Examining the Effect of Assessment Construct Characteristics on Machine Learning Scoring of Scientific Argumentation

This paper investigates how assessment construct characteristics (complexity, diversity, structure) affect machine learning model performance when automatically scoring middle school students' written scientific argumentation responses across 17 assessment tasks aligned to K-12 science education standards (NGSS).

Argumentation, a key scientific practice presented in the Framework for K-12 Science Education , requires students to construct and critique arguments, but timely evaluation of arguments in large-scale classrooms is challenging. Recent work has shown the potential of automated scoring systems for open response assessments, leveraging machine learning (ML) and artificial intelligence (AI) to aid the scoring of written arguments in complex assessments. Moreover, research has amplified that the fea

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