There is no doubt that students’ emotional states influence their general well-being and their learning success. Although current advances of computer hardware and Artificial Intelligence (AI) techniques made it possible to include real-time emotion-sensing as one of the ways to improve students’ learning experience and performance, there are many challenges that might inhibit or delay the deployment and usage of emotional learning analytics (LA) in education. In this chapter, we will critically review the current state-of-the art of emotion detection techniques, analysis and visualisation, the benefits of emotion analysis in education and the ethical issues surrounding emotion-aware systems in education. Finally, we hope that our guidelines on how to tackle each of those issues can support research in this area.

Over the last 30 years, information in the form of digital data has become the foundation upon which decisions are made. Governments, businesses and organisations have realised that they must have access to the right data at the right time, and also undertake differing types of analysis to make correct decisions. Data have become a part of everyday life, and the ability for all citizens to have some level of data literacy is becoming increasingly important. Data literacy in schools, or the ability to understand and use data effectively to inform decisions, will create transferable skills for students moving into the twenty-first century workforce. This means students need to be highly skilled in collecting, recording, accessing and representing data. Students should know where and how to access data, and how to use data to tell their story. Geography plays an essential role in developing these skills in the school curriculum, particularly in the secondary years. While ‘using data’ is a skill in many subjects, geography provides the opportunity for data to be used in real-world contexts, and the skills are very transferable for many future pathways.