Bringing Nuance to Automated Exam and Classroom Response System Grading: A Tool for Rapid, Flexible, and Scalable Partial-Credit Scoring

摘要

We present here an extension of Morrison's and Ruder's "Sequence-Response Questions" (SRQs) that allows for more nuance in the assessment of student responses to these questions. We have implemented grading software (which we call ANGST, "Automated Nuanced Grading & Statistics Tool") in a Microsoft Excel sheet that can take SRQ answer data from any source and flexibly and automatically grade these responses with partial credit. This allows for instructors to assess a range of understanding of material from student-generated answers as in a traditional written exam, while still reducing grading workload for large classes. It also allows instructors to do automated statistical analysis on the most popular answers, and subanswers, either from sources like exams or classroom response systems (CRSs), to determine common misunderstandings and facilitate adjustments to instruction.