| 研究生: |
杜德一 De-yi Du |
|---|---|
| 論文名稱: |
探討學習夥伴推薦機制如何影響學習者對於e-Portfolio的使用意願之研究 The role of learning companion recommendation and how it affects learners’ intention to use e-Portfolio |
| 指導教授: |
楊鎮華
J.H. Yang |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
資訊電機學院 - 資訊工程學系在職專班 Executive Master of Computer Science & Information Engineering |
| 畢業學年度: | 98 |
| 語文別: | 中文 |
| 論文頁數: | 63 |
| 中文關鍵詞: | 推薦系統 、科技接受模型 、正規概念分析 、資訊系統成功模型 、e-Portfolio |
| 外文關鍵詞: | FCA, e-Portfolio, IS Success model, TAM, Recommendation system |
| 相關次數: | 點閱:12 下載:0 |
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e-Portfolio平台提供學生記錄各種學習檔案,能幫助學生在學習上反思,並提升學習成效,e-Portfolio是一種社會學習環境,學習者能與學習夥伴進行社會學習活動,發揮協同學習的效果。本研究提出「學習夥伴推薦服務」(Learning Companion Recommendation Service, LCRS),以正規化概念分析(Formal Concept Analysis, FCA)計算學習樹相似度,推薦學習領域相似的學習夥伴,以增加互動機會而形成社群。
本研究以科技接受模型結合資訊系統成功模型的「資訊品質」與「服務品質」以及加入「主觀規範」等因素,提出探討LCRS對學習者使用e-Portfolio意願的影響的研究模型,研究變項有六個部份,分別為「推薦服務資訊品質」、「推薦服務服務品質」、「主觀規範」、「推薦服務認知易用性」、「推薦服務認知有用性」及「e-Portfolio使用意願」。在進行e-Portfolio教育訓練之後,以實體問卷施測取得樣本資料。研究結果顯示「推薦服務資訊品質」對於「推薦服務認知易用性」與「推薦服務認知有用性」有正向顯著影響;「推薦服務服務品質」對於「推薦服務認知易用性」與「推薦服務認知有用性」有正向顯著影響;「主觀規範」對「推薦服務認知易用性」與「推薦服務認知有用性」有正向顯著影響;「主觀規範」、「推薦服務認知易用性」與「推薦服務認知有用性」對「e-Portfolio使用意願」有顯著正向影響。整體而言LCRS對於學生使用e-Portfolio的意願有正向影響。
Electronic portfolio (e-Portfolio) platform provides students a place to record their learning portfolios. E-Portfolios can facilitate learners’ reflection on learning and enhance their learning effects. E-Portfolio platform is a social learning environment where learners can have social learning activities with their companion and exert collaborative learning. This research proposed Learning Companion Recommendation Service (LCRS), and used Formal Concept Analysis (FCA) to calculate the similarity of learning trees. LCRS can recommend learning companions in similar learning area and increase interactions between learning companions.
This research proposed a model which discussed whether LCRS had influences on learners’ willingness to use e-Portfolio. The proposed model was based on Technology Acceptance Model (TAM), “information quality” and “service quality” of Information System Success Model (IS Success Model) and “subjective norm”. The variables in this research were “information quality of LCRS”, “service quality of LCRS”, “subjective norm”, “perceived ease of use of LCRS”, “perceived usefulness of LCRS”, and “use intention of e-portfolio”. After e-portfolio training courses, learners responded the questionnaire. The results showed that “information quality” had significant influence on “perceived ease of use of LCRS” and “perceived usefulness of LCRS”; “service quality of LCRS” had significant influence on “perceived ease of use of LCRS” and “perceived usefulness of LCRS”; “subjective norm” had significant influence on “perceived ease of use of LCRS” and “perceived usefulness of LCRS”; “subjective norm”, “perceived ease of use of LCRS” and “perceived usefulness of LCRS” had significant influences on “use intention of e-portfolio”. In general, LCRS positively influenced learners’ willingness to use e-Portfolio.
Abrami, P. C., Wade, A., Pillay, V., Aslan, O., Bures, E. M., & Bentley, C. (2009). Encouraging self-regulated learning through electronic portfolios. Canadian Journal of Learning and Technology/La revue canadienne de l’apprentissage et de la technologie, 34(3).
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Ajzen, I. (2002). Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior. Journal of Applied Social Psychology, 32(4), 665-683.
Allee, V. (2000). Knowledge networks and communities of practice. OD Practitioner Online, 32(4), 1–15.
Barrett, H. C. (2007). Researching Electronic Portfolios and Learner Engagement: The REFLECT Initiative. Journal of Adolescent & Adult Literacy, 50(6), 436-449.
Chou, C., Chan, T., & Lin, C. (2003). Redefining the learning companion: the past, present, and future of educational agents. Computers & Education, 40(3), 255-269.
Chau, J. (2007). A developer’s challenges on an e-portfolio journey. In ICT: Providing choices for learners and learning. Proceedings ascilite Singapore 2007. Retrieved from http://www.ascilite.org.au/conferences/singapore07/procs/chau.pdf
Davide, T., Manuel, G., Giovanni, F., & Mario, A. (2009). E-portfolio and semantic web to support informal learning in social network environment. In International Conference on Virtual Learning ICVL 2009. Retrieved from http://www.icvl.eu/2009/disc/icvl/
documente/pdf/intel/ICVL_IntelEducation_paper04.pdf
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487.
DeLone, W. H., & McLean, E. R. (1992). Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3(1), 60-95.
Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9-30.
Evans, M. A., & Powell, A. (2007). Conceptual and practical issues related to the design for and sustainability of communities of practice: the case of e-portfolio use in preservice teacher training. Technology, Pedagogy and Education, 16(2), 199 - 214.
Hendriks, P. (1999). Why share knowledge? The influence of ICT on the motivation for knowledge sharing. Knowledge and Process Management, 6(2), 91-100.
Hillyer, J., & Ley, T. C. (1996). Portfolios and second graders'' self-assessments of their development as writers. Reading Improvement, 33, 148-59.
Hsu, C., & Lu, H. (2004). Why do people play on-line games? an extended TAM with social influences and flow experience. Inf. Manage., 41(7), 853-868.
Iskold, A. (2007, January 16). The Art, Science and Business of Recommendation Engines. ReadWriteWeb. Retrieved April 18, 2010, from the World Wide Web: http://www.readwriteweb.com/archives/recommendation_engines.php
Kim, T. G., Lee, J. H., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29(3), 500-513.
Kulkarni, U. R., Ravindran, S., & Freeze, R. (2007). A Knowledge Management Success Model: Theoretical Development and Empirical Validation. Journal of Management Information Systems, 23(3), 309-347.
Landrum, H., & Prybutok, V. R. (2004). A service quality and success model for the information service industry. European Journal of Operational Research, 156(3), 628-642.
Li, J. (2009). Theoretical Model of Consumer Acceptance: In the View of Website Quality. In E-Business and Information System Security, 2009. EBISS ''09. International Conference on (pp. 1-4). Presented at the E-Business and Information System Security, 2009. EBISS ''09. International Conference on.
Linton, F., & Schaefer, H. (2000). Recommender Systems for Learning: Building User and Expert Models through Long-Term Observation of Application Use. User Modeling and User-Adapted Interaction, 10(2-3), 181-208.
Lin, J., Chuan, C. H., & Denis, C. W. (2008). Usefulness, Ease of Use, Attitude, and Their Interaction Effects on Usage Intention of Three Electronic Mail Systems. Presented at the annual meeting of the International Communication Association, TBA, Montreal, Quebec, Canada. Retrieved from http://www.allacademic.com/meta/p230958_index.
html
Malhotra, Y., & Galletta, D. (1999). Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation. In Proceedings of the Hawaii International Conference on System Sciences (Vol. 32, pp. 5–5).
Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research, 2(3), 173-191.
Money, W., & Turner, A. (2004). Application of the Technology Acceptance Model to a Knowledge Management System. Hawaii International Conference on System Sciences, 8, 80237b.
Montaner, M., López, B., & de la Rosa, J. L. (2003). A Taxonomy of Recommender Agents on the Internet. Artificial Intelligence Review, 19(4), 285-330.
Nickols, F. (2003). Communities of practice: An overview. Retrieved from http://www.nick ols.us/CoPOverview.pdf
Priss, U. (2006). Formal concept analysis in information science. Annual Review of Information Science and Technology, 40(1), 521-543.
Ravet, S. (2005). ePortfolio for a learning society. In E Learning Conference, Brussels. Retrieved February (Vol. 26, p. 2008).
Resnick, P., & Varian, H. R. (1997). Recommender systems. Commun. ACM, 40(3), 56-58.
Roca, J. C., Chiu, C., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683-696.
Sarwar, B., Karypis, G., Konstan, J., & Reidl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295). Hong Kong, Hong Kong: ACM.
Schafer, J. B., Konstan, J., & Riedi, J. (1999). Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce (pp. 158-166). Denver, Colorado, United States: ACM.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103.
Seddon, P. B. (1997). A Respecification and Extension of the DeLone and McLean Model of IS Success. Information Systems Research, 8(3), 240.
Shen, D., Laffey, J., Lin, Y., & Huang, X. (2006). Social Influence for Perceived Usefulness and Ease-of-Use of Course Delivery Systems. Journal of Interactive Online Learning, 5(3).
Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144-176.
Thong, J. Y. L., Hong, W., & Tam, K. (2002). Understanding user acceptance of digital libraries: what are the roles of interface characteristics, organizational context, and individual differences? International Journal of Human-Computer Studies, 57(3), 215-242.
Tosh, D., & Werdmuller, B. (2004). ePortfolios and weblogs: one vision for ePortfolio development. Retrieved from http://www.eradc.org/papers/ePortfolio_Weblog.pdf.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342–365.
Wang, Y., Wang, H., & Shee, D. Y. (2007). Measuring e-learning systems success in an organizational context: Scale development and validation. Computers in Human Behavior, 23(4), 1792-1808.
Wang, W., & Wang, C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761-774.
Wesel, M. V., & Prop, A. (2008, November). The influence of Portfolio media on student perceptions and learning outcomes. Paper presented at Student Mobility and ICT: Can E-LEARNING overcome barriers of Life-Long learning?, Maastricht.
Wu, J., & Wang, Y. (2006). Measuring KMS success: A respecification of the DeLone and McLean''s model. Information & Management, 43(6), 728-739.
Wu, J., Chen, Y., & Lin, L. (2007). Empirical evaluation of the revised end user computing acceptance model. Computers in Human Behavior, 23(1), 162-174.
Yang, Y., Du, Y., Sun, J., & Hai, Y. (2008). A Topic-Specific Web Crawler with Concept Similarity Context Graph Based on FCA. In Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (pp. 840-847). Shanghai, China: Springer-Verlag.