Please use this identifier to cite or link to this item:
標題: 以課程資訊分析揭露讀者潛在學習需求可行性及其應用之研究
Uncovering Potential Learning Information Needs Based on Analysis of Course Information: Feasibility Study and Application
作者: 林家陞
Jia-Sheng Lin
關鍵字: 推薦系統
Recommendation System
Information Encountering
Prior Knowledge
Potential Information Needs
引用: Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions On Knowledge And Data Engineering, 17(6), 734-749. Al-Taie, M. Z., & Kadry, S. (2014). Visualization of Explanations in Recommender Systems. Journal of Advanced Management Science, 2(2), 140-144. André, P., Schraefel, M. C., Teevan, J., & Dumais, S. T. (2009). Discovery is never by chance: Designing for (un)serendipity. In N. Bryan-Kinns (Eds.), Proceeding of the Seventh ACM Conference on Creativity and Cognition (pp. 305-314). Berkeley, CA. Antelman, K., Lynema, E., & Pace, A. K. (2006). Toward a twenty-first century library catalog. Information Technology & Libraries, 25(3), 128-139. Babbie, E. (2013). 社會科學研究方法(第十三版,林秀雲譯)。臺北市:新加坡商聖智學習。(原著出版於2012年) Bar-lian, J. (2002). Criteria for evaluating information retrieval systems in highly dynamic environments. In Proceedings of the 2nd International Workshop on Web Dynamics (pp.70-77). Retrieved from Bawden, D., & Robinson, L. (2012). Introduction to information science. London: Facet Publishing. Belkin, N. J. (1978). Information concepts for information science. Journal of Documentation, 34(1), 55-85. Belkin, N. J. (2010). On the evaluation of interactive information retrieval systems. Rutgers University Community Repository. doi: 10.7282/T3SF2TJK Björneborn, L. (2008). Serendipity dimensions and users' information behaviour in the physical library interface. Information Research, 13(1). Retrieved from Borgman, C. L. (1997). Multi-media, multi-cultural, and multi-lingual digital libraries: Or how do we exchange data in 400 languages? Retrieved from Breeding, M. (2009). Cloud discovery services for libraries. In 2009 Annual ASERL Membership Meeting. Retrieved from Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In G. F. Cooper, & S. Moral (Eds.), Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (pp. 43-52). Wisconsin: Madison. Buder, J., Schwind, C. (2012). Learning with personalized recommender systems: A psychological view. Computers in Human Behavior, 28(1), 207-216. Callison, D. (1997). Evolution of methods to measure student information use. Library and Information Science Research, 19(4), 347-357. Campos, J., & de Figueiredo, A. D. (2001). Searching the Unsearchable: Inducing Serendipitous Insights. In R. Weber, & C. Gresse (Eds.), Proceedings of the Workshop Program at the Fourth International Conference on Case-Based Reasoning, ICCBR 2001, Technical Note AIC-01-003. Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence. Case, D. O. (2012). Looking for information: A servey of research on information seeking, needs, and behavior (3rd ed.). Boston: Academic Press. Chen, Z., Jiang, Y., & Zhao, Y. (2010). A collaborative filtering recommendation algorithm based on user interest change and trust evaluation. International Journal of Digital Content Technology and its Applications, 4(9), 106-113. Connaway, L. S., & Randall, K. M. (2013). Why the Internet is more attractive than the library. Serials Librarian, 64(1-4), 41-56. doi: 10.1080/0361526X.2013.761053 Cooper, W. S. (1973). On selecting a measure of retrieval effectiveness. Journal of the American Society for Information Science, 24(2), 87-100. doi: 10.1002/asi.4630240204 Cunha, M. P. E. (2005). Serendipity: Why some organizations are luckier than others. Lissabon, Portugal: Faculdade de Economia, Universidade Nova de Lisboa. DeLone, W. H., & McLean, E. R. (1992). Information system success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95. Dervin, B. (1992). From the mind's eye of the user: The sense-making qualitative-quantiatative methodology. In J. D. Glazer & R. R. Powell (Eds.), Qualitative Research in Information Management (pp. 61-84). Englewood: Libraries Unlimited. Doll, W. J., & Torkzadeh, G. (1988). The measurement of end-user computing satisfaction. MIS Quarterly, 12(2), 259-274. Erdelez, S. (1995). Information encountering: An exploration beyond information seeking (Unpublished doctoral dissertation). University of Syracuse, New York. Erdelez, S. (1996). Information encountering on the Internet. In M. Williams (Ed.), Proceedings of the 17th National Online Meeting (pp. 101-107). Medford, NJ: Information Today. Erdelez, S. (1997). Information encountering: A conceptual framework for accidental information discovery. In P. Vakkari, R. Savolainen, & B. Dervin (Eds.), Information Seeking in Context: Proceedings of an International Conference on Research in Information Needs, Seeking and Use in Different Contexts (pp. 412-421). London: Taylor Graham. Erdelez, S. (1999). Information encountering: It's more than just bumping into information. Bulletin of the American Society for Information Science and Technology, 25, 26-29. Erdelez, S. (2000). Towards understanding information encountering on the web. In Proceedings of the 63rd annual meeting of the American Society for information science (pp. 363-371). Medford, NJ: Information Today. Erdelez, S. (2004). Investigation of information encountering in the controlled research environment. Information Processing and Management, 40(6), 1013-1025. Erdelez, S. (2005). Information encountering. In K. E. Fisher, S. Erdelez, & L. E. F. McKechnie (Eds.), Theories of Information Behavior (pp. 179-184). Medford, NJ: Information Today. Erdelez, S., Basic, J., & Levitov, D. D. (2011). Potential for inclusion of information encountering within information literacy models. Information Research, 16(3). Retrieved from Ferguson, S., Hider, P., & Kelly, T. (2005). Information systems evaluation and the search for success: Lessons for LIS research. The Australian Library Journal, 54(3), 245-256. doi:10.1080/00049670.2005.10721762 Foster, A., & Ford, N. (2003). Serendipity and information seeking: An empirical study. Journal of Documentation, 59(3), 321-340. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70. Griffiths, J. R., Johnson, F., & Hartley, R. J. (2007). User satisfaction as a measure of system performance. Journal of Librarianship and Information Science, 39(3), 142-152. doi: 10.1177/0961000607080417 Heinström, J. (2006). Psychological factors behind incidental information acquisition. Library & Information Science Research, 28(4), 579-594. Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) TOIS Homepage archive, 22(1), 5-53. Hessel, H., & Fransen, J. (2012). Resource discovery: Comparative results on two catalog interfaces. Information Technology and Libraries, 31(2), 21-44. doi: 10.6017/ital.v31i2.2165 Hildreth, C. R. (1987). Beyond boolean: Designing the next generation of online cata1ogs. Library Trends, 35, 647-667. Hildreth, C. R. (2001). Accounting for users' inflated assessments of on-line catalogue search performance and usefulness: An experimental study. Information Research, 6(2). Retrieved from Isinkaye, F. O., Folajimi Y. O., Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273. ISO 9241-11 (1998). Ergonomic requirements for office work with visual display terminals (VDTs) — Part 11: Guidance on usability. Retrieved from Kelly, D. (2009). Methods for evaluating interactive information retrieval systems with users. Foundations and Trends in Information Retrieval, 3(1-2), 1-224. doi: 10.1561/1500000012 Kim, H. H. (2005). ONTOWEB: Implementing an ontology-based web retrieval system. Journal of the American Society for Information Science and Technology, 56(11), 1167-1176. doi: 10.1002/asi.20220 Konstan, J. A., & Riedl, J. (2012). Recommender systems: From algorithms to user experience. User Model User-Adap Inter, 22, 101-123. Kuhlthau, C. C. (1991). Inside the search process: Information seeking from the user's perspective. Journal of the American Society for Information Science, 42(5), 361-371. Lawley, J., & Tompkins, P. (2008). Maximising serendipity: The art of recognising and fostering potential-A systemic approach to change. Retrieved from & Maximising-Serendipity/Page2.html Lederman, S. (2009, July 19). Discovering discovery services [Web log post]. Retrieved from discovering-discovery-services/ Lesk, M. (1996). The seven ages of information retrieval. In International Federation of Library Associations and Institutions Occasional Paper, 5 (pp. 1-16). Retrieved from Li, Q., & Kim, B. M. (2003). Clustering approach for hybrid recommender system. Proceedings IEEE/WIC International Conference on Web Intelligence. Liestman, D. (1992). Chance in the midst of design: Approaches to library research serendipity. Reference & User Service Quarterly, 31(4), 524-532. Lu, Chi-Jung. (2012). Accidental discovery of information on the user-defined social web: A mixed-method study (Unpublished doctoral dissertation). University of Pittsburgh, Pennsylvania. Mahmood, M. A., Burn, J. M., Gemoets, L. A., & Jacquez, C. (2000). Variables affecting information technology end-user satisfaction: A meta-analysis of the empirical literature. International Journal of Human-Computer Studies, 52(4), 751-771. Majors, R. (2012). Comparative user experiences of next-generation catalogue interfaces. Library Trends, 61(1), 186-207. doi: 10.1353/lib.2012.0029 Makri, S., & Blandford, A. (2012a). Coming across information serendipitously – Part 1: A process model. Journal of Documentation, 68(5), 684-705. Makri, S., & Blandford, A. (2012b). Coming across information serendipitously – Part 2: A classification framework. Journal of Documentation, 68(5), 706-724. Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to information retrieval. New York: Cambridge. Manzari, L., & Trinidad-Christensen, J. (2006). User-centered design of a web site for library and information science students: Heuristic evaluation and usability testing. Information Technology and Libraries, 25(3), 163-169. Massa, P., & Bhattacharjee, B. (2004). Using trust in recommender systems: An experimental analysis. In C. Jensen, S. Poslad, & T. Dimitrakos (Eds.), Trust Management. iTrust 2004. Lecture Notes in Computer Science, 2995(pp. 221-235). Berlin, Heidelberg: Springer. McCay-Peet, L., & Toms, E. G. (2010). The process of serendipity in knowledge work. In Proceeding of the Third Symposium on Information Interaction in Context (pp. 377-382). New Brunswick, NJ: ACM. McCay-Peet, L., & Toms, E. G. (2011). Measuring the dimensions of serendipity in digital environments. Information Research, 16(3). Retrieved from Mick, C. K., Lindsey, G. N., & Callahan, D. (1980). Toward usable user studies. Journal of the American Society for Information Science, 31(5), 347-356. Miwa, M. (2000). Use of human intermediation in information problem solving: A user's perspective. Washington, D.C.: ERIC Clearinghouse on Information & Technology. Miwa, M., Egusa, Y., Saito, H., Takaku, M., Terai, H., & Kando, N. (2011). A method to capture information encountering embedded in exploratory web searches. Information Research, 16(3). Retrieved from Nagy, A. (2011). Analyzing the next-generation catalog. Library Technology Reports, 47(7), 5-27. Nielson, J. (1993). Usability engineering. Boston: Academic Press. Pálsdóttir, Á. (2010). The connection between purposive information seeking and information encountering: A study of Icelanders' health and lifestyle information seeking. Journal of Documentation, 66(2), 224-244. Qiu, Y., & Frei, H.P. (1993). Concept Based Query Expansion. In Proceedings of the 16th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Pittsburgh (pp.160-169). New York, NY. doi: 10.1145/160688.160713 Ramakrishnan, N. Keller, B. J., Mirza, B. J., Grama, A. Y., Karypis, G. (2002). When being weak is brave: Privacy in recommender systems. IEEE Internet Computing, 2002, 1-12 Reneker, M. (1992). Information seeking among members of an academic community (Unpublished doctoral dissertation). Columbia University, New York. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J.(1994). GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work (pp. 175-186). ACM, New York (1994) Ricci, F., Rokach, L., & Shapira, B. (2011). Recommender Systems Handbook. US: Springer Rubin, R. E. (2010). Redefining the library: The impacts and implications of technological change. Foundations of Library and Information Science (3rd ed.) (pp.225-270). NY: Neal-Schuman Publishers, Inc. Sadeh, T. (2004). The challenge of metasearching. New Library World, 105, 104-112. Sakagami, H., & Kamba, T.(1997). Learning personal preferences on online newspaper articles from user behaviors. Computer Networks and ISDN Systems, 29(8-13), 1447-1455. doi: 10.1016/S0169-7552(97)00016-0 Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management. 24(5), 513-523. Sampaio, S. de F. M., Dong, C., & Sampaio, P. R. F. (2005). Incorporating the timeliness quality dimension in internet query systems. In Web Information Systems Engineering – WISE 2005 Workshops Volume 3807 of the series Lecture Notes in Computer Science (pp. 53-62). Sanderson, M., & Croft, W. B. (2012). The history of information retrieval research. Proceedings of the IEEE, 100, 1444-1451. doi: 10.1109/JPROC.2012.2189916 Schafer, J. B., Konstan, J. A., Riedl, J. (2001). E-Commerce recommendation applications. In R. Kohavi, F. Provost(Eds.) Applications of Data Mining to Electronic Commerce (pp. 115-153). US: Springer. doi: 10.1007/978-1-4615-1627-9_6 Srinivasan, A. (1985). Alternative measures of system effectiveness: Associations and implications. MIS Quarterly, 9(3), 243-253. Stewart, K. N., & Basic, J. (2014). Information encountering and management in information literacy instruction of undergraduate, students. International Journal of Information Management, 34(2), 74-79. Su, L.T. (2003a). A comprehensive and systematic model of user evaluation of web search engines: II. An evaluation by undergraduates. Journal of the American Society for Information Science and Technology, 54(13), 1193-1223. doi: 10.1002/asi.10304 Su, L.T. (2003b). A comprehensive and systematic model of user evaluation of web search engines: I. Theory and background. Journal of the American Society for Information Science and Technology, 54(13), 1175-1192. doi: 10.1002/asi.10303 Sun, X., Sharples, S., & Makri, S. (2011). A user-centred mobile diary study approach to understanding serendipity in information research. Information Research, 16(3). Retrieved from Taylor, A. G., & Joudrey, D. N. (2009). System and system design. The Organization of Information (3rd ed.)(pp.159-198). Westport, CN: Libraries Unlimited. Text REtrieval Conference (2013). Common Evaluation Measures. In E. M. Voorhees (Ed.) The Twenty-Second Text REtrieval Conference (TREC 2013) Proceedings. Retrieved from Toms, E. G. (1998). Information exploration of the third kind: The concept of chance encounters. CHI 98Workshop on Innovation and Evaluation in Information Exploration Interfaces. Toms, E. G. (2000a). Serendipitous information retrieval. Proceedings of the First DELOS Network of Excellence Workshop on Information Seeking, Searching and Querying in Digital Libraries (pp.11-12). Retrieved from Toms, E. G. (2000b). Understanding and facilitating the browsing of electronic text. International Journal of Human-Computer Studies, 52(3), 423-452. Toms, E. G., & McCay-Peet, L. (2009). Chance encounters in the digital library. In M. Agosti, J. Borbinha, S. Kapidakis, C. Papatheodorou, & G. Tsakonas (Eds), Research and Advanced Technology for Digital Libraries: 13th European Conference, ECDL 2009, Corfu, Greece, September 27 - October 2, 2009. Proceedings (pp. 192-202). New York: Springer. Vakkari, P. (1997). Information seeking in context: A challenging metatheory. In P. Vakkari, R. Savolainen, & B. Dervin (Eds.), Proceedings of an International Conference on Research in Information Needs, Seeking and Use in Different Contexts (pp. 451-464). London: Taylor Graham. Vakkari, P. (1999). Task complexity, problem structure and information actions: Integrating studies on information seeking and retrieval. Information Processing & Management, 35(6), 819-837. Verbert, K., Parra, D., Brusilovsky, P., & Duval, E. (2013).Visualizing recommendations to support exploration, transparency and controllability. IUI '13 companion: Proceedings of the companion publication of the 2013 international conference on intelligent user interfaces companion (pp. 351-362), New York, NY: ACM. Voorhees, E. M. (1999). TREC-8 Question Answering Track Report. In E. M. Voorhees (Ed.) Proceedings of the 8th Text Retrieval Conference (pp. 77-82). Retrieved from Wakeling, S., Clough, P., Sen, B., & Connaway, L. S. (2012). 'Readers who borrowed this also borrowed … ': Recommender systems in UK libraries'. Library Hi Tech, 30(1), 134-150. doi: 10.1108/07378831211213265 Watson, E.A. (2008). Going fishing: Serendipity in library and information science (Unpublished master thesis). University of North Carolina, Chapel Hill, NC. Webster, J., Jung, S., & Herlocker, J. (2004). Collaborative filtering: A new approach to searching digital libraries. New Review of Information Networking, 10(2), 177-191. Weng, S. S., & Liu, M. J. (2004). Feature-based Recommendation for One-to-One Marketing. Expert Systems with Applications, 26(4), 493-508. White, R. W., Ruthven, I., & Jose, J. M. (2002). The use of implicit evidence for relevance feedback in web retrieval. In Advances in Information Retrieval Volume 2291 of the series Lecture Notes in Computer Science (pp.93-109). Springer Berlin Heidelberg. White, R. W., Ruthven, I., & Jose, J. M. (2005). A study of factors affecting the utility of implicit relevance feedback. In Proceeding SIGIR '05 Proceedings of the 28th annual international ACM SIGIR conference on Research and Development in Information Retrieval (pp.35-42). New York: ACM. Williamson, K. (1998). Discovered by chance: The role of incidental information acquisition in an ecological model of information use. Library & Information Science Research, 20(1), 23-40. Wilson, T. D. (1999). Models in information behavior research. Journal of Documentation, 55(3), 249-279. Wilson, T. D. (2010). Fifty years of information behavior research. Bulletin of the American Society for Information Science and Technology, 36, 27-34. doi: 10.1002/bult.2010.1720360308 Xu, Y. (2017). Fluid Interactive Information Visualization: A Visualization Tool for Book Recommendation. Retrieved from Yager, R. R. (2003). Fuzzy logic methods in recommender systems. Fuzzy Sets and Systems, 136(2), 133-149. Yi, M. (2008). Information organization and retrieval using a topic maps-based ontology: Results of a task-based evaluation. Journal of the American Society for Information Science and Technology, 59(12), 1898-1911. doi: 10.1002/asi.20899 Zhang, X. (1992). Information-seeking patterns and behavior of selected undergraduate students in a Chinese university (Unpublished doctoral dissertation). Columbia University, New York. Zuccon, G., & Koopman, B. (2014). Integrating understandability in the evaluation of consumer health search engines. In Medical Information Retrieval (MedIR) Workshop, 11 July 2014. Australia: Gold Coast. Retrieved from 卜小蝶 (民85)。圖書資訊檢索技術。臺北市:文華圖書館管理。 卜小蝶、江信昱 (民103)。網路學術資訊尋獲與再尋獲之檢索行為探析。圖書資訊學刊,12(2),117-160。doi:10.6182/jlis.2014.12(2).117 王梅玲、謝寶煖 (2014)。圖書館自動化與資訊系統。圖書資訊學導論 (第二版,頁315-340)。臺北市:五南圖書。 王喜沙 (民82)。線上公用目錄評估及研究方法之探討。政大圖資通訊,5,32-46。 江玉婷、陳光華 (1999)。TREC現況及其對資訊檢索研究之影響。政治大學圖書與資訊學刊,29,36-59。 余明哲 (民92)。圖書館個人化館藏推薦系統(未出版碩士論文)。國立交通大學資訊科學系,新竹市。 吳政叡 (2002)。都柏林核心集使用者查詢行為調查:以施合鄭基金會為例。國家圖書館館刊,91(1),19-27。 李宜容 (民86)。探討讀者使用線上公用目錄檢索點及主題檢索之情形。圖書與資訊學刊,22,39-55。 李靜宜、柯皓仁 (2012)。電子資源整合查詢系統使用者接受度與使用行為之研究。教育資料與圖書館學,49(3),369-404。 林妙樺 (2006)。數位典藏系統入口網站界面可用性評估模式之探討。大學圖書館,10(2),160-182。 林佳穎、吳明德 (民100)。圖書館電子資源整合查詢系統之好用性評估:以國立臺灣大學圖書館MUSE電子資源整合查詢系統為例。大學圖書館,15(2),1-18。 林孟真 (民85)。圖書館自動化的發展。圖書館自動化之理論與實務 (頁97-202)。臺北市:五南。 林雯瑤 (2006)。層面分類的概念與應用。教育資料與圖書館學,44(2),153-171。 姜義臺 (民98)。圖書館電子資源整合檢索系統優使性之研究:以SmartWeaver為例 (未出版碩士論文)。國立中興大學圖書資訊學研究所,臺中市。 柯皓仁 (2013)。資源探索服務之功能評估。國立成功大學圖書館館刊,22,1-16。 范清詠 (2009)。視覺化的數位典藏檢索介面之研究 (未出版碩士論文)。臺中技術學院多媒體設計系碩士班,臺中市。 唐牧群、洪承理 (2012)。評估以MeSH做為PubMed資料庫搜尋之建議詞彙的有效性檢索行為研究。教育資料與圖書館學,49(3),405-445。 徐枚伶 (2014)。大學院校學生使用Google Scholar之意願-以臺灣師範大學為例 (未出版碩士論文)。臺灣大學圖書資訊學研究所,臺北市。 徐芬春 (1995)。淺析線上公用目錄檢索技術的歷史演進。圖書與資訊學刊,15,30-41。 張瑞珊 (民104)。多語使用者搜尋外文資訊之需求與尋求情境研究 (未出版碩士論文)。國立中興大學圖書資訊學研究所,臺中市。 張慧銖 (2011)。圖書館電子資源組織-從書架到網路。新北市:Airiti Press。 張慧銖 (民92)。線上公用目錄之發展。圖書館目錄發展研究 (頁247-320)。臺北市:文華圖書管理。 曹淑娟、蔡季軒、王振勳 (2013)。大學生使用電子資料庫頻率相關影響因素之研究:以朝陽科技大學為例。朝陽人文社會學刊,11(2),頁87-122。 郭俊桔、張瑞珊、張育蓉 (2013)。導入矩陣分群之視覺化圖書推薦系統。教育資料與圖書館學,51(1),5-35。 陳玉樹、姜雅玲 (2013)。問卷調查法。在蔡清田(主編),社會科學研究方法新論 (頁1-26)。臺北市:五南。 陳光華 (2004)。資訊檢索的績效評估。在2004年現代資訊組織與檢索研討會 (頁125-139)。臺北縣。 陳光華、莊雅蓁 (2001)。資訊檢索之中文詞彙擴展。資訊傳播與圖書館學,8(1),59-75。 陳向明 (2002)。社會科學質的研究。臺北市:五南。 陳和琴、張慧銖、江綉瑛、陳昭珍 (民92)。書目資料庫之建立與維護。資訊組織 (頁253-296)。臺北縣:空大。 陳芷瑛 (民93)。SFX:新一代的電子資源整合技術。國立中央大學圖書館通訊,39。檢自: 陳臻 (2009)。從使用者探討線上公用目錄設計原則之研究:以臺北科技大學為例 (未出版碩士論文)。國立政治大學圖書資訊與檔案學研究所,臺北市。 彭彥綸 (2008)。運用內容聯合概念於資訊搜尋結果之呈現 (未出版碩士論文)。大同大學資訊經營學研究所,臺中市。 曾元顯 (1997)。新一代資訊檢索技術在圖書館OPAC系統的應用。大學圖書館,1(3),82-93。 曾元顯 (民86)。關鍵詞自動擷取技術與相關詞回饋。中國圖書館學會會報,59,59-64。 曾昱嫥 (民104)。大學圖書館提供電子書取用管道與使用者工作任務之關係 (未出版碩士論文)。國立中興大學圖書資訊學研究所,臺中市。 曾繁絹、李宗翰 (2008)。圖書館電子資源整合查詢系統評估之研究。圖書資訊學刊,6(1/2),111-142。 粟村 倫久 (2006)。情報遭遇に関する利用者行動モデルの再検討:ウェブ上の情報遭遇に対する調査。Library and Information Science, 55, 47-69。 黃世雄、黃鴻珠、宋雪芳 (民89)。圖書館自動化系統與網路。圖書館網路與資源運用 (頁1-34)。臺北縣:空大。 黃慕萱 (民85)。資訊檢索中「相關」概念之研究。臺北市:台灣學生。 楊心瑜、林麗娟 (2010)。歷史研究生使用臺灣歷史學門相關數位典藏研究。圖書與資訊學刊,74,53-74。 楊世瑩 (2009)。信度。SPSS統計分析即學即用 (頁14-2)。臺北縣:碁峰。 葉乃嘉 (2013)。研究方法的第一本書:教育、人文與社會科學研究的入門書。臺北市:五南。 詹盛如 (2013)。個案研究法。在蔡清田(主編),社會科學研究方法新論 (頁1-26)。臺北市:五南。 蔡怡欣 (2010)。線上資訊偶遇經驗與個人特徵之研究 (未出版碩士論文)。天主教輔仁大學圖書資訊學系碩士班,新北市。 蔡明月 (民80)。線上檢索系統發展過程。線上資訊檢索:理論與應用 (頁21-36)。臺北市:台灣學生。 蔡維君 (2006)。大學圖書館網站好用性評估:以臺灣大學圖書館網站為例 (未出版碩士論文)。臺灣大學圖書資訊學研究所,臺北市。 戴玉旻、陳莉君、偌弗.卡克拉爾、柯皓仁 (2001)。台灣電子期刊使用者行為分析:以Elsevier SDOS電子期刊系統為例。圖書與資訊學刊,39,1-27。doi: 10.6575/JoLIS 薛鈺蓉 (2010)。SARS-結合朋友主動資訊之推薦機制(未出版碩士論文)。國立臺灣大學資訊管理學研究所,臺北市。 謝吉隆、沈柏辰、楊立偉 (2015)。輔助新聞檢索之視覺化介面實作與研究參與者評估。圖書資訊學研究,9(2),149-189。
摘要: 在資訊尋求行為研究中大部分研究多探討使用者主動找尋資訊的情境,關於使用者被動接收資訊的情境探討則相對較少,而關於使用者被動接收資訊的情形,在過去研究中則提出了資訊偶遇的概念,在其概念中,指出使用者會偶然地接觸到有用資源,也指出使用者其實具有多樣的潛在資訊需求,以及使用者個人本身具備的先備知識能幫助使用者辨識資源,判斷資源的有用性或幫助程度。 本研究嘗試以課程相關資源為推薦項目,透過分析學校系統中既有的課程相關資訊,以及配合圖書館館藏紀錄,以圖書館館藏查詢平台為基底,在其上建置推薦系統的方式,嘗試揭露大學生與課程修課相關的潛在學習及資訊需求,並透過推薦系統推薦可能幫助解決前述資訊需求的資源,邀請大學生進行推薦系統的操作,配合問卷、訪談等方式進行資料蒐集,探討透過既有之課程資料的分析,嘗試揭露大學生的修課資訊需求,並探討大學生個人先備知識對於資源判斷可能產生的影響,以及驗證建置相應推薦系統之可行性。 研究發現使用者的先備知識會影響其對於推薦之資源的判斷,幫助使用者辨識不同主題範圍之資源與課程的關聯性,當使用者具備修課經驗時,會傾向將推薦之資源與實際修課內容進行比較;而當使用者不具備修課經驗時,則多傾向分析資源與課程之間主題範圍的差異來進行比較,同時,當使用者具備修課經驗時會相對容易辨識及判斷推薦之資源對課程可能的具體幫助;另一方面,無論使用者是否具備修課經驗,都會傾向取用容易理解,或是主題難易度較淺的資源項目,以及使用者會對偏實務應用或個案探討相關的資源較感興趣。本研究建置之推薦系統在面對不同科系的使用者時都能夠推薦出使用者認為是有幫助的資源,驗證本研究分析課程資訊進行推薦的系統建置方向確實有其發展可行性,最後研究亦發現當系統採用圖像式的資源呈現方式時,確實能在第一時間吸引到參與者的注意。 根據研究結果提出對未來推薦系統建置時的建議包含未來可以嘗試考量個人先備知識對資源判斷的影響,融入與課程有關的資源推薦,並且建置以圖像呈現為主的推薦系統介面,吸引使用者注意及興趣,以及在資源呈現上嘗試增加相關資訊的說明或提示,幫助使用者更容易辨識資源對自己的可能幫助;對圖書館與教師的建議則包含未來可以盡可能增加館藏的曝光,以及讓對使用者修課可能有幫助的資源能更容易被教師應用於課程授課上。
In the research of information seeking behavior, most of the researches explored the situation in which users actively seek information. The situation of passively receiving information was relatively less, and there was the concept of this situation has been proposed in the past research, called 'Information Encountering', which pointed out that users will access useful resources by serendipitly, and it also pointed out that users have a variety of potential information needs, and the user's own prior knowledge can help users identify resources and determine the usefulness or helpness of resources. This study attempts to use the curriculum-related resources as a recommended project. By analyzing the existing curriculum information in the school system and matching the library collection records, the library collection inquiry platform is used as the basis, and the recommendation system is built on it. Uncovering the potential learning and information needs of college students related to curriculum courses, and recommending resources to help solve the above information needs through the recommendation system, inviting college students to use the recommendation system, and collecting data through questionnaires, interviews, etc. The analysis of the course materials attempts to expose the information needs of college students, and explores the possible impact of college students' prior knowledge on resource judgment and the feasibility of constructing the corresponding recommendation system. The study found that users' prior knowledge affects their judgment on recommended resources, and helps users identify the relevance of resources and courses in different subject areas. When users have experience of the course, they will tend to compare the difference of the recommended resources and course content; when the user does not have the experience of the course, the tendency is to analyze the difference between the subject and the scope of the course to compare, and at the same time, when the user has the experience of the course, it is relatively easy for users to identify and judge the usefulness, and the practical help to the course. On the other hand, regardless of whether the user has the experience of the course, they will tend to use resource items that are easy to understand, or that are not difficult to use, and that users will apply to the practice or Case studies on relevant resources are more interesting. The recommendation system built in this study can recommend resources that users think are helpful when facing users of different departments, and verify the system to analyze the course information for recommendation. The direction of construction does have its development feasibility. Finally, the research also found that when the system adopts Mind map like presentation, indeed able to attract the attention of the participants at the first time. According to the research results, the suggestions for the future recommendation system construction include the future consideration of the impact of personal prior knowledge on resource judgment, the integration of resource recommendations related to the curriculum, and the establishment of a recommendation system interface based on image presentation for attracting users' interest and interest, as well as explanations or tips on trying to increase the information on the resource presentation, to help users more easily identify the possible helpness of resources; the recommendations for libraries and teachers include the possibility of increasing the exposure of collection as much as possible in the future, as well as resources that may be helpful to the user, can be more easily applied by the teacher to the course.
文章公開時間: 2018-08-23
Appears in Collections:圖書資訊學研究所



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.