDAACS.2013.6663025
Fournier-Viger, P., Faghihi, U., Nkambou, R., & Nguifo, E. M. (2010). Exploiting Sequential Patterns Found in Users’ Solutions and Virtual Tutor Behavior to Improve Assistance in ITS. Journal of Educational Technology & Society, 13(1), 13-24.
Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.-W., & Tseng, V. S. (2014). SPMF: a Java Open-Source Pattern Mining Library. Journal of Machine Learning Research, 1-5.
Fournier-Viger, P., Nkambou, R., & Nguifo, E. M. (2008). A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems. MICAI 2008: Advances in Artificial Intelligence: 7th Mexican International (pp. 765-778). Atizapán de Zaragoza, Mexico: Springer.
Fournier-Viger, P., Nkambou, R., Nguifo, E. M., & Faghihi, U. (2009). Building Agents That Learn by Observing Other Agents Performing a Task: A Sequential Pattern Mining Approach, Opportunities and Challenges for Next-Generation Applied Intelligence (pp. 279-284). Springer Berlin Heidelberg.
Freedman, M. P. (1997). Relationship among laboratory instruction, attitude toward science, and achievement in science knowledge. Journal of Research in Science Teaching, 34(4), 343-357. doi:10.1002/(SICI)1098-2736(199704)34:4<343::AID-TEA5>3.0.CO;2-R
Gardner, P. L., (1975). Attitudes to Science: A review. Studies In Science Education,59 (2), 1-41.
Germann, P. J., Aram, R., & Burke, G. (1996). Identifying patterns and relationships among the responses of seventh-grade students to the science process skill of designing experiments. Journal of Research in Science Teaching, 33(1), 79-99. doi:10.1002/(SICI)1098-2736(199601)33:1<79::AID-TEA5>3.0.CO;2-M
Gobert, J. D., Pedro, M. S., Raziuddin, J., & Baker, R. S. (2013). From Log Files to Assessment Metrics: Measuring Students’ Science Inquiry Skills Using Educational Data Mining. Journal of the Learning Sciences, 22(4), 521-563. doi:10.1080/10508406.2013.837391
Graf, S., Liu, T.C., & Kinshuk. (2010). Analysis of learners’ navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning, 26(2), 116-131. doi: 10.1111/j.1365-2729.2009.00336.x.
Greeno, J. G. (2001). Students with competence, authority, and accountability: Affording intellective identities in classrooms. New York: The College Board.
Haladyna, T., & Shaughnessy, J. (1982), Attitude toward science: a quantitive synthesis, Sci. Edu., 66(4), 547-563.
Haury, D. L. (1993). Teaching Science through Inquiry. ERIC Clearinghouse for Science Mathematics and Environmental Education. Columbus, OH.
He, W. (2013). Examining students’ online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior, 29(1), 90-102. doi:10.1016/j.chb.2012.07.020
Hsu, Y.-S., Chang, H.-Y., Fang, S.-C., & Wu, H.-K. (2015). Developing technology-infused inquiry learning modules to promote science learning in Taiwan. In M. S. Khine (Ed.), Science education in East Asia: Pedagogical innovations and best practices(pp. 373-403). New York: Springer.
Hung, J.-L., & Zhang, K. (2008). Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching. MERLOT Journal of Online Learning and Teaching, 4(4), 426-437.
Hwang, G.-J. (2003). A conceptual map model for developing intelligent tutoring systems. Computers & Education, 40(3), 217-235. doi:10.1016/S0360-1315(02)00121-5
Hwang, G.-J., & Chang, H.-F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers & Education, 56(4), 1023-1031. doi:10.1016/j.compedu.2010.12.002
Jan, J.-C., Chen, C.-M., & Huang, P.-H. (2016). Enhancement of digital reading performance by using a novel web-based collaborative reading annotation system with two quality annotation filtering mechanisms. International Journal of Human-Computer Studies, 86, 81-93. doi:10.1016/j.ijhcs.2015.09.006
Joanna Taylor, & Jerry Bilbrey. (2012). Effectiveness of inquiry based and teacher directed instruction in an Alabama elementary school. Journal of Instructional Pedagogies, 8, 17.
Jong, B.-S., Chan, T.-Y., & Wu, Y.-L. (2007). Learning Log Explorer in E-Learning Diagnosis. Education, IEEE Transactions on Education, 50(3), 216-228. doi:10.1109/TE.2007.900023
Kamber, M., Han, J., & Pei, J. (2012). Data Mining: Concepts and Techniques (Third Edition.). Boston: Morgan Kaufmann.
Klahr, D., & Dunbar, K., (1987). Dual space search during scientific reasoning . Cognitive Science, 12, 1-48.
Kuhn, D., Garcia-Mila, M., Zohar, A., Andersen, C., White, S. H., Klahr, D., & Carver, S. M. (1995). Strategies of Knowledge Acquisition. Monographs of the Society for Research in Child Development, 60(4), i-157. doi:10.2307/1166059
Lai, C.-L., & Hwang, G.-J. (2015). An interactive peer-assessment criteria development approach to improving students’ art design performance using handheld devices. Computers & Education, 85, 149-159. doi:10.1016/j.compedu.2015.02.011
Laxhammar, R., & Falkman, G. (2014). Online Learning and Sequential Anomaly Detection in Trajectories. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(6), 1158-1173. doi:10.1109/TPAMI.2013.172
Li, B., Kuo, R., Chang, M., & Garn, K. (2015, September). Reward Points Calculation based on Sequential Pattern Analysis in an Educational Mobile App. In the Proceedings of 21st International Conference on Distributed Multimedia Systems (pp. 186-190), Vancouver, Canada.
Lin, P.-C., Hou, H.-T., Wang, S.-M., & Chang, K.-E. (2013). Analyzing knowledge dimensions and cognitive process of a project-based online discussion instructional activity using Facebook in an adult and continuing education course. Computers & Education, 60, 110–121.
Marx, R. W., Blumenfeld, P. C., Krajcik, J. S., Fishman, B., Soloway, E., Geier, R., & Tal, R. T. (2004). Inquiry-based science in the middle grades: Assessment of learning in urban systemic reform. Journal of Research in Science Teaching, 41(10), 1063-1080. doi:10.1002/tea.20039
Moran, G., Dumas, J. E., & Symons, D. K. (1992). Approaches to sequential analysis and the description of contingency in behavioral interaction. Behavioral Assessment, 14, 65-92.
Myers, M. J., & Burgess, A. B. (2003). Inquiry-based laboratory course improves students’ ability to design experiments and interpret data. Advances in Physiology Education, 27(1-4), 26-33.
National Research Council. (2000). Inquiry and the National Science Education Standards:A Guide for Teaching and Learning. Washington, DC: National Research Council.
Nkambou, R., Fournier-Viger, P., & Nguifo, E. M. (2011). Learning task models in ill-defined domain using an hybrid knowledge discovery framework. Knowledge-Based Systems, 24(1), 176-185. doi:10.1016/j.knosys.2010.08.002
OUYang, Y., & Zhu, M. (2007). eLORM: Learning Object Relationship Mining based Repository. In The 9th IEEE International Conference on E-Commerce Technology and the 4th IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services, 2007 (pp. 691-698). doi:10.1109/CEC-EEE.2007.44
Rachel Spronken-Smith . (2012). Experiencing the Process of Knowledge Creation: The Nature and Use of Inquiry-Based Learning in Higher Education, The Journal of Geography in Higher Education(2), 183–201.
Paulson, F. L., Paulson, P. R., & Meyer, C. (1991). What Makes a Portfolio a Portfolio? Educational Leadership, 48(5), 60-63.
Quera, V., & Bakeman, R. (2000). Quantification strategies in behavioral observation research. In T. Thompson, D. Felce, & F. J. Symons (Eds), Behavioral observation (1 ed., pp. 297–315).
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. doi:10.1002/cae.20456
Romero, C., Porras, A., Ventura, S., Hervas, C., & Zafra, A. (2006). Using sequential pattern mining for links recommendation in adaptive hypermedia educational systems. Current Developments on Technology-Assisted Education , 1016-1020.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146. doi:10.1016/j.eswa.2006.04.005
Rong Gu, Miaoliang Zhu, Liying Zhao, & Ningning Zhang. (2008). Interest mining in virtual learning environments. Online Information Review, 32(2), 133-146. doi:10.1108/14684520810879782
Schraw, G., & Dennison, R. S. (1994). Assessing Metacognitive Awareness. Contemporary Educational Psychology, 19(4), 460-475. doi:10.1006/ceps.1994.1033
Sparapani, E. F., Abel, F. J., Easton, S. E., Edwards, P., & Herbster, D. L. (1996). Portfolio Assessment: A Way to Authentically Monitor Progress and Evaluate Teacher Preparation. Annual Meeting of the Association of Teacher Educators’ 76th Annual Meeting. (pp. 1-20), St. Louis,Missouri.
SPMF. (20151019). Documentation. SPMF. Retrieved October 19, 2015, from http://www.philippe-fournier-viger.com/spmf/index.php?link=documentation.php#example13
Stohr-Hunt, P. M. (1996). An analysis of frequency of hands-on experience and science achievement. Journal of Research in Science Teaching, 33(1), 101-109. doi:10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z
Sweller, J., Merrienboer, J. J. G. van, & Paas, F. G. W. C. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:10.1023/A:1022193728205
Tin Can API. (2015a, October 17). Learning Record Store. Retrieved October 17, 2015, from https://tincanapi.com/learning-record-store/
Tin Can API. (2015b, October 17). SCORM vs Tin Can API. Retrieved October 17, 2015, from https://tincanapi.com/scorm-vs-the-tin-can-api/
Tin Can API. (2015c, October 17). What is the Tin Can API? Retrieved October 17, 2015, from https://tincanapi.com/overview/
Wu, H.-K., & Hsieh, C.-E. (2006). Developing Sixth Graders’ Inquiry Skills to Construct Explanations in Inquiry‐based Learning Environments. International Journal of Science Education, 28(11), 1289-1313. doi:10.1080/09500690600621035
Wu, J.-W., Tseng, J. C. R., Hwang, G.-J., Wu, J.-W., Tseng, J. C. R., & Hwang, G.-J. (2015). Development of an Inquiry-Based Learning Support System Based on an Intelligent Knowledge Exploration Approach. Educational Technology & Society, 18(3), 282–300.
Yang, T. C., Chen, Y. & Hwang, G. J. (2015). The influences of a two-tier test strategy on student learning: A lag sequential analysis approach. Computers & Education, 82, 366-377.
Yenilmez, A., Sungur, S., & Tekkaya, C. (2006). Students’ achievement in relation to reasoning ability, prior knowledge and gender. Research in Science & Technological Education, 24(1), 129–138. doi:10.1080/02635140500485498
Zion, M., Michalsky, T., & Mevarech, Z. R. (2005). The effects of metacognitive instruction embedded within an asynchronous learning network on scientific inquiry skills. International Journal of Science Education, 27(8), 957-983. doi:10.1080/09500690500068626
描述 碩士
國立政治大學
圖書資訊與檔案學研究所
103155001
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103155001
資料類型 thesis
dc.contributor.advisor 陳志銘zh_TW
dc.contributor.author (Authors) 王文芳zh_TW
dc.creator (作者) 王文芳zh_TW
dc.date (日期) 2016en_US
dc.date.accessioned 2-Aug-2016 17:55:30 (UTC+8)-
dc.date.available 2-Aug-2016 17:55:30 (UTC+8)-
dc.date.issued (上傳時間) 2-Aug-2016 17:55:30 (UTC+8)-
dc.identifier (Other Identifiers) G0103155001en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/99592-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 圖書資訊與檔案學研究所zh_TW
dc.description (描述) 103155001zh_TW
dc.description.abstract (摘要) 探究式教學法常被應用於科學學習活動中，有助於學生理解科學本質與推理過程，探究式學習歷程對於掌握影響探究式學習成效因素具有其重要意義，但是教師難以完全掌握學生的探究式學習歷程。因此，若能有目的、精確且真實的紀錄學習者在科學探究學習平台上的學習行為，將能更加全面性的掌控影響探究式學習成效的原因。

本研究以CWISE為輔助學習者進行科學探究學習之平台，並於平台中發展xAPI學習歷程記錄器模組，詳細的紀錄學生之學習歷程，以即時收集學生在科學探究學習過程之學習歷程資料，除了分析影響探究學習成效的個人因素與學習歷程行為外，並搭配序列探勘方法(sequential pattern mining)及序列分析(lag sequential analysis)，探討不同學習成效、探究能力、科學態度學習者之探究學習成效、整體學習時間與探究式模擬實驗學習時間，以及探究學習歷程是否具有顯著轉移與差異。

研究結果顯示：(1)探究能力越高者其探究學習成效越佳；(2)體驗探究模擬實驗活動時間越長，其探究能力與學習成效越佳；(3) 基於序列探勘，高低不同學習成效學習者在探究學習之整體課程瀏覽順序無顯著差異，均是依照課程設計的探究式學習流程順序進行學習；(4)基於序列分析，高學習成效與高探究能力學習者在進行浮力單元模擬實驗後，會再次調整先前設立之假說，而低學習成效與低探究能力學習者，則欠缺此一關鍵的探究學習行為；(5)探究模擬實驗活動有助於提高其探究能力與學習成效。最後，本研究依據研究結果針對探究式課程的課程設計提出建議，並提出未來研究方向。		zh_TW
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的與問題 3
第三節 研究範圍與限制 4
第四節 名詞解釋 5
第二章 文獻探討 7
第一節 科學探究能力 7
第二節 學習歷程紀錄 8
第三節 序列探勘於教學上的應用 10
第四節 序列分析及其應用 12
第三章 研究方法與實驗設計 14
第一節 研究架構 14
第二節 研究方法 16
第三節 研究對象 17
第四節 研究工具 17
第五節 系統架構 27
第六節 實驗流程 29
第七節 資料分析方法 30
第八節 研究步驟 30
第四章 實驗結果分析 33
第一節 整體學習者之學習成效分析 33
第二節 不同變項組別學習者之學習成效差異分析 33
第三節 不同組別學習者之學習時間差異分析 36
第四節 不同組別學習者之探究活動學習時間差異分析 37
第五節 學習成效、探究能力與科學態度、學習時間之相關性分析 38
第六節 科學探究行為序列探勘 39
第七節 序列分析 44
第八節 訪談資料整理 46
第九節 實驗分析結果歸納與討論 48
第五章 結論與建議 51
第一節 結論 51
第二節 教學建議 52
第三節 未來研究方向 53
參考文獻 54		zh_TW
dc.format.extent 1305844 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103155001en_US
dc.subject (關鍵詞) 探究式教學zh_TW
dc.subject (關鍵詞) 學習歷程紀錄zh_TW
dc.subject (關鍵詞) 學習歷程分析zh_TW
dc.subject (關鍵詞) 序列探勘zh_TW
dc.subject (關鍵詞) 序列分析zh_TW
dc.title (題名) 基於序列探勘之網路科學探究學習歷程分析zh_TW
dc.title (題名) Learning Process Analysis Based on Sequential Pattern Mining in a Web-based Inquiry Science Environmenten_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 吳武典、林幸台、王振德、郭靜姿修訂（1999）。基本人格量表。台北縣：心理出版社。
林奇賢、黃耿鐘（2007）。數位學習歷程檔案系統在網路學習環境中的角色與意義。理工研究學報，41（2），43–64。
林敏慧、陳美樺、管怡婷、郭榮學、陳慶帆（2001年6月）。網路教學與傳統教學之差異與融合分析。2001全球華人計算機教育應用大會，國立中央大學。
林緯倫, & 連韻文。(2001)。如何能發現隱藏的規則？從科學資優生表現的特色，探索提升規則發現能力的方法。科學教育學刊9(3)，197–217。
紀秋雲（2013）。資訊科技融入教學對國小高年級學童學習成效之研究-以新北市某國小為例。未出版之碩士論文。銘傳大學教育研究所碩士在職專班，桃園市。
徐慶雲(2008)。實施探究式科學闖關遊戲提升國小學童科學學習成就之行動研究。未出版之碩士論文。國立屏東教育大學數理教育研究所論文，屏東市。
黃月怡（2011）。應用分類技術於線上學習之研究。未出版之碩士論文。國立中正大學資訊管理學系暨研究所，嘉義縣。
陳文森（2003）。非同步網路教學學習路徑的研究。未出版之碩士論文。國立高雄師範大學資訊教育研究所，高雄市。
教育部（2008）。國民中小學九年一貫課程綱要自然與生活科技學習領域。台北：教育部。
許金山（2007）。混合式數位學習歷程及成效之分析。生活科技教育月刊，39（1），66–84。
黃秋瑞（2003）。以科學史教材協助高中教師瞭解克卜勒定律概念發展之效益研究。未出版之碩士論文。國立臺灣師範大學科學教育研究所，台北市。
黃毓琪（2007）。IT及STS探究式教學對國小學童科學解釋能力之影響。未出版之碩士論文。國立屏東教育大學，屏東市。
莊嘉坤（1995）。國小學生科學態度潛在類別的分析研究。國立屏東師院學報，8，111-136。
楊建民（2010）。探究式教學法與講述式教學法在國小Scratch程式教學學習成效之研究。未出版之碩士論文。國立屏東教育大學資訊科學系，屏東市。
劉宏文（2001）。高中學生進行開放式科學探究活動之個案研究。未出版之碩士論文。國立彰化師範大學科學教育研究所，彰化市。
蘇懿生、黃台珠(1998)。對科學的態度-ㄧ個有待研究的問題。科學教育月刊，215，2-13。
Abdi, A. (2014). The Effect of Inquiry-Based Learning Method on Students’ Academic Achievement in Science Course. Universal Journal of Educational Research, 2(1), 37–41.
ADL Net. (2015, December 8). xAPI-Dashboard. Retrieved December 8, 2015, from https://github.com/adlnet/xAPI-Dashboard
ADL. (2014). Sharable Content Object Reference Model (SCORM)2004.Retrieved October 15, 2015, from http://www.eife-l.org/publications/standards/elearning-standard/scormoverview/english_release
Advanced Distributed Learning. (2015, October 15). Advanced Distributed Learning. Retrieved October 15, 2015, from http://www.adlnet.org/
Agrawal, R., & Srikant, R. (1995, March). Mining Sequential Patterns. In Proceedings of the Eleventh International Conference on Data Engineering (pp. 3-14). Washington, DC, USA: IEEE Computer Society.
Bakeman, R., Deckner, D. F., & Quera, V. (2005). Analysis of behavioral streams. In D. M. Teti (Ed.), Handbook of research methods in developmental science (pp. 394–420). Oxford, UK: Blackwell Publishers.
Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., & Koller, D. (2014). Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming. Journal of the Learning Sciences, 23(4), 561-599. doi:10.1080/10508406.2014.954750
Buffler, A., Allie, S., & Lubben, F. (2001). The development of first year physics students’ ideas about measurement in terms of point and set paradigms. International Journal of Science Education, 23(11), 1137-1156. doi:10.1080/09500690110039567
Burch, C. B. (1999). Inside the Portfolio Experience: The Student’s Perspective. English Education, 32(1), 34-49.
Cavallo, A. M. L., Potter, W. H., & Rozman, M. (2004). Gender Differences in Learning Constructs, Shifts in Learning Constructs, and Their Relationship to Course Achievement in a Structured Inquiry, Yearlong College Physics Course for Life Science Majors. School Science and Mathematics, 104(6), 288–300. doi:10.1111/j.1949-8594.2004.tb18000.x
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331. doi:10.1504/IJTEL.2012.051815
Chen, C.-M., & Chang, C.-C. (2014). Mining learning social networks for cooperative learning with appropriate learning partners in a problem-based learning environment. Interactive Learning Environments, 22(1), 97-124. doi:10.1080/10494820.2011.641677
Chen, C.-M., & Chen, M.-C. (2009). Mobile formative assessment tool based on data mining techniques for supporting web-based learning. Computers & Education, 52(1), 256-273. doi:10.1016/j.compedu.2008.08.005
Chen, C.-M., Hsieh, Y.-L., & Hsu, S.-H. (2007). Mining learner profile utilizing association rule for web-based learning diagnosis. Expert Systems with Applications, 33(1), 6-22. doi:10.1016/j.eswa.2006.04.025
Chen, C-M. & Lin, S-T.(2014) Assessing effects of information architecture of digital libraries on supporting e-learning: A case study on the Digital Library of Nature & Culture. Computers & Education, 75(1) 92-102.
Chen, C.-M., Wang, J.-Y., Chen, Y.-T., & Wu, J.-H. (2014). Forecasting reading anxiety for promoting English-language reading performance based on reading annotation behavior. Interactive Learning Environments, 1-25. doi:10.1080/10494820.2014.917107
Chen, C.-T., & She, H.-C. (2014). The Effectiveness of Scientific Inquiry with/without Integration of Scientific Reasoning. International Journal of Science and Mathematics Education, 13(1), 1–20. doi:10.1007/s10763-013-9508-7
Chiang, T.H.C, Yang, S.J.H. & Hwang, G.J. (2014) ‘Students’ online interactive patterns in augmented reality-based inquiry activities’, Computers & Education, Vol. 78, pp.97–108.
Cropley, A. J., & Page, K. (2002). Creativity in Education and Learning—A Guide for Teachers and Educators. Long Range Planning, 35, 199-200.
Duval, E. (2011). Attention Please!: Learning Analytics for Visualization and Recommendation. Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 9–17). New York, USA doi:10.1145/2090116.2090118
Faghihi, U., Fournier-Viger, P., Nkambou, R., & Poirier, P. (2009). A Generic Episodic Learning Model Implemented in a Cognitive Agent by Means of Temporal Pattern Mining. Next-Generation Applied Intelligence (pp. 545-555). Springer Berlin Heidelberg.
Fortenbacher, A., Beuster, L., Elkina, M., Kappe, L., Merceron, A., Pursian, A., … Wenzlaff, B. (2013). LeMo: A learning analytics application focussing on user path analysis and interactive visualization. 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS) (Vol. 02, pp. 748-753). doi:10.1109/IDAACS.2013.6663025
Fournier-Viger, P., Faghihi, U., Nkambou, R., & Nguifo, E. M. (2010). Exploiting Sequential Patterns Found in Users’ Solutions and Virtual Tutor Behavior to Improve Assistance in ITS. Journal of Educational Technology & Society, 13(1), 13-24.
Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.-W., & Tseng, V. S. (2014). SPMF: a Java Open-Source Pattern Mining Library. Journal of Machine Learning Research, 1-5.
Fournier-Viger, P., Nkambou, R., & Nguifo, E. M. (2008). A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems. MICAI 2008: Advances in Artificial Intelligence: 7th Mexican International (pp. 765-778). Atizapán de Zaragoza, Mexico: Springer.
Fournier-Viger, P., Nkambou, R., Nguifo, E. M., & Faghihi, U. (2009). Building Agents That Learn by Observing Other Agents Performing a Task: A Sequential Pattern Mining Approach, Opportunities and Challenges for Next-Generation Applied Intelligence (pp. 279-284). Springer Berlin Heidelberg.
Freedman, M. P. (1997). Relationship among laboratory instruction, attitude toward science, and achievement in science knowledge. Journal of Research in Science Teaching, 34(4), 343-357. doi:10.1002/(SICI)1098-2736(199704)34:4<343::AID-TEA5>3.0.CO;2-R
Gardner, P. L., (1975). Attitudes to Science: A review. Studies In Science Education,59 (2), 1-41.
Germann, P. J., Aram, R., & Burke, G. (1996). Identifying patterns and relationships among the responses of seventh-grade students to the science process skill of designing experiments. Journal of Research in Science Teaching, 33(1), 79-99. doi:10.1002/(SICI)1098-2736(199601)33:1<79::AID-TEA5>3.0.CO;2-M
Gobert, J. D., Pedro, M. S., Raziuddin, J., & Baker, R. S. (2013). From Log Files to Assessment Metrics: Measuring Students’ Science Inquiry Skills Using Educational Data Mining. Journal of the Learning Sciences, 22(4), 521-563. doi:10.1080/10508406.2013.837391
Graf, S., Liu, T.C., & Kinshuk. (2010). Analysis of learners’ navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning, 26(2), 116-131. doi: 10.1111/j.1365-2729.2009.00336.x.
Greeno, J. G. (2001). Students with competence, authority, and accountability: Affording intellective identities in classrooms. New York: The College Board.
Haladyna, T., & Shaughnessy, J. (1982), Attitude toward science: a quantitive synthesis, Sci. Edu., 66(4), 547-563.
Haury, D. L. (1993). Teaching Science through Inquiry. ERIC Clearinghouse for Science Mathematics and Environmental Education. Columbus, OH.
He, W. (2013). Examining students’ online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior, 29(1), 90-102. doi:10.1016/j.chb.2012.07.020
Hsu, Y.-S., Chang, H.-Y., Fang, S.-C., & Wu, H.-K. (2015). Developing technology-infused inquiry learning modules to promote science learning in Taiwan. In M. S. Khine (Ed.), Science education in East Asia: Pedagogical innovations and best practices(pp. 373-403). New York: Springer.
Hung, J.-L., & Zhang, K. (2008). Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching. MERLOT Journal of Online Learning and Teaching, 4(4), 426-437.
Hwang, G.-J. (2003). A conceptual map model for developing intelligent tutoring systems. Computers & Education, 40(3), 217-235. doi:10.1016/S0360-1315(02)00121-5
Hwang, G.-J., & Chang, H.-F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers & Education, 56(4), 1023-1031. doi:10.1016/j.compedu.2010.12.002
Jan, J.-C., Chen, C.-M., & Huang, P.-H. (2016). Enhancement of digital reading performance by using a novel web-based collaborative reading annotation system with two quality annotation filtering mechanisms. International Journal of Human-Computer Studies, 86, 81-93. doi:10.1016/j.ijhcs.2015.09.006
Joanna Taylor, & Jerry Bilbrey. (2012). Effectiveness of inquiry based and teacher directed instruction in an Alabama elementary school. Journal of Instructional Pedagogies, 8, 17.
Jong, B.-S., Chan, T.-Y., & Wu, Y.-L. (2007). Learning Log Explorer in E-Learning Diagnosis. Education, IEEE Transactions on Education, 50(3), 216-228. doi:10.1109/TE.2007.900023
Kamber, M., Han, J., & Pei, J. (2012). Data Mining: Concepts and Techniques (Third Edition.). Boston: Morgan Kaufmann.
Klahr, D., & Dunbar, K., (1987). Dual space search during scientific reasoning . Cognitive Science, 12, 1-48.
Kuhn, D., Garcia-Mila, M., Zohar, A., Andersen, C., White, S. H., Klahr, D., & Carver, S. M. (1995). Strategies of Knowledge Acquisition. Monographs of the Society for Research in Child Development, 60(4), i-157. doi:10.2307/1166059
Lai, C.-L., & Hwang, G.-J. (2015). An interactive peer-assessment criteria development approach to improving students’ art design performance using handheld devices. Computers & Education, 85, 149-159. doi:10.1016/j.compedu.2015.02.011
Laxhammar, R., & Falkman, G. (2014). Online Learning and Sequential Anomaly Detection in Trajectories. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(6), 1158-1173. doi:10.1109/TPAMI.2013.172
Li, B., Kuo, R., Chang, M., & Garn, K. (2015, September). Reward Points Calculation based on Sequential Pattern Analysis in an Educational Mobile App. In the Proceedings of 21st International Conference on Distributed Multimedia Systems (pp. 186-190), Vancouver, Canada.
Lin, P.-C., Hou, H.-T., Wang, S.-M., & Chang, K.-E. (2013). Analyzing knowledge dimensions and cognitive process of a project-based online discussion instructional activity using Facebook in an adult and continuing education course. Computers & Education, 60, 110–121.
Marx, R. W., Blumenfeld, P. C., Krajcik, J. S., Fishman, B., Soloway, E., Geier, R., & Tal, R. T. (2004). Inquiry-based science in the middle grades: Assessment of learning in urban systemic reform. Journal of Research in Science Teaching, 41(10), 1063-1080. doi:10.1002/tea.20039
Moran, G., Dumas, J. E., & Symons, D. K. (1992). Approaches to sequential analysis and the description of contingency in behavioral interaction. Behavioral Assessment, 14, 65-92.
Myers, M. J., & Burgess, A. B. (2003). Inquiry-based laboratory course improves students’ ability to design experiments and interpret data. Advances in Physiology Education, 27(1-4), 26-33.
National Research Council. (2000). Inquiry and the National Science Education Standards:A Guide for Teaching and Learning. Washington, DC: National Research Council.
Nkambou, R., Fournier-Viger, P., & Nguifo, E. M. (2011). Learning task models in ill-defined domain using an hybrid knowledge discovery framework. Knowledge-Based Systems, 24(1), 176-185. doi:10.1016/j.knosys.2010.08.002
OUYang, Y., & Zhu, M. (2007). eLORM: Learning Object Relationship Mining based Repository. In The 9th IEEE International Conference on E-Commerce Technology and the 4th IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services, 2007 (pp. 691-698). doi:10.1109/CEC-EEE.2007.44
Rachel Spronken-Smith . (2012). Experiencing the Process of Knowledge Creation: The Nature and Use of Inquiry-Based Learning in Higher Education, The Journal of Geography in Higher Education(2), 183–201.
Paulson, F. L., Paulson, P. R., & Meyer, C. (1991). What Makes a Portfolio a Portfolio? Educational Leadership, 48(5), 60-63.
Quera, V., & Bakeman, R. (2000). Quantification strategies in behavioral observation research. In T. Thompson, D. Felce, & F. J. Symons (Eds), Behavioral observation (1 ed., pp. 297–315).
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. doi:10.1002/cae.20456
Romero, C., Porras, A., Ventura, S., Hervas, C., & Zafra, A. (2006). Using sequential pattern mining for links recommendation in adaptive hypermedia educational systems. Current Developments on Technology-Assisted Education , 1016-1020.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146. doi:10.1016/j.eswa.2006.04.005
Rong Gu, Miaoliang Zhu, Liying Zhao, & Ningning Zhang. (2008). Interest mining in virtual learning environments. Online Information Review, 32(2), 133-146. doi:10.1108/14684520810879782
Schraw, G., & Dennison, R. S. (1994). Assessing Metacognitive Awareness. Contemporary Educational Psychology, 19(4), 460-475. doi:10.1006/ceps.1994.1033
Sparapani, E. F., Abel, F. J., Easton, S. E., Edwards, P., & Herbster, D. L. (1996). Portfolio Assessment: A Way to Authentically Monitor Progress and Evaluate Teacher Preparation. Annual Meeting of the Association of Teacher Educators’ 76th Annual Meeting. (pp. 1-20), St. Louis,Missouri.
SPMF. (20151019). Documentation. SPMF. Retrieved October 19, 2015, from http://www.philippe-fournier-viger.com/spmf/index.php?link=documentation.php#example13
Stohr-Hunt, P. M. (1996). An analysis of frequency of hands-on experience and science achievement. Journal of Research in Science Teaching, 33(1), 101-109. doi:10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z
Sweller, J., Merrienboer, J. J. G. van, & Paas, F. G. W. C. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:10.1023/A:1022193728205
Tin Can API. (2015a, October 17). Learning Record Store. Retrieved October 17, 2015, from https://tincanapi.com/learning-record-store/
Tin Can API. (2015b, October 17). SCORM vs Tin Can API. Retrieved October 17, 2015, from https://tincanapi.com/scorm-vs-the-tin-can-api/
Tin Can API. (2015c, October 17). What is the Tin Can API? Retrieved October 17, 2015, from https://tincanapi.com/overview/
Wu, H.-K., & Hsieh, C.-E. (2006). Developing Sixth Graders’ Inquiry Skills to Construct Explanations in Inquiry‐based Learning Environments. International Journal of Science Education, 28(11), 1289-1313. doi:10.1080/09500690600621035
Wu, J.-W., Tseng, J. C. R., Hwang, G.-J., Wu, J.-W., Tseng, J. C. R., & Hwang, G.-J. (2015). Development of an Inquiry-Based Learning Support System Based on an Intelligent Knowledge Exploration Approach. Educational Technology & Society, 18(3), 282–300.
Yang, T. C., Chen, Y. & Hwang, G. J. (2015). The influences of a two-tier test strategy on student learning: A lag sequential analysis approach. Computers & Education, 82, 366-377.
Yenilmez, A., Sungur, S., & Tekkaya, C. (2006). Students’ achievement in relation to reasoning ability, prior knowledge and gender. Research in Science & Technological Education, 24(1), 129–138. doi:10.1080/02635140500485498
Zion, M., Michalsky, T., & Mevarech, Z. R. (2005). The effects of metacognitive instruction embedded within an asynchronous learning network on scientific inquiry skills. International Journal of Science Education, 27(8), 957-983. doi:10.1080/09500690500068626		zh_TW