EAGER: Reimagining Teachers' Assessment Literacy Using Game-Based Learning Analytics

The underlying premise of big data in education is that technology can help teachers and schools make data-informed decisions to better support student learning. As technology use in classrooms continues to increase, the data generated from technology use will also increase. Teachers will soon be required to develop a completely new set of assessment literacies around using the rich data and analytics provided in educational technology.

However, little is known about how these kinds of data should be processed and presented to reflect teachers' decision-making and pedagogical desires. To address this gap, this project will reimagine teachers' assessment literacy in data- and technology-rich classrooms by leveraging a game-based assessment to help teachers become educated and critical consumers of analytics and algorithms embedded in educational technology.

With more literate teachers, it is possible to maximize the potential of educational technology so that teachers can provide personalized and data-driven feedback during the process of learning.

This project will leverage an existing game-based assessment, Shadowspect, that measures Common Core math standards as well as cognitive and noncognitive skills such as spatial reasoning and persistence. The researchers will conduct design-based research to iteratively develop and embed interactive dashboards in the game and investigate how effectively different representations and features of the dashboards can support teachers' understanding and inform their actions.

Additionally, the researchers will investigate how learning analytics can be used to directly support K-12 teachers' assessment practices and identify the knowledge and skills teachers need to be successful in this area. The ultimate goal of the research is to understand teachers' assessment literacy in the era of big data and yield generalizable design principles for interactive dashboard tools that can facilitate teachers' data use without substantive external training.

Ultimately, this research can lead to future investigations of how teachers' formative assessment practices might change when they have an increased understanding of learning analytics.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.