Improving teacher candidate knowledge using content acquisition podcasts

Abstract

There are many open questions with respect to theory and empirical support for methods used in college teaching, especially when technology is incorporated into instruction. In this article, we report the results of a study of a multimedia-based instructional tool called Content Acquisition Podcasts (CAPs) that provides university instructors with a tool that is grounded in applied theory and has advanced through several iterations of developmental and experimental testing as suggested by Clark (2009). CAPs are a form of enhanced podcasts (still images synchronized with audio) that incorporate Mayer's cognitive theory of multimedia learning (2009), and accompanying instructional design principles (2008) to ensure the looks and sounds of instruction do not overwhelm the limitations of users' cognitive processes. This article reports data from one of the first five experimental tests of CAPs in which undergraduate teacher candidates received instruction related to content from an introductory course in special education. In this study, teacher candidates from two universities were randomly assigned either to watch a CAP or read a textbook chapter containing the same content for two topics: characteristics of students with learning disabilities or high-functioning autism. We employed a pretest-posttest-maintenance design to evaluate participant performance on dependent measures of knowledge. Results indicate that when participants learned with CAPs, they had significantly higher scores on content-knowledge tests at both posttest and maintenance assessments than when they studied via the usual text-based materials.