Mougiakou, S., Vinatsella, D., Sampson, D., Papamitsiou, Z., Giannakos, M., & Ifenthaler, D. (2023). Teaching analytics. In S. Mougiakou, D. Vinatsella, D. Sampson, Z. Papamitsiou, M. Giannakos, & D. Ifenthaler (Eds.), Educational data analytics for teachers and school leaders (pp. 189-235). Springer. https://doi.org/10.1007/978-3-031-15266-5_4

Abstract:

“This chapter will introduce the basics of methods and tools for analysing and interpreting educational data for facilitating educational decision making, including course and curricula design. Teaching analytics use static and dynamic information about the design of learning environments for near real-time modelling, prediction, and optimisation of learning artefacts, learning designs, learning processes, curriculum designs, and educational decision making.

• The first topic focuses on data sources for supporting teaching analytics. You will reflect on the instructional design process and locate data sources for optimising learning environments as well as understand limitations and requirements for data quality.

• The second topic includes critical reflections on data ethics and privacy principles. You will build awareness toward data privacy, distinguish different levels of data protection and identify issues of authorship, ownership, data access and data-sharing.

• The third topic addresses the application and communication of educational data and analytics findings to various stakeholders. You will design and revise automated and semi-automated interventions as well as apply methodologies for improving the design of learning environments, teaching processes as well as curricula.

In order to warm-up, explore the “didactic triangle” in Fig. 4.1 and reflect what data may stem from each of the key concepts and related interactions.”