Applications of learning analytics for primary and secondary schools

This dissertation investigates how learning analytics can be applied to primary and secondary school contexts. The possibilities of learning analytics has grown with the increasing application of educational technology in the classroom. Despite this, the implementations of learning analytics in K12 tend to focus on student optimizations and teacher dashboards. These implementations omit valuable stakeholders who could benefit from learning analytics applications and limit the breadth of applications that could be developed to enhance learning. As such, this dissertation aims to explore how learning analytics could be used for administrators and for teachers beyond teacher dashboards.

This dissertation presents five contributions for researchers in learning analytics and educational technology. First, we explore how learning analytics with clustering could be used to better understand teacher competencies at scale for administrators. Second, we investigate the strengths of diverse recommender system architectures for use in primary and secondary school applications and connect these strengths to educational theory. Third, we use an educational recommender system with teachers and seek to better understand how teachers use and evaluate a recommender system in their work. Fourth, we apply learning analytics to developing learning designs. Finally, we investigate how learning analytics can provide an insight into student’s computational thinking ability for alternative forms of evaluation and feedback.