Improving Matrix Factorization Techniques of Student Test Data with Partial Order Constraints

@inproceedings{Beheshti2012ImprovingMF,
  title={Improving Matrix Factorization Techniques of Student Test Data with Partial Order Constraints},
  author={Behzad Beheshti and Michel C. Desmarais},
  booktitle={UMAP},
  year={2012}
}
Matrix factorization is a general technique that can extract latent factors from data. Recent studies applied matrix factorization to the problem of establishing which skills are required by question items, and for assessing student skills mastery from student performance data. A number of generic algorithms, such as Non-negative Matrix Factorization and Tensor factorization, are used in these studies to perform the factorization, but few have looked at optimizing these algorithms to the… CONTINUE READING