Factorization models for forecasting student performance

EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining

Volumn , Issue , 2011, Pages 11-20

  Thai Nghe, Nguyen ^a   Horváth, Tomáś ^a   Schmidt Thieme, Lars ^a  

^a UNIVERSITY OF HILDESHEIM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EDUCATIONAL DATA MINING; FACTORIZATION MODEL; FACTORIZATION TECHNIQUES; FORECASTING TECHNIQUES; KNOW-HOW; LARGE DATASETS; PREDICTION TASKS; SEQUENTIAL EFFECTS; STUDENT PERFORMANCE; TENSOR FACTORIZATION;

DATA MINING; FACTORIZATION; FORECASTING;

STUDENTS;

EID: 84857465699     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Corbett, A. T. and Anderson, J. R. 1995. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 253-278.
2. Dunlavy, D. M., Kolda, T. G., and Acar, E. 2011. Temporal link prediction using matrix and tensor factorizations. ACM Trans. Knowl. Discov. Data 5, 10:1-10:27.
3. Feng, M., Heffernan, N., and Koedinger, K. 2009. Addressing the assessment challenge with an online system that tutors as it assesses. User Modeling and User-Adapted Interaction 19, 3, 243-266.
4. Gantner, Z., Drumond, L., Freudenthaler, C., Rendle, S., and Schmidt-Thieme, L. 2010. Learning attribute-to-feature mappings for cold-start recommendations. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010). IEEE Computer Society.
5. Harshman, R. A. 1970. Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-modal factor analysis. UCLA Working Papers in Phonetics 16, 1, 84.
6. Koedinger, K., Baker, R., Cunningham, K., Skogsholm, A., Leber, B., and Stamper, J. 2010. A data repository for the edm community: The pslc datashop. In Handbook of Educational Data Mining, C. Romero, S. Ventura, M. Pechenizkiy, and R. Baker, Eds. Lecture Notes in Computer Science. CRC Press.
7. Kolda, T. G. and Bader, B. W. 2009. Tensor decompositions and applications. SIAM Review 51, 3, 455-500.
8. Koren, Y. 2010. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data 4, 1, 1-24.
9. Pardos, Z. A. and Heffernan, N. T. 2010. Using hmms and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. KDD Cup 2010: Improving Cognitive Models with Educational Data Mining.
10. Pavlik, P. I., Cen, H., and Koedinger, K. R. 2009. Performance factors analysis -a new alternative to knowledge tracing. In Proceeding of the 2009 Conference on Artificial Intelligence in Education. IOS Press, Amsterdam, The Netherlands, 531-538.
11. Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web (WWW'10). ACM, New York, USA, 811-820.
12. Romero, C. and Ventura, S. 2006. Data Mining in E-learning. WIT Pr Computational Mechanics.
13. Romero, C., Ventura, S., Pechenizkiy, M., and Baker, R. S. 2010. Handbook of Educational Data Mining. Chapman and Hall/CRC Data Mining and Knowledge Discovery Series.
14. Thai-Nghe, N., Busche, A., and Schmidt-Thieme, L. 2009. Improving academic performance prediction by dealing with class imbalance. In Proceeding of 9th IEEE International Conference on Intelligent Systems Design and Applications (ISDA'09). Pisa, Italy, IEEE Computer Society, 878-883.
15. Thai-Nghe, N., Drumond, L., Horvath, T., Krohn-Grimberghe, A., Nanopoulos, A., and Schmidt-Thieme, L. 2011. Factorization techniques for predicting student performance. In Educational Recommender Systems and Technologies: Practices and Challenges (In press), O. C. Santos and J. G. Boticario, Eds. IGI Global.
16. Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., and Schmidt-Thieme, L. 2010. Recommender system for predicting student performance. In Proceedings of the 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2010). Vol. 1. Elsevier's Procedia CS, 2811 - 2819.
17. Thai-Nghe, N., Gantner, Z., and Schmidt-Thieme, L. 2010. Cost-sensitive learning methods for imbalanced data. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2010).
18. Thai-Nghe, N., Horvath, T., and Schmidt-Thieme, L. 2011. Personalized forecasting student performance. In Proceedings of the 11th IEEE International Conference on Advanced Learning Technologies (ICALT 2011) (to appear). IEEE CS.
19. Toscher, A. and Jahrer, M. 2010. Collaborative filtering applied to educational data mining. KDD Cup 2010: Improving Cognitive Models with Educational Data Mining.
20. Yu, H.-F., Lo, H.-Y., and Lin, C.-J. 2010. Feature engineering and classifier ensemble for kdd cup 2010. KDD Cup 2010: Improving Cognitive Models with Educational Data Mining.