應用文字探勘於數位學習環境的形成性評量

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：45

、訪客IP：3.147.68.39

姓名

許榮隆(Jung-Lung Hsu) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

應用文字探勘於數位學習環境的形成性評量
(A Text Mining Approach for Formative Assessment in e-Learning Environment)

相關論文

★ 技術商品銷售之技術人員關鍵職能探討	★ 資訊委外之承包商能力、信任及溝通與委外成效關係之個案研究
★ 兵工技術軍官職能需求分析-以某軍事工廠為例	★ 不同楷模學習模式對VB程式語言學習之影響
★ 影響採購「網路資料中心產品」因素之探討	★ 資訊人員績效評估之研究—以陸軍某資訊單位為例
★ 高職資料處理科學生網路成癮相關因素及其影響之探討	★ 資訊服務委外對資訊部門及人員之衝擊-某大型外商公司之個案研究
★ 二次導入ERP系統之研究-以某個案公司為例	★ 資料倉儲於證券產業應用之個案研究
★ 影響消費者採用創新數位產品之因素---以整合式手機為例	★ 企業合併下資訊系統整合過程之個案研究
★ 資料倉儲系統建置之個案研究	★ 電子表單系統導入之探討 - 以 A 公司為例
★ 企業資訊安全機制導入與評估–以H公司為例	★ 從人力網站探討國內資訊人力現況–以104銀行資料為例

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]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

近年來，有許多的教學者對於e-learning的方式深感興趣，並嘗試將其導入實務的教學情境中。此外，也有許多的研究學者體認到e-learning的蓬勃發展，而致力於相關研究問題的探討。本研究藉由歸納出學習績效的評估類型和學習績效的評估單位兩個構面，重新構思學習績效。本文的重點在於區分出個人認知的形成性評量和集體認知的形成性評量。此一劃分方式之所以重要的原因在於，本文認為集體認知的形成性評量不應該被簡化為個人認知的形成性評量的總和。具體而言，個人認知的形成性評量和集體認知的形成性評量，具有彼此交互糾結的本質，因此實施形成性評量時不宜偏廢任一環節。有鑑於此，本研究認為針對小組進行形成性評量時，除了針對小組成員的認知結構和認知過程進行分析，更應當注意集體認知的形成性分析。此外，實施形成性評量的主要瓶頸在於其勞力密集且耗時的本質，因此本研究嘗試借助於一項新的資訊技術：文字探勘，以便提出可行的方法並解除形成性評量所加諸於教學者身上的負擔。本研究資料的來源為修習人力資源管理課程的56位學生所進行的小組討論。在個人認知的形成性評量方面，學習者的認知層次將根據Bloom的教育目標分類分為六類：知識、理解、應用、分析、綜合和評鑑。而在集體認知的形成性評量方面，本研究將結合潛在語意索引，提出集體認知環以便描繪受測小組的集體認知層次。總結來說，本研究不僅提出集體認知的形成性評量，同時也嘗試提出一個合理的方法，幫助教學者在e-learning的環境下，進行個人認知的形成性評量和集體認知的形成性評量。

摘要(英)

Recently, not only instructors and educators are interested in the advent of e-learning, but a number of researchers are eager to shed light on relevant issues on this field. This study conceptualizes learning performance along two dimensions: Type of assessment and unit of assessment. Accordingly, distinction of formative assessment between individual cognition and collective cognition is introduced. This is an important distinction because the attempt regarding formative assessment of collective cognition as the sum of that of individual cognitions may be misguided. More specifically, the intertwined nature of formative assessment of collective cognition and individual cognition suggests researchers draw attention to analyzing cognitive structures and cognitive processes at the individual level as well as at collective level. Furthermore, since the major bottleneck of putting formative assessment into practice lies in the nature of labor-intensive and time-consuming, this study conceives one feasible way: text mining, to relieve the burden imposed on instructors. Data will be gathered from 56 participants enrolling in a “Human Resource Management” course. In the formative assessment of individual cognition, learners’ cognition will be classified into six level, namely knowledge, comprehension, application, analysis, synthesis and evaluation, based on Bloom’s taxonomy of educational objectives. With respect to formative assessment of collective cognition, this study will make use of latent semantic indexing to outline a collective cognition circle. To sum up, the approach introduced in this study serves a well solution to consider formative assessment of both individual cognition and collective cognition.

關鍵字(中)

★ 文字探勘
★ 形成性評量
★ 數位學習
★ 集體認知

關鍵字(英)

★ Text mining
★ formative assessment
★ e-learning
★ collective cognition

論文目次

CHAPTER 1. INTRODUCTION 1
1.1 Research background 1
1.2 Research objectives 5
1.3 Overview of chapters 6
CHAPTER 2 LITERATURE REVIEW 7
2.1 Assessment of learning performance 7
2.1.1 Summative assessment 9
2.1.2 Formative assessment 9
2.1.3 A brief summary 10
2.2 Knowledge construction as social interaction and cognitive development 13
2.2.1 Personal aspect of knowledge construction 13
2.2.2 Interpersonal aspect of knowledge construction 14
2.2.3 A brief summary 16
2.3 An approach to quantify and visualize formative assessment 18
2.4.1 A short introduction to text mining 24
2.4.2 Latent semantic indexing 29
CHAPTER 3 RESEARCH DESIGN 38
3.1 Research procedures 38
3.1.1 Steps toward individual cognition assessment 38
3.2.2 Steps toward collective cognition assessment 48
3.2 Participants 53
3.3 Evaluation measurement 54
3.4 System environment 56
CHAPTER 4 RESULTS 59
4.1 Preprocessing 59
4.2 Evaluation 60
4.3 Results about individuals cognition 63
4.4 Results about collective cognition 67
CHAPTER 5 DISCUSSION 74
CHAPTER 6 CONCLUSION 89
6.1 Contributions 90
6.2 Implications 91
6.3 Limitations 95
REFERENCES 97

參考文獻

Airasian, P. W., & Miranda, H. (2002). The Role of Assessment in the Revised Taxonomy. Theory Into Practice, 41(4), 249-254.
Allen, J., James, A. D., & Gamlen, P. (2007). Formal versus informal knowledge networks in R&D: a case study using social network analysis. R&D Management, 37(3), 179-196.
Amer, A. (2006). Reflections on Bloom's Revised Taxonomy. Electronic Journal of Research in Educational Psychology, 4(1), 213-230.
Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: a revision of Bloom's taxonomy of educational objectives: Longman, New York.
Anderson, L. W., & Sosniak, L. A. (1994). Bloom's taxonomy: a forty-year retrospective: National Society for the Study of Education Chicago, Ill.: Distributed by the University of Chicago Press, Chicago, Ill.
Banks, S. R. (2005). Classroom assessment: issues and practices: Pearson/Allyn and Bacon, Boston.
Bargh, J. A., & Schul, Y. (1980). On the Cognitive Benefits of Teaching. Journal of Educational Psychology, 72(5), 593-604.
Berends, H., Bij, H., Debackere, K., & Weggeman, M. (2006). Knowledge sharing mechanisms in industrial research. R&D Management, 36(1), 85-95.
Black, P., & Wiliam, D. (1998). Assessment and Classroom Learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7-74.
Bloom, B. S. (1956). Taxonomy of Educational Objectives, Handbook I: Cognitive Domain. New York: McKay.
Bloom, B. S. (1971). Handbook on Formative and Summative Evaluation of Student Learning: New York: McGraw-Hill.
Bloom, B. S. (1994). Reflections on the development and use of the taxonomy. Yearbook: National Society for the Study of Education, 92(2), 1-8.
Brandon, D. P., & Hollingshead, A. B. (2004). Transactive Memory Systems in Organizations: Matching Tasks, Expertise, and People. Organization Science, 15(6), 633-644.
Brown, A. L., & Campione, J. C. (1986). Psychological Theory and the Study of Learning Disabilities. American Psychologist, 41(10), 1059-1068.
Ciravegna, F. (2001). LP 2 an Adaptive Algorithm for Information Extraction from Web-related Texts. Proc. of the IJCAI-2001 Workshop on Adaptive Text Extraction and Mining held in conjunction with the 17th International Conference on Artificial Intelligence (IJCAI-01), August.
Cobb, P. (1995). Mathematical learning and small-group interaction: Four case studies. The emergence of mathematical meaning: Interaction in classroom cultures, 25-129.
Cochran, D., & Conklin, J. (2007). A New Bloom: Transforming Learning. Learning & Leading with Technology, 34(5), 4.
Crooks, T. J. (1988). The impact of classroom evaluation practices on students. Review of Educational Research, 58(4), 438-481.
Earl, L. M. (2003). Assessment as Learning: Using Classroom Assessment to Maximize Student Learning: Corwin Press.
Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization Networks and Support Vector Machines. Advances in Computational Mathematics, 13(1), 1-50.
Feldman, R., & Sanger, J. (2006). The Text Mining Handbook: advanced approaches in analyzing unstructured data: Cambridge University Press.
Fleiss, J. (1981 ). The measurement of interrater agreement: Wiley, New York.
Foo, S., & Li, H. (2004). Chinese word segmentation and its effect on information retrieval. Information Processing and Management, 40(1), 161-190.
Frechtling, J. A., & Sharp, L. M. (1997). User-Friendly Handbook for Mixed Method Evaluations: DIANE Publishing.
Freedman, R. L. H. (1994). Open-ended Questioning: A Handbook for Educators: Addison-Wesley.
Garfinkel, H. (1967). Studies inEthnomethodology: EnglewoodCliffs, NJ: PrenticeHall.
Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques: Morgan Kaufmann.
Harlen, W. (1996). Four Years of Change in Education 5-14: US: ERIC.
Hearst, M. A. (1999). Untangling text data mining. Proceedings of the 37th conference on Association for Computational Linguistics, 3-10.
Huang, C. J., Tsai, P. H., Hsu, C. L., & Pan, R. C. (2006). Exploring Cognitive Difference in instructional outcomes using Text mining technology. Systems, Man and Cybernetics, 2006. ICSMC'06. IEEE International Conference on, 3.
Huba, M. E., & Freed, J. E. (2000). Learner-Centered Assessment on College Campuses: Shifting the Focus from Teaching to Learning.
Hutchins, E. (1995). Cognition in the Wild: Bradford Books.
Hutchins, E. (2000). Distributed Cognition. Retrieved March, 14, 2001.
Hwang, W. Y., Chen, N. S., & Hsu, R. L. (2006). Development and evaluation of multimedia whiteboard system for improving mathematical problem solving. Computers & Education, 46(2), 105-121.
Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Proceedings of ECML-98, 10th European Conference on Machine Learning, 1398, 137–142.
Johnson, D. W., & Johnson, R. T. (1992). 8 Positive Interdependence: Key to Effective Cooperation. Interaction in Cooperative Groups: The Theoretical Anatomy of Group Learning.
Kreijns, K., Kirschner, P. A., & Jochems, W. (2002). The sociability of computer-supported collaborative learning environments. Educational Technology & Society, 5(1), 8-22.
Kreitzer, A. E., & Madaus, G. F. (1994). Empirical investigations of the hierarchical structure of the taxonomy. Bloom’s taxonomy. A forty-year retrospective, 64–81.
Kubiszyn, T., & Borich, G. (2003). Educational testing and measurement: Classroom applications and practice New York: John Wiley & Son Inc.
Lee, E. J. (2004). Effects of Visual Representation on Social Influence in Computer-Mediated Communication: Experimental Tests of the Social Identity Model of Deindividuation Effects. Human Communication Research, 30(2), 26.
Li, H., & Yamanishi, K. (2001). Mining from open answers in questionnaire data. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 443-449.
Liang, D. W., Moreland, R., & Argote, L. (1995). Group Versus Individual Training and Group Performance: The Mediating Role of Transactive Memory. Personality and Social Psychology Bulletin, 21(4), 384.
Manaris, B., Wainer, M., Kirkpatrick, A. E., Stalvey, R. A. H., Shannon, C., Leventhal, L., et al. (2007). Implementations of the CC 0 01 Human–Computer Interaction Guidelines using Bloom’s Taxonomy. Computer Science Education, 17(1), 21-57.
Marzano, R. J. (2001). Designing a New Taxonomy of Educational Objectives. Experts in Assessment.
Mei, Q., & Hu, J. (2004). From Text to Exhibitions: A New Approach for E-Learning on Language and Literature based on Text Mining. COLING 2004 eLearning for Computational Linguistics and Computational Linguistics for eLearning}, 90-96.
Mladenic, D. (2006). Text Mining in Action! From Data and Information Analysis to Knowledge Engineering: Proceedings of the 29th Annual Conference of the Gesellschaft Fur Klassifikation EV, University of Magdeburg, March 9-11, 2005.
Mochizuki, T., Fujitani, S., Isshiki, Y., Yamauchi, Y., & Kato, H. (2003). Assessment of Collaborative Learning for Students: Making the State of Discussion Visible for their Reflection by Text Mining of Electronic Forums. Paper presented at the Proceedings of E-Learn.
Moreland, R. L. (1999). Transactive memory: Learning who knows what in work groups and organizations. Shared cognition in organizations: The management of knowledge, 3-31.
Nahm, U. Y., & Mooney, R. J. (2002). Text Mining with Information Extraction. AAAI 2002 Spring Symposium on Mining Answers from Texts and Knowledge Bases.
O'Donnell, A. M., & Dansereau, D. F. (2000). Interactive effects of prior knowledge and material format on cooperative teaching. The Journal of experimental education, 68(2), 101-118.
Palomba, C. A., & Banta, T. W. (1999). Assessment Essentials: Planning, Implementing, and Improving Assessment in Higher Education: Jossey-Bass.
Pavitt, K. (2003). Specialization and systems integration: where manufacture and services still meet. The Business of Systems Integration, 78–91.
Piaget, J. (1973). The Child's Conception of the World. London: Paladin.
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing and Management: an International Journal, 24(5), 513-523.
Scriven, M. (1967). The Methodology of Education. Perspectives of Curriculum Evaluation. Chicago, IL: Rand McNally.
Scriven, M. (1991). Evaluation Thesaurus. Newbury Park, CA: Sage.
Sfard, A., & McClain, K. (2002). Guest Editor's Introduction: Analyzing Tools: Perspectives on the Role of Designed Artifacts in Mathematics Learning. The Journal of the Learning Sciences, 11(2-3), 153-161.
Simon, H. A. (1996). The Sciences of the Artificial: MIT Press.
Sullivan, D. (2001). Document warehousing and text mining: techniques for improving business operations, marketing and sales: Wiley, New York.
Tanner, D. E. (2001). Assessing academic achievement: Allyn and Bacon Boston.
Taylor, M. J., Pountney, D. C., & Baskett, M. (2008). Using animation to support the teaching of computer game development techniques. Computers & Education, 50(4), 1258-1268.
Tseng, Y. H. (1997). The Concept of Automatic Keyword Extraction Technology. Bulletin of the Library Association of China, 59(2), 69-64.
van der Meijden, H., & Veenman, S. (2005). Face-to-face versus computer-mediated communication in a primary school setting. Computers in Human Behavior, 21(5), 831-859.
Vapnik, V. (1998). The Support Vector Method of Function Estimation. Nonlinear Modeling: Advanced Black-Box Techniques.
Vera, A. H., & Simon, H. A. (1993). Situated action: A symbolic interpretation. Cognitive Science, 17(1), 7-48.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds. & Trans.): Cambridge, MA: Harvard University Press.(Original work published 1934).
Webb, N. M., Farivar, S. H., & Mastergeorge, A. M. (2002). Productive Helping in Cooperative Groups. Theory Into Practice, 41(1), 13-20.
Wegner, D. M. (1987). Transactive memory: A contemporary analysis of the group mind. Theories of Group Behavior, 185-208.
Willis, D. (1994). School-Based Assessment: Underlying ideologies and their implications for teachers and learners. New Zealand Journal of Educational Studies, 29(2), 161-174.
Wilson, J. M., Straus, S. G., & McEvily, B. (2006). All in due time: The development of trust in computer-mediated and face-to-face teams. Organizational Behavior and Human Decision Processes, 99(1), 16-33.
Yang, S. J. H., Chen, I. Y. L., & Chen, N. S. (2007). Enhancing the Quality of e-Learning in Virtual Learning Communities by Finding Quality Learning Content and Trustworthy Collaborators. Journal of Educational Technology & Society, 10(2), 84-95.

指導教授

周惠文(Huey-Wen Chou)

審核日期

2008-7-18

推文