Monash University
Dragan Gašević is Professor of Learning Analytics in the Faculty of Information Technology at Monash University where is leads the new learning analytics group. Previously, he was a Professor and the Sir Tim O’Shea Chair in Learning Analytics and Informatics in the Moray House School of Education and the School of Informatics at the University of Edinburgh (2015-2018). He served as the past president (2015-2017) of the Society for Learning Analytics Research (SoLAR) and has held several honorary appointments in Australia, Canada, Hong Kong, and USA. A computer scientist by training and skills, Dragan considers himself a learning analyst who develops computational methods that can shape next-generation learning technologies and advance our understanding of self-regulated and social learning. Dragan had the pleasure to serve as a founding program co-chair of the International Conference on Learning Analytics & Knowledge (LAK) in 2011 and 2012 and the Learning Analytics Summer Institute in 2013 and 2014, general chair of LAK’16, and a founding editor of the Journal of Learning Analytics (2012-2017). Dragan is a (co-)author of numerous research papers and books and a frequent keynote speaker.
The Future of Learning Analytics: Valid and Actionable
The analysis of data collected from user interactions with technology has attracted much attention as a promising approach to enhancing the human learning process. This growing interest led to the formation of the field of learning analytics. The field has now entered the next phase of maturation with a growing community who has an evident impact on research, practice, policy, and decision-making. This talk will first provide a brief overview of recent developments in the field. The talk will then explore two key challenges for learning analytics that require immediate attention to unlock the potential of learning analytics to advance our understanding of and optimize human learning: i) validity of data collection and analysis and ii) explainable insights that can drive future action. The talk will discuss promising directions for addressing the two challenges by considering learning analytics as an interdisciplinary interplay between data science, theory of human learning, and design. The challenges and directions will be examined by building on the findings of many research studies.
© AIBD 2019