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Pre-course student performance prediction with multi-instance multi-label learning

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References

  1. Ren Z, Rangwala H, Johri A. Predicting performance on MOOC assessments using multi-regression models. In: Proceedings of the 9th International Conference on Educational Data Mining, Raleigh, 2016. 484–489

    Google Scholar 

  2. Meier Y, Xu J, Atan O, et al. Predicting grades. IEEE Trans Signal Process, 2016, 64: 959–972

    Article  MathSciNet  Google Scholar 

  3. Zhou Z H, Zhang M L. Multi-instance multi-label learning with application to scene classification. In: Proceedings of International Conference on Neural Information Processing Systems, Vancouver, 2006. 1609–1616

    Google Scholar 

  4. Zafra A, Romero C, Ventura S. Multiple instance learning for classifying students in learning management systems. Expert Syst Appl, 2011, 38: 15020–15031

    Article  Google Scholar 

  5. Sweeney M, Rangwala H, Lester J, et al. Next-term student performance prediction: a recommender systems approach. ArXiv: 1604.01840

  6. Zhang M L. A k-nearest neighbor based multi-instance multi-label learning algorithm. In: Proceedings of the 22nd International Conference on Tools with Artificial Intelligence (ICTAI’10), Arras, 2010. 207–212

    Google Scholar 

  7. Chawla N V, Japkowicz N, Kotcz A. Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newslett, 2004, 6: 1–6

    Article  Google Scholar 

  8. Wang J, Zucker J D. Solving the multiple-instance problem: a lazy learning approach. In: Proceedings of the 17th International Conference on Machine Learning, Stanford, 2000. 1119–1126

    Google Scholar 

  9. Zhou Z H. Machine Learning. Beijing: Tsinghua University Press, 2016. 29–35

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61671274, 61573219, 61701281), Science and Technology Plan Project of Shandong Higher Education Institutions (Grant No. J15LN55), Shandong Provincial Natural Science Foundation (Grant No. ZR2017QF009), Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions, and China Postdoctoral Science Foundation (Grant No. 2016M592190).

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Correspondence to Yilong Yin.

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Ma, Y., Cui, C., Nie, X. et al. Pre-course student performance prediction with multi-instance multi-label learning. Sci. China Inf. Sci. 62, 29101 (2019). https://doi.org/10.1007/s11432-017-9371-y

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