Abstract
More than a decade has passed since the start of the MIT OCW initiative, which, along with other similar projects, has been expected to change dramatically the educational paradigms worldwide. However, better findability is still expected for open educational resources and open courseware, so online guidance and services that support users to locate the appropriate such resources are most welcome. Recommender systems have a very valuable role in this direction. We propose here a hybrid architecture that combines enhanced case-based recommending (driven by a quality model tenet) with (collaborative) feedback from users to recommend open courseware and educational resources.
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Kortemeyer, G.: Ten Years Later: Why Open Educational Resources Have Not Noticeably Affected Higher Education, and Why We Should Care, Educase Review online, http://www.educause.edu/ero/article/ten-years-later-why-open-educational-resources-have-not-noticeably-affected-higher-education-and-why-we-should-ca
Vladoiu, M.: Quality Criteria for Open Courseware and Open Educational Resources. In: 11th ICWL 2012 Workshops. LNCS, vol. 7697. Springer, Heidelberg (in press, 2013)
Vladoiu, M., Constantinescu, Z.: Evaluation and Comparison of Three Open Courseware Based on Quality Criteria. In: Grossniklaus, M., Wimmer, M. (eds.) ICWE Workshops 2012. LNCS, vol. 7703, pp. 204–215. Springer, Heidelberg (2012)
Moise, G., Vladoiu, M., Constantinescu, Z.: MASECO - Multi-Agent System for Evaluation and Classification of OERs and OCW based on Quality Criteria (in press, 2013)
Nicoara, E.S.: The Impact of Massive Online Open Courses in Academic Environments. In: 9th Int. Conf. eLearning and Software for Education. Ed. Universitara, Bucharest (2013)
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H.G.K., Koper, R.: Recommender Systems in Technology Enhanced Learning. In: Kantor, P.B., Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender System Handbook, pp. 387–415. Springer, Berlin (2011)
Lemire, D., Boley, H., McGrath, S., Ball, M.: Collaborative Filtering and Inference Rules for Context-Aware Learning Object Recommendation. International Journal of Interactive Technology and Smart Education 2(3), 179–188 (2005)
Cechinel, C., Sicilia, M.-A., Sánchez Alonso, S., García Barriocanal, E.: Evaluating Collaborative Filtering Recommendations Inside Large Learning Object Repositories. Information Processing and Management 49(1), 34–50 (2013)
Zapata, A., Menéndez, V.H., Prieto, M.E., Romero, C.: A Framework for Recommendation in Learning Object Repositories: An Example of Application in Civil Engineering. Advances in Engineering Software 56, 1–14 (2013)
Resnick, P., Varian, H.R.: Recommender Systems. Commun. ACM 40(3), 56–58 (1997)
Adomavicius, G., Tuzhilin, A.: 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 (2005)
Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)
Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)
Burke, R.: Knowledge-based Recommender Systems. In: Kent, A. (ed.) Encyclopedia of Library and Information Systems, vol. 69(32). Marcel Dekker, New York (2000)
Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating Contextual Information in Recommender Systems using a Multidimensional Approach. ACM Trans. Inf. Syst. 23(1), 103–145 (2005)
Manouselis, N., Drachsler, H., Verbert, K.: TEL as a Recommendation Context, Recommender Systems for Learning, pp. 21–36. Springer, New York (2013)
Buder, J., Schwind, C.: Learning with Personalized Recommender Systems: A Psychological View. Computers in Human Behavior 28, 207–216 (2012)
Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications 7(1), 39–59 (1994)
Smyth, B.: Case-Based Recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 342–376. Springer, Heidelberg (2007)
Lee, J.S., Lee, J.C.: Context Awareness by Case-Based Reasoning in a Music Recommendation System. In: Ichikawa, H., Cho, W.-D., Satoh, I., Youn, H.Y. (eds.) UCS 2007. LNCS, vol. 4836, pp. 45–58. Springer, Heidelberg (2007)
Văduva, I., Albeanu, G.: Introduction to fuzzy modelling. Ed. of Univ. of Bucharest (2004)
Nesbit, J.C., Li, J.Z., Leacock, T.L.: Web-Based Tools for Collaborative Evaluation of Learning Resources. J. of Systemics, Cybernetics and Informatics 3(5), 102–112 (2005)
Burgos Aguilar, J.V.: Rubrics to evaluate OERs (2011), http://www.temoa.info/sites/default/files/OER_Rubrics_0.pdf
ACHIEVE, http://www.achieve.org
OER Commons, http://www.oercommons.org
Vladoiu, M., Constantinescu, Z.: A Taxonomy of Opportunities for Searching, Browsing, and Retrieving OCW and OERs (submitted for publication, 2013)
Rafaeli, S., Barak, M., Dan-Gur, Y., Toch, E.: QSIA: a Web-based Environment for Learning, Assessing and Knowledge Sharing in Communities. Computers & Education 43(3), 273–289 (2004)
Manouselis, N., Vuorikari, R., Van Assche, F.: Simulated Analysis of MAUT Collaborative Filtering for Learning Object Recommendation. In: Proc. of the Workshop on Social Information Retrieval in Technology Enhanced Learning (SIRTEL 2007), pp. 17–20 (2007)
Fiaidhi, J.: RecoSearch: a Model for Collaboratively Filtering Java Learning Objects. Int. J. Instruct. Technol. Distance Learning 1(7), 35–50 (2004)
Tang, T.Y., McCalla, G.I.: Smart Recommendation for an Evolving e-Learning System: Architecture and Experiment. Int. J. E-Learning 4, 105–129 (2005)
Ruiz-Iniesta, A., Jimenez-Diaz, G., Gómez-Albarrán, M.: Recommendation in Repositories of Learning Objects. In: The 9th IEEE International Conference on Advanced Learning Technologies (ICALT 2009), pp. 543–545 (2009)
Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative Filtering Adapted to Recommender Systems of E-learning. Knowl.-Based Syst. 22(4), 261–265 (2009)
Kumar, V., Nesbit, J., Winne, P., Hadwin, A., Jamieson-Noel, D., Han, K.: Quality Rating and Recommendation of Learning Objects. In: Pierre, S. (ed.) E-learning Networked Environments and Architectures, pp. 337–373. Springer, London (2007)
Gomez-Albarran, M., Jimenez-Diaz, G.: Recommendation and Students’Authoring in Repositories of Learning Objects: A Case-Based Reasoning Approach. International Journal of Emerging Technologies in Learning (iJET) 4(1), 35–40 (2009)
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Vladoiu, M., Constantinescu, Z., Moise, G. (2013). QORECT – A Case-Based Framework for Quality-Based Recommending Open Courseware and Open Educational Resources. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_68
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DOI: https://doi.org/10.1007/978-3-642-40495-5_68
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