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Retrieval of Educational Resources from the Web: A Comparison Between Google and Online Educational Repositories

  • Carlo De Medio
  • Carla LimongelliEmail author
  • Alessandro Marani
  • Davide Taibi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11841)

Abstract

The retrieval and composition of educational material are topics that attract many studies from the field of Information Retrieval and Artificial Intelligence. The Web is gradually gaining popularity among teachers and students as a source of learning resources. This transition is, however, facing skepticism from some scholars in the field of education. The main concern is about the quality and reliability of the teaching on the Web. While online educational repositories are explicitly built for educational purposes by competent teachers, web pages are designed and created for offering different services, not only education. In this study, we analyse if the Internet is a good source of teaching material compared to the currently available repositories in education. Using a collection of 50 queries related to educational topics, we compare how many useful learning resources a teacher can retrieve in Google and three popular learning object repositories. The results are very insightful and in favour of Google supported by the t-tests. For most of the queries, Google retrieves a larger number of useful web pages than the repositories (\(p < .01\)), and no queries resulted in zero useful items. Instead, the repositories struggle to find even one relevant material for many queries. This study is clear evidence that even though the repositories offer a richer description of the learning resources through metadata, it is time to undertake more research towards the retrieval of web pages for educational applications.

Keywords

Web search Information retrieval for education Technology Enhanced Learning 

References

  1. 1.
    Learning object metadata, IEEE-LTSC. https://www.ieeeltsc.org/working-groups/wg12LOM/lomDescription/. Accessed 14 Feb 2019
  2. 2.
    Allan, J., Croft, B., Moffat, A., Sanderson, M.: Frontiers, challenges, and opportunities for information retrieval: report from swirl 2012 the second strategic workshop on information retrieval in Lorne. SIGIR Forum 46(1), 2–32 (2012)CrossRefGoogle Scholar
  3. 3.
    Bozo, J., Alarcón, R., Iribarra, S.: Recommending learning objects according to a teachers’ contex model. In: Wolpers, M., Kirschner, P.A., Scheffel, M., Lindstaedt, S., Dimitrova, V. (eds.) EC-TEL 2010. LNCS, vol. 6383, pp. 470–475. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-16020-2_39CrossRefGoogle Scholar
  4. 4.
    Curlango-Rosas, C., Ponce, G.A., Lopez-Morteo, G.A.: A specialized search assistant for learning objects. ACM Trans. Web 5(4), 21:1–21:29 (2011)CrossRefGoogle Scholar
  5. 5.
    Dietze, S., Yu, H.Q., Giordano, D., Kaldoudi, E., Dovrolis, N., Taibi, D.: Linked education: interlinking educational resources and the web of data. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 366–371. ACM (2012)Google Scholar
  6. 6.
    Drachsler, H., Verbert, K., Santos, O.C., Manouselis, N.: Panorama of recommender systems to support learning. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 421–451. Springer, Boston, MA (2015).  https://doi.org/10.1007/978-1-4899-7637-6_12CrossRefGoogle Scholar
  7. 7.
    Learning Technology Standards Committee: IEEE standard for learning object metadata. IEEE standard, 1484(1), 2007-04 (2002) Google Scholar
  8. 8.
    Estivill-Castro, V., Limongelli, C., Lombardi, M., Marani, A.: DAJEE: a dataset of joint educational entities for information retrieval in technology enhanced learning. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 681–684. ACM (2016)Google Scholar
  9. 9.
    Estivill-Castro, V., Lombardi, M., Marani, A.: Improving binary classification of web pages using an ensemble of feature selection algorithms. In: Proceedings of the Australasian Computer Science Week Multiconference, p. 17. ACM (2018)Google Scholar
  10. 10.
    Krieger, K.: Creating learning material from web resources. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 721–730. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-18818-8_45CrossRefGoogle Scholar
  11. 11.
    Limongelli, C., Lombardi, M., Marani, A.: Towards the recommendation of resources in coursera. In: Intelligent Tutoring Systems: 13th International Conference, ITS 2016, 7–10 June 2016, Zagreb, Croatia, Proceedings, vol. 9684, p. 461. Springer, Heidelberg (2016)Google Scholar
  12. 12.
    Limongelli, C., Lombardi, M., Marani, A., Taibi, D.: Enrichment of the dataset of joint educational entities with the web of data. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), pp. 528–529. IEEE (2017)Google Scholar
  13. 13.
    Lombardi, M., Marani, A.: A comparative framework to evaluate recommender systems in technology enhanced learning: a case study. In: Lagunas, O.P., Alcántara, O.H., Figueroa, G.A. (eds.) MICAI 2015. LNCS (LNAI), vol. 9414, pp. 155–170. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27101-9_11CrossRefGoogle Scholar
  14. 14.
    Maloney, S., Moss, A., Keating, J., Kotsanas, G., Morgan, P.: Sharing teaching and learning resources: perceptions of a university’s faculty members. Med. Educ. 47(8), 811–819 (2013)CrossRefGoogle Scholar
  15. 15.
    Mao, J., et al.: When does relevance mean usefulness and user satisfaction in web search? In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016, pp. 463–472. ACM, New York (2016)Google Scholar
  16. 16.
    Marani, A.: WebEduRank: an educational ranking principle of web pages for teaching. Ph.D. thesis, School of Information and Communication Technology, Griffith University (2018)Google Scholar
  17. 17.
    Ochoa, X., Klerkx, J., Vandeputte, B., Duval, E.: On the use of learning object metadata: the GLOBE experience. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds.) EC-TEL 2011. LNCS, vol. 6964, pp. 271–284. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23985-4_22CrossRefGoogle Scholar
  18. 18.
    Palavitsinis, N., Manouselis, N., Sanchez-Alonso, S.: Metadata quality in learning object repositories: a case study. Electron. Libr. 32(1), 62–82 (2014)CrossRefGoogle Scholar
  19. 19.
    Rehak, D., Mason, R.: Engaging with the Learning Object Economy, pp. 22–30 (2003)Google Scholar
  20. 20.
    Tumer, D., Shah, M.A., Bitirim, Y.: An empirical evaluation on semantic search performance of keyword-based and semantic search engines: Google, Yahoo, Msn and Hakia. In: Fourth International Conference on Internet Monitoring and Protection, 2009. ICIMP 2009, pp. 51–55. IEEE (2009)Google Scholar
  21. 21.
    Wiley, D.: Connecting learning objects to instructional design theory: a definition, a metaphor, and a taxonomy. Learn. Technol. 2830, 1–35 (2001)Google Scholar
  22. 22.
    Yu, H.Q., et al.: A linked data-driven & service-oriented architecture for sharing educational resources. In: Linked Learning 2011: the 1st International Workshop on eLearning Approaches for the Linked Data Age. ESCWC 2011 (2011). http://oro.open.ac.uk/28856/

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Carlo De Medio
    • 1
  • Carla Limongelli
    • 1
    Email author
  • Alessandro Marani
    • 2
  • Davide Taibi
    • 3
  1. 1.Engineering DepartmentRoma Tre UniversityRomeItaly
  2. 2.School of Information and Communication TechnologyGriffith UniversityBrisbaneAustralia
  3. 3.Italian National Research Council, Institute for Educational TechnologiesPalermoItaly

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