This chapter energises the field of learning analytics by presenting research on assessment through recognising the humanness and depth of data analytics and learning.
Ifenthaler, D., & Greiff, S. (2021). Leveraging learning analytics for assessment and feedback. In J. Liebowitz (Ed.), Online learning analytics (pp. 1–18). Auerbach Publications. https://doi.org/10.1201/9781003194620
This chapter critically reflects the current state of research in learning analytics and educational assessment. Given the omnipresence of technology-enhanced assessment approaches, vast amounts of data are produced in such systems, which open further opportunities for advancing assessment and feedback systems as well as pedagogical assessment practice. A yet-to-be-solved limitation of learning analytics frameworks is the lack of a stronger focus on dynamic or real-time assessment and feedback, as well as the improvement of learning environments. Therefore, a benefits matrix for analytics-enhanced assessment is suggested, which provides examples on how to harness data and analytics for educational assessment. Further, a framework for implementing analytics-enhanced assessment is suggested. The chapter concludes with a critical reflection on current challenges for making use of analytics data for educational assessments. Clearly, stakeholders in the educational arena need to address ethics and privacy issues linked to analytics-enhanced assessments.
