Identifying Students' Mechanistic Explanations in Textual Responses to Science Questions With Association Rule Mining

• In Event: Understanding the Ways in Which Students Make Sense in Science

Abstract

Reasoning about causal mechanisms is central to scientific inquiry. In science education, it is important for teachers and researchers to detect students’ mechanistic explanations as evidence of their learning. In this paper, we introduce a semi-automated method which combines association rule mining and human rater’s insights to identify students’ mechanistic explanations from their written responses to science questions. We show an example of applying this method on students’ written responses to a question about climate change and compare the mechanistic reasoning between high and low-scoring groups. Such analysis provides important insights into students’ current knowledge structure and informs teachers and researchers about future design of instructional interventions.

Authors

• Yu Guo, Northwestern University

• Wanli Xing, University of Missouri - Columbia

• Hee-Sun Lee, The Concord Consortium