Measuring Knowledge Gaps in Student Responses by Mining Networked Representations of Texts

Abstract

Gaps between knowledge sources are interesting to various stakeholders: they might indicate potential misconceptions awaiting correction, complex or novel knowledge that requires careful delivery or studying. Motivated by these underlying values, this study explores the knowledge gap phenomenon in the context of student textual responses. In the method proposed in this study, discourses are first mapped into structured knowledge spaces where gaps between correct/incorrect responses and assessed knowledge are measured by network-based metrics. Empirical results demonstrate the effectiveness of the proposed method in measuring gaps in student responses. The networked representation of texts proposed in this study is novel in quantitatively framing gaps of knowledge. It also offers a set of validated metrics for analyzing student responses in research and practice.