Abstract
Developing online courses is a complex and time-consuming process that involves organizing a course into a sequence of topics and allocating the appropriate learning content within each topic. This task is especially difficult in complex domains like programming, due to the incremental nature of programming knowledge, where new topics extensively build upon domain concepts that were introduced in earlier lessons. In this paper, we propose a course-adaptive content-based recommender system that assists course authors and instructors in selecting the most relevant learning material for each course topic. The recommender system adapts to the deep prerequisite structure of the course as envisioned by a specific instructor, while unobtrusively deducing that structure from problem-solving examples that the instructor uses to present course concepts. We assessed the quality of recommendations and examined several aspects of the recommendation process by using three datasets collected from two different courses. While the presented recommender system was built for the domain of introductory programming, our course-adaptive recommendation approach could be used in a variety of other domains.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Moffatt, D.V., Moffatt, P.B.: Eighteen pascal texts: an objective comparison. SIGCSE Bull. 14(2), 2–10 (1982)
Wang, S., He, F., Andersen, E.: A unified framework for knowledge assessment and progression analysis and design. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 937–948. ACM, New York (2017)
Cafolla, R.: Project MERLOT: bringing peer review to web-based educational resources. J. Technol. Teacher Educ. 14(2), 313–323 (2006)
Hislop, G., et al.: Sharing your instructional materials via ensemble. J. Comput. Sci. Coll. 26(6), 160–162 (2011)
Murray, T.: An overview of intelligent tutoring system authoring tools: updated analysis of the state of the art. In: Murray, T., Blessing, S.B., Ainsworth, S. (eds.) Authoring Tools for Advanced Technology Learning Environments: Toward Cost-Effective Adaptive, Interactive and Intelligent Educational Software, pp. 491–544. Springer, Dordrecht (2003). https://doi.org/10.1007/978-94-017-0819-7_17
Sottilare, R.A.: Challenges to enhancing authoring tools and methods for intelligent tutoring systems. In: Sottilare, R.A., Graesser, A.C., Hu, X., Brawner, K. (eds.) Design Recommendations for Intelligent Tutoring Systems, pp. 3–7. U.S. Army Research Laboratory, Orlando, FL (2015)
Manouselis, N., Drachsler, H., Verbert, K., Duval, E. (eds.): Recommender Systems for Learning. Springer, Berlin (2013). https://doi.org/10.1007/978-1-4614-4361-2
Mitrovic, A., et al.: ASPIRE: an authoring system and deployment environment for constraint-based tutors. Int. J. Artif. Intell. Educ. 19(2), 155–188 (2009)
Aleven, V., McLaren, B.M., Sewall, J., Koedinger, K.R.: The cognitive tutor authoring tools (CTAT): preliminary evaluation of efficiency gains. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 61–70. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_7
Brusilovsky, P., Eklund, J., Schwarz, E: Web-based education for all: a tool for developing adaptive courseware. In: Proceedings of Seventh International World Wide Web Conference, Brisbane, Australia, 14–18 April 1998, pp. 291–300 (1998)
Chad Lane, H., Core, M.G., Hays, M.J., Auerbach, D., Rosenberg, M.: Situated pedagogical authoring: authoring intelligent tutors from a student’s perspective. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M.F. (eds.) AIED 2015. LNCS (LNAI), vol. 9112, pp. 195–204. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19773-9_20
Cristea, A., Aroyo, L.: Adaptive authoring of adaptive educational hypermedia. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 122–132. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47952-X_14
Brusilovsky, P., Sosnovsky, S., Yudelson, M., Chavan, G.: Interactive authoring support for adaptive educational systems. In: Proceedings of the 2005 Conference on AI in Education, pp. 96–103. IOS Press, Amsterdam (2005)
Cabada, R.Z., Estrada, M.L.B., Garca, C.A.R.: EDUCA: a web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network. Expert Syst. Appl. 38(8), 9522–9529 (2011)
Medio, C.D., Gasparetti, F., Limongelli, C., Sciarrone, F., Temperini, M.: Course-driven teacher modeling for learning objects recommendation in the moodle LMS. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP 2017), pp. 141–145. ACM, New York (2017)
Brusilovsky, P., et al.: Increasing adoption of smart learning content for computer science education. In: Working Group Reports of the 2014 Conference on Innovation and Technology in Computer Science Education, Uppsala, Sweden, pp. 31–57. ACM (2014)
Hosseini, R., Brusilovsky, P.: JavaParser: a fine-grain concept indexing tool for java problems. In: The First Workshop on AI-supported Education for Computer Science, pp. 60–63. Springer, Heidelberg (2013)
Falmagne, J.-C., Cosyn, E., Doignon, J.-P., Thiéry, N.: The assessment of knowledge, in theory and in practice. In: Missaoui, R., Schmidt, J. (eds.) ICFCA 2006. LNCS (LNAI), vol. 3874, pp. 61–79. Springer, Heidelberg (2006). https://doi.org/10.1007/11671404_4
Acknowledgements
We would like to thank Arto Hellas from University of Helsinki for providing dataset 3. We would like to thank Yun Huang, Roya Hosseini, and other members of the PAWS lab for their feedback on this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Chau, H., Barria-Pineda, J., Brusilovsky, P. (2018). Course-Adaptive Content Recommender for Course Authoring. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science(), vol 11082. Springer, Cham. https://doi.org/10.1007/978-3-319-98572-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-98572-5_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98571-8
Online ISBN: 978-3-319-98572-5
eBook Packages: Computer ScienceComputer Science (R0)