Abstract
The Web holds a great quantity of material that can be used to enhance classroom instruction. However, it is not easy to retrieve this material with the search engines currently available. This study produced a specialized search assistant based on Google that significantly increases the number of instances in which teachers find the desired learning objects as compared to using this popular public search engine directly. Success in finding learning objects by study participants went from 80% using Google alone to 96% when using our search assistant in one scenario and, in another scenario, from a 40% success rate with Google alone to 66% with our assistant. This specialized search assistant implements features such as bilingual search and term suggestion which were requested by teacher participants to help improve their searches. Study participants evaluated the specialized search assistant and found it significantly easier to use and more useful than the popular search engine for the purpose of finding learning objects.
- Belkin, N. J. 2000. Helping people find what they don’t know. Comm. ACM 43, 8, 58--61. Google Scholar
Digital Library
- Belkin, N. J., Kelly, D., Kim, G., Kim, J.-Y., Lee, H.-J., Muresan, G., Tang, M.-C. M., Yuan, X.-J., and Cool, C. 2003. Query length in interactive information retrieval. In Proceedings of SIGIR ACM, 205--212. Google Scholar
Digital Library
- Broisin, J. and Vidal, P. 2006. A management framework to recommend and review learning objects in a web-based learning environment. In Proceedings of the 6th International Conference on Advanced Learning Technologies. 41--42. Google Scholar
Digital Library
- Cacheda, F. and Via, A. 2001. Understanding how people use search engines: A statistical analysis for e-business. In Proceedings of the e-Business and e-Work Conference and Exhibition. pp. 319--325.Google Scholar
- Capra, R., Marchionini, G., Oh, J. S., Stutzman, F., and Zhang, Y. 2007. Effects of structure and interaction style on distinct search tasks. In Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’07). ACM, New York, 442--451. Google Scholar
Digital Library
- Chen, H. and Dumais, S. 2000. Bringing order to the web: Automatically categorizing search results. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’00). ACM, New York, 145--152. Google Scholar
Digital Library
- Chen, J. and Bao, Y. 2009. Cross langage search: The case of google language tools. First Monday 14, 3.Google Scholar
Cross Ref
- Churchill, D. 2007. Towards a useful classification of learning objects. Educat. Tech. Res. Develop. 55, 5, 479--497.Google Scholar
Cross Ref
- Cisco-Systems. 2001. Reusable learning object strategy. Designing information and learning objects through concept, fact, procedure, process, and principle templates. http://www.cisco.com/warp/public/10/wwtraining/elearning/implement/rlo_strategy.pdf (accessed 5/02).Google Scholar
- Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart. 13, 3, 319--339. Google Scholar
Digital Library
- Doorten, M., Giesbers, B., Janssen, J., Daniels, J., and Koper, R. 2004. Transforming Existing Content into Reusable Learning Objects. Routledge, New York, NY, Chapter 9.Google Scholar
- Downes, S. 2004. Learning Objects, Resources for Learning Worldwide. Routledge, New York, NY, Chapter 1.Google Scholar
- Dumais, S., Cutrell, E., and Chen, H. 2001. Optimizing search by showing results in context. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’01). ACM Press, New York, 277--284. Google Scholar
Digital Library
- Dunning, J. 2002. Talon learning object system. http://www.indiana.edu/scstest/jd/learningobjects.html (accessed 4/03).Google Scholar
- Farrell, R. G., Liburd, S. D., and Thomas, J. C. 2004. Dynamic assembly of learning objects. In Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters (WWW Alt.’’04). ACM Press, New York, 162--169. Google Scholar
Digital Library
- Friesen, N. 2001. What are educational objects? Interac. Learn. Environ. 9, 3, 219--230.Google Scholar
Cross Ref
- Graddol, D. 2000. The Future of English? A Guide to Forecasting the Popularity of the English Language in the 21st Century. The British Council.Google Scholar
- Hargittai, E. 2004. Classifying and coding online actions. Soc. Sci. Comput. Rev. 22, 2, 210--227. Google Scholar
Digital Library
- Hassan, S. and Mihalcea, R. 2009. Learning to identify educational materials. In Proceedings of the Conference on Recent Advances in Natural Language Processing (RANLP).Google Scholar
- Hearst, M. A. 2009. Search User Interfaces. Cambridge University Press, Cambridge, UK. Google Scholar
Digital Library
- Hölscher, C. and Strube, G. 2000. Web search behavior of internet experts and newbies. In Proceedings of the 9th International World Wide Web Conference on Computer Networks. North-Holland Publishing Co., Amsterdam, The Netherlands, 337--346. Google Scholar
Digital Library
- Huang, C.-K., Chien, L.-F., and Oyang, Y.-J. 2003. Relevant term suggestion in interactive web search based on contextual information in query session logs. J. Amer. Soc. Inf. Sci. Tech. 54, 7, 638--649. Google Scholar
Digital Library
- IEEE. 2002. IEEE standard for learning object metadata. IEEE standard 1484.12.1. Tech. rep., IEEE.Google Scholar
- Internet World Stats. 2010. http://www.internetworldstats.com/languages.htm (accessed 2/10).Google Scholar
- Jansen, B. J. and Spink, A. 2005. An analysis of web searching by european alltheweb.com users. Inf. Process. Manage. 41, 2, 361--381. Google Scholar
Digital Library
- Jansen, B. J., Spink, A., and Saracevic, T. 2000. Real life, real users, and real needs: A study and analysis of user queries on the web. Inf. Proc. Manage. 36, 2, 207--227. Google Scholar
Digital Library
- Käki, M. 2005a. Findex: Search result categories help users when document ranking fails. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’05). ACM Press, New York, 131--140. Google Scholar
Digital Library
- Käki, M. 2005b. Optimizing the number of search result categories. In Proceeding of Human Factors in Computing Systems (Extended Abstract) (CHI’05). ACM, New York, 1517--1520. Google Scholar
Digital Library
- Keleberda, I., Repka, V., and Biletskiy, Y. 2006. Building learner’s ontologies to assist personalized search of learning objects. In Proceedings of the 8th International Conference on Electronic Commerce (ICEC’06). ACM, New York, 569--573. Google Scholar
Digital Library
- Kelly, D., Dollu, V. D., and Fu, X. 2005. The loquacious user: a document-independent source of terms for query expansion. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’05). ACM, New York, 457--464. Google Scholar
Digital Library
- Kelly, D., Gyllstrom, K., and Bailey, E. W. 2009. A comparison of query and term suggestion features for interactive searching. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’09). ACM, New York, 371--378. Google Scholar
Digital Library
- Kelly, D., Cushing, A., Dostert, M., Niu, X., and Gyllstrom, K. 2010. Effects of popularity and quality on the usage of query suggestions during information search. In Proceedings of the 28th International Conference on Human Factors in Computing Systems (CHI’10). ACM, New York, 45--54. Google Scholar
Digital Library
- Koper, R. 2001. Modelling units of study from a pedagogical perspective: The pedagogical meta-model behind EML. Heerlen. Open University of the Netherlands. http://eml.ou.nl/introduction/docs/pedmetamodel.pdf (accessed 6/02).Google Scholar
- Koper, R. 2003. Combining Reusable Learning Resources and Services with Pedagogical Purposeful Units of Learning. Kogan Page, London, 46--59.Google Scholar
- Koper, R. and van Es, R. 2004. A First Step Towards a Theory of Learning Objects. Routledge, New York, NY, Chapter 3.Google Scholar
- Learning Alberta. 2002. Learn alberta glossary. http://www.learnalberta.ca/l (accessed 4/03).Google Scholar
- Liaw, S.-S. and Huang, H.-M. 2003. An investigation of user attitudes toward search engines as an information retrieval tool. Comput. Hum. Behav. 19, 6, 751--765.Google Scholar
Cross Ref
- Mayer, R. E. and Moreno, R. 2003. Nine ways to reduce cognitive load in multimedia learning. Educa. Psych. 38, 1, 43--52.Google Scholar
- McGreal, R., Ed. 2004. Online Education Using Learning Objects. Routledge, New York, NY. Google Scholar
Digital Library
- Moore, D. S. and McCabe, G. P. 2006. Introduction to the Practice of Statistics. W. H. Freeman and Company.Google Scholar
- Morales, R., Ochoa, X., Sanchez, V. G., and Ordonez, V. 2009. La flor-repositorio latinoamericano de objetos de aprendizaje. In Recursos Digitales para el Aprendizaje. Ediciones de la Universidad Autonoma de Yucatan, 308--317.Google Scholar
- Morris, M., Morris, M. G., and Dillon, A. 1997. The influence of user perceptions on software utilization: Application and evaluation of a theoretical model of technology acceptance. IEEE Softw. 14, 58--76. Google Scholar
Digital Library
- Mortimer, L. 2002. (Learning) objects of desire: Promise and practicality. Learning Circuits, http://www.learningcircuits.org/2002/apr2002/mortimer.html (accessed 6/03).Google Scholar
- Mostafa, J. 2005. Seeking better web searches. Sci. Amer., 66--73.Google Scholar
- Nash, S. S. 2005. Learning objects, learning object repositories, and learning theory: Preliminary best practices for online courses. Interdisciplinary J. Knowl. Learn. Obj. 1.Google Scholar
- Neven, F. and Duval, E. 2002. Reusable learning objects: A survey of lom-based repositories. In Proceedings of the 10th ACM International Conference on Multimedia (MULTIMEDIA’02). ACM, New York, 291--294. Google Scholar
Digital Library
- Ochoa, X. and Duval, E. 2006. Use of contextualized attention metadata for ranking and recommending learning objects. In Proceedings of the 1st International Workshop on Contextualized Attention Metadata: Collecting, Managing and Exploiting of Rich Usage Information (CAMA’06). ACM, New York, 9--16. Google Scholar
Digital Library
- Ortega, B. H., Martinez, J. J., and Hoyos, M. J. M. D. 2007. Aceptacion empresarial de las tecnologias de la informacion y de la comunicacion: Un analisis del sector servicios. J. Inf. Syst. Technol. Manage. 4, 1, 3--22.Google Scholar
- Petrelli, D., Levin, S., Beaulieu, M., and Sanderson, M. 2006. Which user interaction for cross-language information retrieval? Design issues and reflections. J. Amer. Soc. Inf. Sci. Technol. 57, 5, 709--722. Google Scholar
Digital Library
- Polsani, P. R. 2004. A First Step Towards a Theory of Learning Objects. Routledge, New York, NY, Chapter 8.Google Scholar
- Pressman, R. 2006. Software Engineering: A Practitioner’s Approach. McGraw-Hill Science/Engineering/Math. Google Scholar
Digital Library
- Quinn, C. and Hobbs, S. 2000. Learning objects and instructional components. Educational Technology and Society 3, 2.Google Scholar
- Recker, M., Dorward, J., Dawson, D., Halioris, S., Liu, Y., Mao, X., Palmer, B., and Park, J. 2005. You can lead a horse to water: Teacher development and use of digital library resources. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’05). ACM, New York, 1--8. Google Scholar
Digital Library
- Rehak, D., and Mason, R. 2003. Keeping the Learning in Learning Objects. Kogan, London, 20--34.Google Scholar
- Rieger, O. 2009. Search engine use behavior of students and faculty: User perceptions and implications for future research. First Monday 14, 12.Google Scholar
Cross Ref
- Seyedarabi, F. 2006. The missing link how search engines can support the informational needs of teachers. eLearn Mag. Google Scholar
Digital Library
- Si, L. and Callan, J. 2005. Modeling search engine effectiveness for federated search. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’05). ACM, New York, 83--90. Google Scholar
Digital Library
- Sigurbjornsson, B., Kamps, J., and de Rijke, M. 2005. Blueprint of a crosslingual web retrieval collection. In Proceedings of the 5th Dutch-Belgian Information Retrieval Workshop. R. van Zwol Ed. Utrecht University, Center for Content and Knowledge Engineering.Google Scholar
- Skår, L. A., Heiberg, T., and Kongsli, V. 2003. Reuse learning objects through LOM and XML. In Companion of the 18th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA’03). ACM, New York, 78--79. Google Scholar
Digital Library
- Sloep, P. B. 2004. Reuse, Portability and Interoperability of Learning Content. Routledge, New York, NY, Chapter 10.Google Scholar
- Sosteric, M. and Hesemeier, S. 2004. A First Step Towards a Theory of Learning Objects. Routledge, New York, NY, Chapter 2.Google Scholar
- Spink, A., Jansen, B. J., Blakely, C., and Koshman, S. 2006. A study of results overlap and uniqueness among major web search engines. Inf. Proc. Manage. 42, 5, 1379--1391. Google Scholar
Digital Library
- Teevan, J. 2008. How people recall, recognize, and reuse search results. ACM Trans. Inf. Syst. 26, 4, 1--27. Google Scholar
Digital Library
- Teevan, J., Morris, M. R., and Bush, S. 2009. Discovering and using groups to improve personalized search. In Proceedings of the 2nd ACM International Conference on Web Search and Data Mining (WSDM’09). ACM, New York, 15--24. Google Scholar
Digital Library
- Thompson, C., Smarr, J., Nguyen, H., and Manning, C. D. 2003. Finding educational resources on the web: Exploiting automatic extraction of metadata. In Proceedings of ECML Workshop on Adaptive Text Extraction and Mining.Google Scholar
- Walraven, A., Brand-Gruwel, S., and Boshuizen, H. P. 2009. How students evaluate information and sources when searching the world wide web for information. Comput. Educat. 52, 1, 234--246. Google Scholar
Digital Library
- Weller, M., Pegler, C., and Mason, R. 2003. Putting the pieces together: What working with learning objects means for the educator. Elearn International, Edinburgh.Google Scholar
- White, R. W. and Morris, D. 2007. Investigating the querying and browsing behavior of advanced search engine users. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’07). ACM Press, New York, 255--262. Google Scholar
Digital Library
- White, R. W., Richardson, M., Bilenko, M., and Heath, A. P. 2008. Enhancing web search by promoting multiple search engine use. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’08). ACM, New York, 43--50. Google Scholar
Digital Library
- Wieseler, W. 1999. Rio: A standards-based approach for reusable information objects. Cisco Systems. http://www.cisco.com/warp/public/779/ibs/solutions/publishing/whitepapers/ (accessed 5/00).Google Scholar
- Wiley, D. A. 1999. The post-lego learning object. http://wiley.ed.usu.edu/docs/post-lego/ (accessed 4/03).Google Scholar
- Wiley, D. A., Ed. 2001. Instructional Use of Learning Objects. Agency for Instructional Technology. Google Scholar
Digital Library
- Xu, Y. and Mease, D. 2009. Evaluating web search using task completion time. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’09). ACM, New York, 676--677. Google Scholar
Digital Library
Index Terms
A Specialized Search Assistant for Learning Objects
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