Abstract
This paper explores the initial investigation of six recommendation algorithms for deployment in SAS® Curriculum Pathways®, an online repository which houses over 1250 educational resources. The proposed approaches stem from three basic strategies: recommendations based on resource metadata, user behavior, and alignment to academic standards. An evaluation from subject experts suggests that usage-based recommendations are best aligned with teacher needs, though there are interesting domain interactions that suggest the need for continued investigation.
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References
Leachman, M., Mai, C.: Most States Funding Schools Less Than Before the Recession. Center on Budget Policies and Priorities, May 20, 2014
Research on the Use of Khan Academy in Schools (2014)
DeVries, I.: Evaluating Open Educational Resources: Lessons Learned. Procedia - Soc Behav Sci. (2013)
Manouselis, N., Drachsler, H., Verbert, K., Duval, E.: Recommender Systems for Learning. Springer Science & Business Media (2013)
Achieve Inc.: State Support for Open Educational Resources: Key Findings from Achieve’s OER Institute. Washington D.C. (2013)
Koren, Y.: The BellKor Solution to the Netflix Grand Prize (2009)
Goldberg, K.: Eigentaste: A Constant Time Collaborative Filtering Algorithm. Inf. Retr. Boston. 4, 133–151 (2001)
Manouselis, N., Sampson, D.: Recommendation of quality approaches for the European quality observatory. In: 4th IEEE International Conference on Advanced Learning Technologies/EQO-Workshop Quality in European E-Learning (2004)
Vialardi, C., Bravo, J., Shafti, L., Ortigosa, A.: Recommendation in Higher Education Using Data Mining Techniques (2009)
Duffin, J., Muramatsu, B., Logan, N.: OER Recommender: Linking NSDL Pathways and OpenCourseWare Repositories (2008)
Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., Duval, E.: Dataset-driven research for improving recommender systems for learning. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge - LAK 2011, pp. 44–53. ACM Press, New York (2011)
Wang, Y., Sumiya, K.: Semantic ranking of lecture slides based on conceptual relationship and presentational structure. Procedia. Comput. Sci. 1(2), 2801–2810 (2010)
Shen, L.-p., Shen, R.-M.: Learning content recommendation service based-on simple sequencing specification. In: Liu, W., Shi, Y., Li, Q. (eds.) ICWL 2004. LNCS, vol. 3143, pp. 363–370. Springer, Heidelberg (2004)
Underwood, J.: Metis: a contentmap-based recommender system for digital learning activities. In: Educational Recommender Systems and Technologies, pp. 24–42 (2012)
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Sabourin, J., Kosturko, L., McQuiggan, S. (2015). Where to Next? A Comparison of Recommendation Strategies for Navigating a Learning Object Repository. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds) User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science(), vol 9146. Springer, Cham. https://doi.org/10.1007/978-3-319-20267-9_17
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DOI: https://doi.org/10.1007/978-3-319-20267-9_17
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