Topic Extraction from Item-Level Grades (2005)

by Titus Winters, Christian Shelton, Thomas Payne, and Guobiao Mei

Abstract: The most common form of dataset within the educational domain is likely the course gradebook. Data mining on the assignment-level scores is unlikely to provide meaningful results, but a matrix recording scores for every student and every question may provide hidden insight into the workings of a course. Here we will investigate collaborative filtering techniques applied to such data in an attempt to discover what the fundamental topics of a course are and the proficiencies of each student in those topics.

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Titus Winters, Christian Shelton, Thomas Payne, and Guobiao Mei (2005). "Topic Extraction from Item-Level Grades." AAAI-05 Workshop: Educational Data Mining (pp. 1-8).

Bibtex citation

@inproceedings{WinShePayMei05workshop,
   author = "Titus Winters and Christian Shelton and Thomas Payne and Guobiao Mei",
   title = "Topic Extraction from Item-Level Grades",
   booktitle = "{AAAI}-05 Workshop: Educational Data Mining",
   pages = "1--8",
   month = jul,
   year = 2005,
}