第五章 結論與建議
第三節 未來研究方向
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第三節 未來研究方向
一、跨平台學習歷程整合
本研究僅探討學習者在 CWISE 平台上的學習歷程,未來可以整合不同類型 的數位學習平台進行更廣泛面向的學習歷程蒐集,並進行不同能素養能力的學習 歷程探勘分析,作為教學者教學設計之有效參考。
二、設計探究式課程引導精靈提升學習者學習成效
本研究利用序列分析與序列探勘得知,不同探究能力與學習成效學習者之學 習歷程具有差異,在未來若能將高探究能力與高學習成效學習者之學習行為,利 用引導精靈的方式融入探究式平台功能,使得系統偵測出某一學習者學習行為尚 未符合高探究能力與學習成效學習者的學習行為時,可以適時予以提醒並加以引 導,進而提升學習者的學習成效。
三、學習者學習歷程資料即時分析作為系統教學策略調整依據
本研究針對學習者歷程分析為事後分析,無法再進行課程時即時給予教師即 時回饋。若能將序列分析與序列探勘功能模組與 CWISE 教學平台整合,進而針 對學習者學習歷程進行即時分析,立即顯示結果並提供授課教師參考,即可作為 即時調整教學策略重要依據。
四、搭配腦波注意力偵測系統蒐集探究式學習過程注意力歷程資料
本研究利用 xAPI 學習歷程監控模組即時記錄學習者學習歷程,並依據歷程 紀錄結果探勘學習者使用行為,但無法準確得知當下使用者注意力情形,若能搭 配腦波注意力偵測系統,即時針對每位學習者當下學習行為所與對應注意力進行 分析,將可進一步探勘出導致高低注意力的學習行為。‧ 國
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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
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
55
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.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
56
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
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
57
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
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
58
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
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
59
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-‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
60
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.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
61
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
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
62
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.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
63
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
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
64
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
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
65
scientific inquiry skills. International Journal of Science Education, 27(8), 957-983. doi:10.1080/09500690500068626