publication . Preprint . Part of book or chapter of book . 2018

ALE: Additive Latent Effect Models for Grade Prediction

Zhiyun Ren; Xia Ning; Huzefa Rangwala;
Open Access English
  • Published: 16 Jan 2018
Abstract
Comment: 9 pages, 3 figures
Subjects
ACM Computing Classification System: ComputingMilieux_COMPUTERSANDEDUCATION
free text keywords: Computer Science - Learning
Related Organizations
Funded by
NSF| BIGDATA: IA: DKA: Collaborative Research: Learning Data Analytics: Providing Actionable Insights to Increase College Student Success
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1447489
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
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Part of book or chapter of book . 2018
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20 references, page 1 of 2

[1] Charu C. Aggarwal. Recommender Systems: The Textbook. Springer Publishing Company, Incorporated, 1st edition, 2016.

[2] Asmaa Elbadrawy and George Karypis. Domain-aware grade prediction and top-n course recommendation. Boston, MA, Sep, 2016. [OpenAIRE]

[3] Asmaa Elbadrawy, Agoritsa Polyzou, Zhiyun Ren, Mackenzie Sweeney, George Karypis, and Huzefa Rangwala. Predicting student performance using personalized analytics. Computer, 49(4):61-69, 2016.

[4] Asmaa Elbadrawy, Scott Studham, and George Karypis. Personalized multi-regression models for predicting students performance in course activities. UMN CS, pages 14-011, 2014.

[5] Ruining He, Chen Fang, Zhaowen Wang, and Julian McAuley. Vista: A visually, socially, and temporallyaware model for artistic recommendation. arXiv preprint arXiv:1607.04373, 2016. [OpenAIRE]

[6] Ruining He and Julian McAuley. Fusing similarity models with markov chains for sparse sequential recommendation. arXiv preprint arXiv:1609.09152, 2016. [OpenAIRE]

[7] Noam Koenigstein, Gideon Dror, and Yehuda Koren. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In Proceedings of the fifth ACM conference on Recommender systems, pages 165- 172. ACM, 2011. [OpenAIRE]

[8] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30-37, August 2009.

[9] Andrew Lan, Tom Goldstein, Richard Baraniuk, and Christoph Studer. Dealbreaker: A nonlinear latent variable model for educational data. In Proceedings of The 33rd International Conference on Machine Learning, pages 266-275, 2016.

[10] Yannick Meier, Jie Xu, Onur Atan, and Mihaela van der Schaar. Personalized grade prediction: A data mining approach. In Data Mining (ICDM), 2015 IEEE International Conference on, pages 907-912. IEEE, 2015.

[11] Sara Morsy and George Karypis. Cumulative knowledgebased regression models for next-term grade prediction. In Proceedings of the 2017 SIAM International Conference on Data Mining, pages 552-560. SIAM, 2017. [OpenAIRE]

[12] Xia Ning, Christian Desrosiers, and George Karypis. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook, pages 37-76. 2015.

[13] Michelle Parker. Advising for retention and graduation. 2015.

[14] Agoritsa Polyzou and George Karypis. Grade prediction with models specific to students and courses. International Journal of Data Science and Analytics, 2(3-4):159-171, 2016.

[15] Steffen Rendle, Christoph Freudenthaler, and Lars SchmidtThieme. Factorizing personalized markov chains for nextbasket recommendation. In Proceedings of the 19th international conference on World wide web, pages 811-820. ACM, 2010.

20 references, page 1 of 2
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publication . Preprint . Part of book or chapter of book . 2018

ALE: Additive Latent Effect Models for Grade Prediction

Zhiyun Ren; Xia Ning; Huzefa Rangwala;