A Systematic Literature Review on Automated Scoring of Mathematical Constructed Responses

Automated scoring is a research field that facilitates constructivist educational evaluation by applying recently developed artificial intelligence technology to learner evaluation. As mathematics is an essential subject of the K-12 curriculum, research on automated scoring of constructed mathematical responses is of great importance. However, unlike research on automated scoring in other subjects’ responses, studies for automated scoring of mathematical responses still require further exploration. In this study, we propose classification criteria for mathematical constructed responses including response types and input types, identified 21 studies from 15 academic journals registered in SCOPUS with systematic literature review, and investigated them in detail. As a result, Research on automated scoring of mathematical constructed responses has gradually increased in the 2010s, and mainly focusing on algebra and functions, number and operations at the secondary school level. With the advancement of artificial intelligence technology, automated scoring models have also evolved from early rule-based and statistical-based models to machine learning-, deep learning-, and large language models. Furthermore, early automated scoring studies which focused solely on single-modal and digital formatted answers are progressing toward automated scoring of multi-modal and handwritten answers. Based on these findings, We propose a framework that can classify research based on response types and input data formats. Finally, we conclude by emphasizing the importance and necessity of research in automated scoring of constructed mathematical responses, and by providing suggestions for future research directions.