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This book explores feedback analytics in assessment, which plays a vital role in learning, provides insight into learning processes, and informs educational decisions. A key component of assessment is feedback, which has been shown to have a significant impact on learning success. Feedback aims to reduce the gap between a learner's current understanding and the desired learning outcomes. Researchers have categorised feedback in different ways, taking into account factors such as the type of information provided and the timing of delivery.

Pervasive digital learning environments generate valuable data through learner interactions and beyond. While learning analytics encompasses various subfields, including assessment analytics, which focuses on assessment data, a more specific area of inquiry is needed to fully understand the role of feedback.

This area, which is defined as 'feedback analytics', focuses specifically on the analysis of feedback processes within assessments. Whereas assessment analytics examines the broader assessment data, feedback analytics delves into the nuances of feedback itself, aiming to uncover patterns in how feedback is given, received and used by learners. To realise the full potential of feedback analytics, further research is needed to thoroughly understand the data generated by feedback processes, to identify key metrics for measuring feedback effectiveness, to bridge the gap between theoretical frameworks and practical applications, to design learning environments and processes to maximise the impact of feedback, and to provide empirical evidence on the effectiveness and efficiency of different feedback strategies. This focused research agenda is addressed in this edited volume and will be crucial for optimising learning and assessment through more effective and targeted feedback interventions.