Leveraging Auxiliary Data from Similar Problems to Improve Automatic Open Response Scoring

As computer-based learning platforms have become ubiquitous, 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 problem specific automated scoring model could benefit from auxiliary data collected from similar problems to address this “cold start” problem. We examine factors such as sample 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 increasing sample size, but also leads to greater overall model performance than using data solely from the original problem when sample size is held constant. © 2022 Copyright is held by the author(s).