Low-Stakes, Growth-Oriented Testing in Large-Enrollment General Chemistry 1: Formulation, Implementation, and Statistical Analysis

Abstract

We formulate an alternative to high-stakes examinations that is designed to help students grow, and we describe its implementation in a large-enrollment General Chemistry 1 class. In our alternative grading approach, students complete weekly assessments. Each assessment has four items that are aligned to explicit learning objectives and a level in Marzano's taxonomy, retrieval, comprehension, analysis, and knowledge utilization, which can be used by students and instructors to gauge the progression of student learning. Proficiency-based grading and multiple attempts reduce the stakes of the assessments. Unique assessments are generated through a computational infrastructure that draws question stems from an item bank and further randomizes quantities, elements, compounds, reactions, spectra, Lewis structures, orbitals, etc. in the questions. Nearly all assessment items require student-generated responses and cover a complete General Chemistry 1 curriculum. We interpret Marzano's taxonomy in the General Chemistry context and outline the structure of the learning objectives, cognitive levels, assessment schedule, and grading scheme. Item response theory (Rasch analysis) validates the theoretical framework and indicates that assessment items are high quality. Students demonstrate improvement through assessment retakes, and they report that the system motivates them to study and learn.