Skip to main content

Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance

Abstract

The aggregate behaviors of users can collectively encode deep semantic information about the objects with which they interact. In this paper, we demonstrate novel ways in which the synthesis of these data can illuminate the terrain of users’ environment and support them in their decision making and wayfinding. A novel application of recurrent neural networks and skip-gram models, approaches popularized by their application to modeling language, are brought to bear on student university enrollment sequences to create vector representations of courses and map out traversals across them. We present demonstrations of how scrutability from these neural networks can be gained and how the combination of these techniques can be seen as an evolution of content tagging and a means for a recommender to balance user preferences inferred from data with those explicitly specified. From validation of the models to the development of a UI, we discuss additional requisite functionality informed by the results of a usability study leading to the ultimate deployment of the system at a university.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Notes

  1. http://www.gao.gov/assets/690/686530.pdf.

  2. https://github.com/CAHLR/d3-scatterplot.

References

  • Abdollahpouri, H., Burke, R., Mobasher, B.: Recommender systems as multistakeholder environments. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 347–348. ACM (2017)

  • Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing user modeling on Twitter for personalized news recommendations. In: Konstan, J. (ed.) User Modeling, Adaption and Personalization, pp. 1–12. Springer, Berlin (2011)

    Google Scholar 

  • Aleven, V., McLaughlin, E.A., Glenn, R.A., Koedinger, K.R.: Instruction based on adaptive learning technologies. In: Mayer, R.E., Alexander, P. (eds.) Handbook of Research on Learning and Instruction. Routledge, London (2016)

    Google Scholar 

  • Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. ACM (2012)

  • Barkan, O., Koenigstein, N.: Item2vec: neural item embedding for collaborative filtering. In: Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on, pp. 1–6. IEEE (2016)

  • Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., Bengio, Y.: Theano: New Features and Speed Improvements. arXiv preprint arXiv:1211.5590 (2012)

  • Bayer, I., He, X., Kanagal, B., Rendle, S.: A generic coordinate descent framework for learning from implicit feedback. In: Proceedings of the 26th International Conference on World Wide Web, pp, 1341–1350. International World Wide Web Conferences Steering Committee (2017)

  • Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)

    MATH  Google Scholar 

  • Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math compiler in Python. In: Proceedings of the 9th Python in Science Conference, pp. 1–7 (2010)

  • Berkovsky, S., Kuflik, T., Ricci, F.: Cross-domain mediation in collaborative filtering. In: International Conference on User Modeling, pp. 355–359. Springer, Berlin (2007)

  • Bolukbasi, T., Chang, K.-W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In: Advances in Neural Information Processing Systems, pp. 4349–4357 (2016)

  • Brown, P.F., Desouza, P.V., Mercer, R.L., Della Pietra, V.J., Lai, J.C.: Class-based n-gram models of natural language. Comput. Linguist. 18(4), 467–479 (1992)

    Google Scholar 

  • Brusilovsky, P.: Methods and techniques of adaptive hypermedia. User Model. User-Adapt. Interact. 6(2–3), 87–129 (1996)

    Article  MATH  Google Scholar 

  • Bull, S., Kay, J.: Open learner models. In: Nkambou, R. (ed.) Advances in Intelligent Tutoring Systems, pp. 301–322. Springer, Berlin (2010)

    Chapter  Google Scholar 

  • Burke, R.D.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  • Burrell, J.: How the machine thinks: understanding opacity in machine learning algorithms. Big Data Soc. 3(1), 2053951715622512 (2016)

    Article  Google Scholar 

  • Carmagnola, F., Vernero, F., Grillo, P.: Sonars: a social networks-based algorithm for social recommender systems. In: Houben, G.-J., et al. (eds.) Proceedings of the 17 th Conference on User Modeling, Adaptation and Personalization. LNCS 5535, pp. 223–234. Springer, Trento, Italy (2009)

  • 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: Luckin, R., Klemmer, S., Koedinger, K. (eds.) Proceedings of the Fifth Conference on Learning at Scale. ACM, London, UK (2018)

  • Chen, H.-H.: Behavior2vec: generating distributed representations of users behaviors on products for recommender systems. ACM Trans. Knowl. Discov. Data (TKDD) 12(4), 43 (2018)

    Google Scholar 

  • Chollet, F., et al.: Keras. https://keras.io (2015)

  • Corbett, A.: Cognitive computer tutors: solving the two-sigma problem. In: International Conference on User Modeling, pp. 137–147 (2001)

  • Cragun, B.J., Day, P.R.: Dynamic Regulation of Television Viewing Content Based on Viewer Profile and Viewing History. US Patent 5,973,683 (1999)

  • Czarkowski, M., Kay, J.: Bringing scrutability to adaptive hypertext teaching. In: Gauthier G., Frasson C., VanLehn K. (eds) International Conference on Intelligent Tutoring Systems, pp. 423–432. Springer, Berlin (2000)

  • Czarkowski, M., Kay, J., Potts, S.: Scrutability as a core interface element. In: Proceedings of the 2005 Conference on Artificial Intelligence in Education: Supporting Learning Through Intelligent and Socially Informed Technology, pp. 783–785. IOS Press (2005)

  • DeAngelo, L., Franke, R., Hurtado, S., Pryor, J.H., Tran, S.: Completing College: Assessing Graduation Rates at Four-Year Institutions. Higher Education Research Institute, UCLA, Los Angeles (2011)

    Google Scholar 

  • Devooght, R., Bersini, H.: Long and short-term recommendations with recurrent neural networks. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 13–21. ACM, (2017)

  • Diamond, A., Lee, K.: Interventions shown to aid executive function development in children 4 to 12 years old. Science 333(6045), 959–964 (2011)

    Article  Google Scholar 

  • Elbadrawy, A., Karypis, G.: Domain-aware grade prediction and top-n course recommendation. In: RecSys, pp. 183–190 (2016)

  • Fan, Y., Qian, Y., Xie, F.-L., Soong, F.K.: TTS synthesis with bidirectional LSTM based recurrent neural networks. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)

  • Farzan, R., Brusilovsky, P.: Encouraging user participation in a course recommender system: an impact on user behavior. Comput. Hum. Behav. 27(1), 276–284 (2011)

    Article  Google Scholar 

  • Four-year myth: Complete College America. Indianapolis (2014)

  • Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. In: 9th International Conference on Artificial Neural Networks, pp. 850–855 (1999)

  • Goldberg, Y.: A primer on neural network models for natural language processing. J. Artif. Intell. Res. 57, 345–420 (2016)

    MathSciNet  Article  MATH  Google Scholar 

  • Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649. IEEE (2013)

  • Grbovic, M., Radosavljevic, V., Djuric, N., Bhamidipati, N., Savla, J., Bhagwan, V., Sharp, D.: E-commerce in your inbox: product recommendations at scale. In: Proceedings of the 21th International Conference on Knowledge Discovery and Data Mining, pp. 1809–1818. ACM (2015)

  • Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: Draw: A Recurrent Neural Network for Image Generation. arXiv preprint arXiv:1502.04623 (2015)

  • Guàrdia-Sebaoun, E., Guigue, V., Gallinari, P.: Latent trajectory modeling: a light and efficient way to introduce time in recommender systems. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 281–284. ACM (2015)

  • Guo, G., Zhang, J., Thalmann, D.: A simple but effective method to incorporate trusted neighbors in recommender systems. In: Masthoff, J., Mobasher, B., Desmarais, M., Nkambou, R. (eds.) International Conference on User Modeling, Adaptation, and Personalization, pp. 114–125. Springer, Berlin (2012)

    Chapter  Google Scholar 

  • He, X., Zhang, H., Kan, M.-Y., Chua, T.-S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International Conference on Research and Development in Information Retrieval, pp. 549–558. ACM (2016)

  • He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182. International World Wide Web Conferences Steering Committee (2017)

  • Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-Based Recommendations with Recurrent Neural Networks, ICLR. arXiv preprint arXiv:1511.06939 (2016)

  • Hinton, G.E.: Learning distributed representations of concepts. In: Proceedings of the 8th Annual Conference of the Cognitive Science Society, vol. 1, p. 12. Amherst (1986)

  • Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  • Hodara, M., Martinez-Wenzl, M., Stevens, D., Mazzeo, C.: Improving credit mobility for community college transfer students. Plan. High. Educ. 45, 51 (2016)

    Google Scholar 

  • Jannach, D., Ludewig, M., Lerche, L.: Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Model. User-Adapt. Interact. 27, 1–42 (2017)

    Article  Google Scholar 

  • Jayaprakash, S.M., Moody, E.W., Lauría, E.J.M., Regan, J.R., Baron, J.D.: Early alert of academically at-risk students: an open source analytics initiative. J. Learn. Anal. 1(1), 6–47 (2014)

    Article  Google Scholar 

  • Kay, J.: Stereotypes, student models and scrutability. In: Gauthier, G., Frasson, C., VanLehn, K. (eds.) Intelligent Tutoring Systems, vol. 1839, pp. 19–30. Springer, Berlin (2000)

    Chapter  Google Scholar 

  • Kay, J., Kummerfeld, B.: Scrutability, user control and privacy for distributed personalization. In: Proceedings of the CHI Workshop on Privacy-Enhanced Personalization, pp. 21–22 (2006)

  • Kingma, D., Ba, J.: Adam: a Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014)

  • Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adapt. Interact. 22(1), 101–123 (2012)

    Article  Google Scholar 

  • Koren, Y.: Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 53, pp. 89–97. ACM (2010a)

  • Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data (TKDD) 4(1), 1 (2010b)

    MathSciNet  Article  Google Scholar 

  • Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 77–118. Springer, Berlin (2015)

    Chapter  Google Scholar 

  • Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  • Kouki, P., Schaffer, J., Pujara, J., O’Donovan, J., Getoor, L.: User preferences for hybrid explanations. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 84–88. ACM. (2017)

  • Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

  • Kuusela, H., Pallab, P.: A comparison of concurrent and retrospective verbal protocol analysis. Am. J. Psychol. 113(3), 387 (2000)

    Article  Google Scholar 

  • Kyriacou, D., Davis, H.C., Tiropanis, T.: Evaluating three scrutability and three privacy user privileges for a scrutable user modelling infrastructure. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) International Conference on User Modeling. Adaptation, and Personalization, pp. 428–434. Springer, Berlin (2009)

  • Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. AAAI 333, 2267–2273 (2015)

    Google Scholar 

  • LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  • Levy, O., Goldberg, Y.: Dependency-based word embeddings. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 302–308 (2014a)

  • Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. In: Advances in Neural Information Processing Systems, pp. 2177–2185 (2014b)

  • Li, Z., Tinapple, D., Sundaram, H.: Visual planner: beyond prerequisites, designing an interactive course planner for a 21st century flexible curriculum. In: CHI’12 Extended Abstracts on Human Factors in Computing Systems, pp. 1613–1618. ACM (2012)

  • Liang, D., Charlin, L., McInerney, J., Blei, D.M.: Modeling user exposure in recommendation. In: Proceedings of the 25th International Conference on World Wide Web, pp. 951–961. International World Wide Web Conferences Steering Committee (2016)

  • Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  • Luo, Y., Pardos, Z.A.: Diagnosing university student subject proficiency and predicting degree completion in vector space. In: Eaton, E., Wollowski, M. (eds.) AAAI, pp. 7920–7927 (2018)

  • Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, 19–60 (2010)

    MathSciNet  MATH  Google Scholar 

  • Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., Yuille, A.: Deep Captioning with Multimodal Recurrent Neural Networks (M-RNN). arXiv preprint arXiv:1412.6632 (2014)

  • Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., Khudanpur, S.: Recurrent neural network based language model. In: 11th Annual Conference of the International Speech Communication Association, vol. 2, p. 3 (2010)

  • Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781 (2013a)

  • 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 (2013b)

  • Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)

    Article  Google Scholar 

  • Musto, C., Semeraro, G., de Gemmis, M., Lops, P.: Learning word embeddings from Wikipedia for content-based recommender systems. In: European Conference on Information Retrieval, pp. 729–734. Springer, Berlin (2016)

  • Parameswaran, A., Venetis, P., Garcia-Molina, H.: Recommendation systems with complex constraints: a course recommendation perspective. ACM Trans. Inf. Syst. (TOIS) 29(4), 20 (2011)

    Article  Google Scholar 

  • Pardos, Z.A., Dadu, A.: Imputing KCs with representations of problem content and context. In Cena, F., Desmarias, M. (eds.) Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 148–155. ACM, Bratislava, Slovakia (2017)

  • Pardos, Z.A., Horodyskyj, L.: Analysis of Student Behaviour in Habitable Worlds Using Continuous Representation Visualization. CoRR preprint arXiv:1710.06654 (2017)

  • Pardos, Z.A., Nam, A.J.H.: The school of information and its relationship to computer science at UC Berkeley. In: iConference 2017 Proceedings (2017)

  • Pardos, Z.A., Nam, A.J.H.: A Map of Knowledge. CoRR preprint arXiv:1811.07974 (2018)

  • Pardos, Z.A., Tang, S., Davis, D., Le, C.V.: Enabling real-time adaptivity in MOOCs with a personalized next-step recommendation framework. In: Thille, C., Reich, J. (eds.) Proceedings of the Fourth (2017) ACM Conference on Learning@Scale, pp. 23–32. ACM, Cambridge, MA, USA (2017)

  • Pardos, Z.A., Farrar, S., Academy, K., Kolb, J., Peh, G.X., Lee, J.H.: Distributed representation of misconceptions. In: Proceedings of the 13th International Conference of the Learning Sciences (ICLS), London, pp. 1791–1798 (2018)

  • Paterek, A.: Improving regularized singular valued decomposition for collaborative filtering. In: Proceedings of KDDcup and Workshop, vol. 2007, pp. 5–8 (2007)

  • Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)

    Article  Google Scholar 

  • Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovski, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, pp. 325–341. Springer, Berlin (2007)

    Chapter  Google Scholar 

  • Phelan, O., McCarthy, K., Smyth, B.: Using Twitter to recommend real-time topical news. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 385–388. ACM (2009)

  • Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 157–164. ACM (2011)

  • Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th International Conference on Intelligent User Interfaces, pp. 127–134. ACM (2002)

  • Řehůřek, R., Sojka, P.: Software framework for topic modelling with large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Valletta, Malta, pp. 45–50. ELRA. http://is.muni.cz/publication/884893/en (2010)

  • Sacin, C.V., Agapito, J.B., Shafti, L., Ortigosa, A.: Recommendation in higher education using data mining techniques. In: International Working Group on Educational Data Mining (2009)

  • Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovski, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, pp. 291–324. Springer, Berlin (2007)

  • Shapiro, D., Dundar, A., Huie, F., Wakhungu, P.K., Yuan, X., Nathan, A., Bhimdiwala, A.: Completing College: A National View of Student Completion Rates—Fall 2011 Cohort (signature report no. 14). National Student Clearinghouse (2017)

  • Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Buckingham S.S., Ferguson, R., Duval, E., Verbert, K., Baker, R.S.J.D.: Open Learning Analytics: An Integrated & Modularized Platform, Ph.D. thesis, Doctoral dissertation. Open University Press (2011)

  • Snow, E.L., Allen, L.K., Jacovina, M.E., McNamara, D.S.: Does agency matter? Exploring the impact of controlled behaviors within a game-based environment. Comput. Educ. 82, 378–392 (2015)

    Article  Google Scholar 

  • Suglia, A., Greco, C., Musto, C., de Gemmis, M., Lops, P., Semeraro, G.: A deep architecture for content-based recommendations exploiting recurrent neural networks. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 202–211. ACM (2017)

  • Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

  • Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 17–22. ACM (2016)

  • Tang, S., Peterson, J.C., Pardos, Z.A.: Predictive modelling of student behaviour using granular large-scale action data. Handb. Learn. Anal. 1, 223–233 (2017)

    Article  Google Scholar 

  • Van Der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014)

    MathSciNet  MATH  Google Scholar 

  • Van Meteren, R., Van Someren, M.: Using content-based filtering for recommendation. In: Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, pp. 47–56 (2000)

  • Wang, L., Sy, A., Liu, L., Piech, C.: Learning to represent student knowledge on programming exercises using deep learning. In: Proceedings of the 10th International Conference on Educational Data Mining, Wuhan, China, pp. 324–329 (2017)

  • Wang, X., He, X., Feng, F., Nie, L., Chua, T.-S.: TEM: Tree-enhanced embedding model for explainable recommendation. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 1543–1552. International World Wide Web Conferences Steering Committee (2018)

  • Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., Tingley, D.: Delving Deeper Into MOOC Student Dropout Prediction. arXiv preprint arXiv:1702.06404 (2017)

  • Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y.: Show, attend and tell: neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048–2057 (2015)

  • Yang, D., Sinha, T., Adamson, D., Rosé, C.P.: Turn on, tune in, drop out: anticipating student dropouts in massive open online courses. In: Proceedings of the 2013 NIPS Data-Driven Education Workshop, vol. 11, p. 14 (2013)

  • Zhiwen, Y., Zhou, X., Hao, Y., Jianhua, G.: Tv program recommendation for multiple viewers based on user profile merging. User Model. User-adapt. Interact. 16(1), 63–82 (2006)

    Article  Google Scholar 

  • Zanotti, G., Horvath, M., Barbosa, L.N., Immedisetty, V.T.K.G., Gemmell, J.: Infusing collaborative recommenders with distributed representations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 35–42. ACM (2016)

  • Zimmerman, B.J.: Self-regulated learning and academic achievement: an overview. Educ. Psychol. 25(1), 3–17 (1990)

    MathSciNet  Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the generous contributions by UC Berkeley administrators and staff; Andrew Eppig, Mark Chiang, Max Michel, Jen Stringer, and Walter Wong with a special thanks to associate registrar Johanna Metzgar for her partnership in the deployment of the system. We would like to also thank the following undergraduate student research assistants for their contributions to the system’s development; Christopher Vu Le, Andrew Joo Hun Nam, Arshad Ali Abdul Samad, Alessandra Silviera, Divyansh Agarwal, and Yuetian Luo. This work was supported by Grants from the National Science Foundation (Awards 1547055, 1446641).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zachary A. Pardos.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by Grants from the National Science Foundation (Awards 1547055, 1446641).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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, 487–525 (2019). https://doi.org/10.1007/s11257-019-09218-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11257-019-09218-7

Keywords

  • Recommender systems
  • Distributed representation
  • Recurrent neural networks
  • Skip-gram
  • Scrutability
  • Usability study
  • Higher education