Design and Deployment of a Better Course Search Tool: Inferring Latent Keywords from Enrollment Networks

  • Matthew Dong
  • Run Yu
  • Zachary A. PardosEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11722)


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.


Course search Inferred keywords Latent topics Course2vec Skip-gram Higher education Recommender systems 


  1. 1.
    Backenköhler, M., Scherzinger, F., Singla, A., Wolf, V.: Data-driven approach towards a personalized curriculum. In: Proceedings of the 11th EDM Conference (2018)Google Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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). Scholar
  6. 6.
    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). Scholar
  7. 7.
    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). Scholar
  8. 8.
    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). Scholar
  9. 9.
    Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation. CoRR (2013).
  10. 10.
    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)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    Motz, B., Busey, T., Rickert, M., Landy, D.: Finding topics in enrollment data. In: Proceedings of the 11th EDM Conference (2018)Google Scholar
  13. 13.
    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). Scholar
  14. 14.
    Pardos, Z.A., Nam, A.J.H.: A map of knowledge. CoRR preprint, abs/1811.07974 (2018).
  15. 15.
    Polyzou, A., Karypis, G.: Feature extraction for classifying students based on their academic performance. In: Proceedings of the 11th EDM Conference (2018)Google Scholar
  16. 16.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill Inc., New York (1986)zbMATHGoogle Scholar
  17. 17.
    Shani, G., Shapira, B.: Edurank: a collaborative filtering approach to personalization in e-learning. In: Proceedings of the 7th EDM Conference (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of CaliforniaBerkeleyUSA
  2. 2.Wuhan UniversityWuhanChina

Personalised recommendations