Whiteboard animations are increasingly used in education despite little evidence of their efficacy. In this study, we measured the impact of whiteboard animations and other common instructional formats on learning outcomes, experience, and motivation. We recruited participants from Amazon’s Mechanical Turk (N=568; 326 females). Participants were randomly assigned to view online lessons about popular topics in social science from wellestablished scholars in one of five common instructional formats: whiteboard animation, electronic slideshow, stage lecture, audio, and text. Results showed a benefit of whiteboard animations in terms of learning and subjective experiences of enjoyment and engagement.
Text components of digital lessons and problems are often static: they are written once and too often not improved over time. This is true for both large text components like webpages and documents as well as the small components that form the building blocks of courses: explanations, hints, examples, discussion questions/answers, emails, study tips, motivational messages. This represents a missed opportunity, since it should be technologically straightforward to enhance learning by improving text, as instructors get new ideas and data is collected about what helps learning. We describe how instructors can use recent work (Williams, Kim, Rafferty, Maldonado, Gajos, Lasecki, & Heffernan, 2016a) to make text components into adaptive resources that semiautomatically improve over time, by combining crowdsourcing methods from human computer interaction (HCI) with algorithms from statistical machine learning that use data for optimization.