Employing Authentic Analytics for More Authentic Tasks

Although teaching systems and other infrastructure collect large amounts of information that can give insights into the learning behaviour of students, much of this data tends to be concerned with secondary aspects - for example, when students accessed resources or how they performed in inauthentic tasks such as adaptive quizzes. Whereas, when a design task, a higher-order thinking task, or a long-form writing task is given, students typically do their thinking and research outside of the learning environment and only the submitted product is available for analysis. In this chapter, we examine the opportunities for student-facing learning analytics in authentic tasks using authentic tools. By employing professional tools, we can design environments that allow students to work on realistic open-ended problems while gathering data on the strategies and practices they use in the creation process. In some fields, such as software engineering, professional and open source projects gather this sort of data, and those same tools allow collection of student data, allowing us to explore whether students are adopting strategies that experts find to be successful. We see this as the goal of developing cognitive apprenticeships, supported by smart technology, that use more authentic environments. We suggest this trend is the coming together of three strands of development in education: rich learning environments, learning analytics, and authentic tasks.