Questioning What We Know: Evaluating Predictability of Candidate Data

(College of Education, Lipscomb University, USA)

Abstract: As teacher educators, it is our aim to support the production of caring, thoughtful educators who inspire children. We strive to equip our candidates to teach and connect with all learners. As a part of preparation, we promote data to drive decision-making in classrooms; but, what does the data we collect tell us? Is our data useful or predictive? In this study, we examined typically-used candidate data (e.g., GPA, student teaching data, test scores) and correlated these with completer scores on future outcome measures (e.g., teacher effectiveness). We found that some of our most trusted data was not as predictive as we had hoped. The resulting data has propelled us to change how we promote and target specific skills and attributes within the context of teacher education. We aim to continue to use our data to improve our program offerings and support our candidates on their road to growth.