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Title: Leveraging Auxiliary Data from Similar Problems to Improve Automatic Open Response Scoring
As computer-based learning platforms have become ubiq- uitous, there is a growing need to better support teachers. Particularly in mathematics, teachers often rely on open- ended questions to assess students’ understanding. While prior works focusing on the development of automated open- ended work assessments have demonstrated their potential, many of those methods require large amounts of student data to make reliable estimates. We explore whether a prob- lem specific automated scoring model could benefit from auxiliary data collected from similar problems to address this “cold start” problem. We examine factors such as sam- ple size and the magnitude of similarity of utilized problem data. We find the use of data from similar problems not only provides benefits to improve predictive performance by in- creasing sample size, but also leads to greater overall model performance than using data solely from the original prob- lem when sample size is held constant.  more » « less
Award ID(s):
1903304
NSF-PAR ID:
10331803
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the 15th International Conference on Educational Data Mining,
Page Range / eLocation ID:
in press
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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