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

  • Zachary A. PardosEmail author
  • Zihao Fan
  • Weijie Jiang
Article
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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.

Keywords

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

Notes

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).

References

  1. 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)Google Scholar
  2. 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
  3. 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
  4. 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)Google Scholar
  5. 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)Google Scholar
  6. 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)
  7. 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)Google Scholar
  8. Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)zbMATHGoogle Scholar
  9. 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)Google Scholar
  10. Berkovsky, S., Kuflik, T., Ricci, F.: Cross-domain mediation in collaborative filtering. In: International Conference on User Modeling, pp. 355–359. Springer, Berlin (2007)Google Scholar
  11. 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)Google Scholar
  12. 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
  13. Brusilovsky, P.: Methods and techniques of adaptive hypermedia. User Model. User-Adapt. Interact. 6(2–3), 87–129 (1996)zbMATHGoogle Scholar
  14. Bull, S., Kay, J.: Open learner models. In: Nkambou, R. (ed.) Advances in Intelligent Tutoring Systems, pp. 301–322. Springer, Berlin (2010)Google Scholar
  15. Burke, R.D.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002)zbMATHGoogle Scholar
  16. Burrell, J.: How the machine thinks: understanding opacity in machine learning algorithms. Big Data Soc. 3(1), 2053951715622512 (2016)Google Scholar
  17. 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)Google Scholar
  18. 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)Google Scholar
  19. 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
  20. Chollet, F., et al.: Keras. https://keras.io (2015)
  21. Corbett, A.: Cognitive computer tutors: solving the two-sigma problem. In: International Conference on User Modeling, pp. 137–147 (2001)Google Scholar
  22. 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)Google Scholar
  23. 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)Google Scholar
  24. 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)Google Scholar
  25. 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
  26. 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)Google Scholar
  27. Diamond, A., Lee, K.: Interventions shown to aid executive function development in children 4 to 12 years old. Science 333(6045), 959–964 (2011)Google Scholar
  28. Elbadrawy, A., Karypis, G.: Domain-aware grade prediction and top-n course recommendation. In: RecSys, pp. 183–190 (2016)Google Scholar
  29. 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)Google Scholar
  30. 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)Google Scholar
  31. Four-year myth: Complete College America. Indianapolis (2014)Google Scholar
  32. 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)Google Scholar
  33. Goldberg, Y.: A primer on neural network models for natural language processing. J. Artif. Intell. Res. 57, 345–420 (2016)MathSciNetzbMATHGoogle Scholar
  34. 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)Google Scholar
  35. 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)Google Scholar
  36. 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)
  37. 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)Google Scholar
  38. 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)Google Scholar
  39. 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)Google Scholar
  40. 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)Google Scholar
  41. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-Based Recommendations with Recurrent Neural Networks, ICLR. arXiv preprint arXiv:1511.06939 (2016)
  42. 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)Google Scholar
  43. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)Google Scholar
  44. 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
  45. 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)Google Scholar
  46. 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)Google Scholar
  47. 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)Google Scholar
  48. 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)Google Scholar
  49. Kingma, D., Ba, J.: Adam: a Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014)
  50. Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adapt. Interact. 22(1), 101–123 (2012)Google Scholar
  51. 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)Google Scholar
  52. Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data (TKDD) 4(1), 1 (2010b)Google Scholar
  53. 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)Google Scholar
  54. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)Google Scholar
  55. 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)Google Scholar
  56. 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)Google Scholar
  57. Kuusela, H., Pallab, P.: A comparison of concurrent and retrospective verbal protocol analysis. Am. J. Psychol. 113(3), 387 (2000)Google Scholar
  58. 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)Google Scholar
  59. Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. AAAI 333, 2267–2273 (2015)Google Scholar
  60. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)Google Scholar
  61. 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)Google Scholar
  62. Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. In: Advances in Neural Information Processing Systems, pp. 2177–2185 (2014b)Google Scholar
  63. 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)Google Scholar
  64. 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)Google Scholar
  65. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)Google Scholar
  66. 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)Google Scholar
  67. Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, 19–60 (2010)MathSciNetzbMATHGoogle Scholar
  68. 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)
  69. 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)Google Scholar
  70. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781 (2013a)
  71. 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)Google Scholar
  72. 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)Google Scholar
  73. 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)Google Scholar
  74. 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)Google Scholar
  75. 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)Google Scholar
  76. Pardos, Z.A., Horodyskyj, L.: Analysis of Student Behaviour in Habitable Worlds Using Continuous Representation Visualization. CoRR preprint arXiv:1710.06654 (2017)
  77. 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)Google Scholar
  78. Pardos, Z.A., Nam, A.J.H.: A Map of Knowledge. CoRR preprint arXiv:1811.07974 (2018)
  79. 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)Google Scholar
  80. 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)Google Scholar
  81. Paterek, A.: Improving regularized singular valued decomposition for collaborative filtering. In: Proceedings of KDDcup and Workshop, vol. 2007, pp. 5–8 (2007)Google Scholar
  82. Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)Google Scholar
  83. 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)Google Scholar
  84. 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)Google Scholar
  85. 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)Google Scholar
  86. 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)Google Scholar
  87. 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)
  88. 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)Google Scholar
  89. 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)Google Scholar
  90. 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)Google Scholar
  91. 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)Google Scholar
  92. 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)Google Scholar
  93. 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)Google Scholar
  94. 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)Google Scholar
  95. 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)Google Scholar
  96. 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)Google Scholar
  97. Van Der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014)MathSciNetzbMATHGoogle Scholar
  98. 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)Google Scholar
  99. 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)Google Scholar
  100. 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)Google Scholar
  101. Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., Tingley, D.: Delving Deeper Into MOOC Student Dropout Prediction. arXiv preprint arXiv:1702.06404 (2017)
  102. 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)Google Scholar
  103. 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)Google Scholar
  104. 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)Google Scholar
  105. 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)Google Scholar
  106. Zimmerman, B.J.: Self-regulated learning and academic achievement: an overview. Educ. Psychol. 25(1), 3–17 (1990)MathSciNetGoogle Scholar

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Authors and Affiliations

  1. 1.University of California, BerkeleyBerkeleyUSA

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