1E: Using Formative Assessment to Improve Student Learning in Large Enrollment Classes


With Jennifer Kaplan, Alex Lyford (University of Georgia); and Amy Froelich (Iowa State University)


Abstract

This session will introduce resources from two projects, ISEA and AACR, that provide instructors with real-time feedback on formative assessment items for statistics classes of any size. One of the recommendations central to the Guidelines for Assessment and Instruction in Statistics Education (GAISE) is the use of assessments to improve and evaluate student learning. The GAISE 2016 authors suggest that formative assessment, which aims to monitor and improve student learning by providing ongoing feedback during the learning process, can enhance instruction by focusing on ideas and concepts that are most challenging for students. It can be difficult, however, to derive meaning from assessments in time to affect instruction for current students, especially for instructors of large classes. The ISEA project has created an electronic assessment model that includes questions in multiple formats (e.g. fill-in-the-blank, matching, and multiple choice) for introductory statistics courses along with report generating software package, called ePort, to provide feedback about student performance at the question, learning outcome, and topic levels to instructors and course supervisors. The AACR project has created open-ended items and associated machine learning ensembles that allow instructors to upload student responses to statistics questions and receive an automatically generated report on student understanding of the statistical topic targeted by the question. By the end of this session, instructors of all class sizes will be prepared to incorporate formative assessments in their classrooms using the products of the ISEA and AACR projects.


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