Towards Domain-Independent Assessment of Elementary Students’ Science Competency using Soft Cardinality
Automated assessment of student learning has become the subject of increasing attention. Students’ textual responses to short answer questions offer a rich source of data for assessment. However, automatically analyzing textual constructed responses poses significant computational challenges, exacerbated by the disfluencies that occur prominently in elementary students’ writing. With robust text analytics, there is the potential to analyze a student’s text responses and accurately predict his or her future success.
This paper presents a novel application of the soft cardinality text analytics method to support assessment of text.