Improve the Prediction of Student Performance with Hint's Assistance Based on an Efficient Non-Negative Factorization-Reference-Cited by-同舟云学术

Improve the Prediction of Student Performance with Hint's Assistance Based on an Efficient Non-Negative Factorization

Published:2017 Issue:4 Volume:E100.D Page:768-775
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

XU Ke¹²,LIU Rujun¹,SUN Yuan³,ZOU Keju⁴,HUANG Yan¹,ZHANG Xinfang¹

Affiliation:

1. Huazhong University of Science and Technology

2. South-Central University for Nationalities

3. Hubei University of Traditional Chinese Medicine

4. Sun Yat-Sen University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E100.D/4/E100.D_2016DAP0026/_pdf

Reference26 articles.

1. [1] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” IEEE Computer, vol.42, no.8, pp.30-37, 2009.

2. [2] N. Thai-Nghe, L. Drumond, T. Horváth, A. Nanopoulos, and L. Schmidt-Thieme, “Matrix and tensor factorization for predicting student performance,” Proc. 3rd International Conference on Computer Supported Education, pp.69-78, Noordwijkerhout, Netherlands, 2011.

3. [3] A.T. Corbett and J.R. Anderson, “Knowledge tracing: Modeling the acquisition of procedural knowledge,” User Modeling and User-Adapted Interaction, vol.4, no.4, pp.253-278, 1995.

4. [4] D.D. Lee and S.H. Seung, “Learning the parts of objects by non-negative matrix-factorization,” Nature, vol.401, pp.788-791, 1999.

5. [5] M.C. Desmarais, “Mapping question items to skills with non-negative matrix factorization,” SIGKDD Explorations, vol.13, no.2, pp.30-36, 2011.

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