What could Learning Analytics learn from HCI theory?
It’s good to share this chapter that’s been brewing for about a year now, with helpful feedback from quite a few colleagues en route, gratefully acknowledged. I go back to my roots and share some viewpoints from HCI on my current field of Learning Analytics. This preprint will appear (subject to minor production edits) in the forthcoming book:
Buckingham Shum, S. (In Press). What could Learning Analytics learn from Human-Computer Interaction theory? In: Kathryn Bartimote, Sarah Howard & Dragan Gašević (Eds.), Theory Informing and Arising from Learning Analytics. Springer Nature
Abstract: The design of Learning Analytics (LA) tools is an example of the general problem of designing interactive tools, which is the focus of Human-Computer Interaction (HCI) research and design practice. LA as a field must understand how to embed LA into organisations and the design of effective, trustworthy human-computer systems is where HCI theory and practice have much to offer. Consequently, this chapter argues that LA can learn from (i) the way that theory has evolved in HCI, (ii) the field’s methods for evaluating interactive systems at different scales, and (iii) HCI debates how established scientific theories and methods relate to design theories and methods. As a highly interdisciplinary applied field, LA (like HCI) faces the challenge of maintaining academic standards in the conduct and review of research from many disciplinary traditions. I propose that HCI offers inspiration for researchers seeking rigorous methods to design and evaluate LA in authentic contexts, including principles to maintain their intellectual rigour, which will also be of interest to LA journals and conferences seeking to maintain peer review standards.
[Update 11 Nov 2024] The book is due out soon, and includes in conversation chapters and podcasts:
“Theory Informing and Arising from Learning Analytics delves into the dynamic intersection of learning theory and educational data analysis within the field of Learning Analytics (LA). This groundbreaking book illuminates how theoretical insights can revolutionize data interpretation, reshape research methodologies, and expand the horizons of human learning and educational theory. Organized into three distinct sections, it offers a comprehensive introduction to the role of theory in LA, features contributions from leading scholars who apply diverse theoretical frameworks to their research, and explores cutting-edge topics where new theories are emerging. A standout feature is the inclusion of three “in conversation” chapters, where expert panels dive into the topics of ethics, self-regulated learning, and qualitative computation, enriched by accompanying podcasts that provide fresh, thought-provoking perspectives. This book is an invaluable resource for researchers, sparking debates on the evolving role of theory in LA and challenging conventional epistemological views. Published by Springer, it is an essential read for both aspiring and seasoned scholars eager to engage with the forefront of LA research.”
