Abstract
Liberal arts universities possess a vast catalog of courses from which students can choose. The common approach to surfacing these courses has been through traditional keyword matching information retrieval. The course catalog descriptions used to match on may, however, be overly brief and omit important topics covered in the course. Furthermore, even if the description is verbose, novice students may use search terms that do not match relevant courses, due to their catalog descriptions being written in the specialized language of a discipline outside of their own. In this work, we design and user test an approach intended to help mitigate these issues by augmenting course catalog descriptions with topic keywords inferred to be relevant to the course by analyzing the information conveyed by student co-enrollment networks. We tune a neural course embedding model based on enrollment sequences, then regress the embedding to a bag-of-words representation of course descriptions. Using this technique, we are able to infer keywords, in a system deployed for a user study, that students (N = 75) rated as more relevant than a word drawn at random from a course’s description.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Backenköhler, M., Scherzinger, F., Singla, A., Wolf, V.: Data-driven approach towards a personalized curriculum. In: Proceedings of the 11th EDM Conference (2018)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Chaturapruek, S., Dee, T., Johari, R., Kizilcec, R., Stevens, M.: How a data-driven course planning tool affects college students’ GPA: evidence from two field experiments. In: Proceedings of the 5th Learning @ Scale Conference (2018)
Chen, W., Lan, A.S., Cao, D., Brinton, C., Chiang, M.: Behavioral analysis at scale: learning course prerequisite structures from learner clickstreams. In: Proceedings of the 11th EDM Conference (2018)
Farzan, R., Brusilovsky, P.: Social navigation support in a course recommendation system. In: Wade, V.P., Ashman, H., Smyth, B. (eds.) AH 2006. LNCS, vol. 4018, pp. 91–100. Springer, Heidelberg (2006). https://doi.org/10.1007/11768012_11
Fessl, A., Wertner, A., Pammer-Schindler, V.: Digging for gold: motivating users to explore alternative search interfaces. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) EC-TEL 2018. LNCS, vol. 11082, pp. 636–639. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98572-5_62
Kiryakov, A., Popov, B., Ognyanoff, D., Manov, D., Kirilov, A., Goranov, M.: Semantic annotation, indexing, and retrieval. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 484–499. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39718-2_31
Mesbah, S., Chen, G., Valle Torre, M., Bozzon, A., Lofi, C., Houben, G.-J.: Concept focus: semantic meta-data for describing MOOC content. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) EC-TEL 2018. LNCS, vol. 11082, pp. 467–481. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98572-5_36
Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation. CoRR (2013). http://arxiv.org/abs/1309.4168
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Mikolov, T., Yih, W.t., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751 (2013)
Motz, B., Busey, T., Rickert, M., Landy, D.: Finding topics in enrollment data. In: Proceedings of the 11th EDM Conference (2018)
Pardos, Z.A., Fan, Z., Jiang, W.: Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance. User Model. User-Adap. Inter. 29(2), 487–525 (2019). https://doi.org/10.1007/s11257-019-09218-7
Pardos, Z.A., Nam, A.J.H.: A map of knowledge. CoRR preprint, abs/1811.07974 (2018). https://arxiv.org/abs/1811.07974
Polyzou, A., Karypis, G.: Feature extraction for classifying students based on their academic performance. In: Proceedings of the 11th EDM Conference (2018)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill Inc., New York (1986)
Shani, G., Shapira, B.: Edurank: a collaborative filtering approach to personalization in e-learning. In: Proceedings of the 7th EDM Conference (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Dong, M., Yu, R., Pardos, Z.A. (2019). Design and Deployment of a Better Course Search Tool: Inferring Latent Keywords from Enrollment Networks. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds) Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science(), vol 11722. Springer, Cham. https://doi.org/10.1007/978-3-030-29736-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-29736-7_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29735-0
Online ISBN: 978-3-030-29736-7
eBook Packages: Computer ScienceComputer Science (R0)