[apologies for multiple copies]


Hi all,


It is my pleasure to invite you to SoLAR Webinar "What Do We Mean by Rigour in Learning Analytics?" This will be a unique webinar panel session, co-organised between SoLAR and the Journal of Learning Analytics, featuring a fantastic line-up of panellists.


Via Zoom (meeting URL provided in the registration email)



New York, US: Wednesday, 11 Nov 2020, 3:00 PM – 4:00 PM

London, UK: Wednesday, 11 Nov 8:00 PM – 9:00 PM

Sydney, Australia: Thursday, 12 Nov 7:00 AM – 8:00 AM



Sandra Milligan, University of Melbourne

Philip Winne, Simon Fraser University

Dragan Ga╣eviŠ, Monash University

Jelena JovanoviŠ, University of Belgrade

Kristine Lund, University of Lyon, France

Bodong Chen, University of Minnesota


Chaired by The Journal of Learning Analytics Editors:

Xavier Ochoa, New York University

Alyssa Wise, New York University

Simon Knight, University of Technology Sydney


To register, go to https://www.eventbrite.com.au/e/solar-webinar-what-do-we-mean-by-rigour-in-learning-analytics-registration-123168563489



We are looking forward to seeing you at the webinar!


Kind regards,

Vitomir Kovanovic,


Society for Learning Analytics Research (SoLAR)



What Do We Mean by Rigour in Learning Analytics?


This SoLAR webinar, led by Journal of Learning Analytics (JLA) editors Simon Knight, Xavier Ochoa and Alyssa Wise invites the community to engage with the complex question of what constitutes “rigorous research” in learning analytics (LA). LA is a highly interdisciplinary field drawing on machine-learning techniques and statistical analysis, as well as qualitative approaches, and the papers submitted to and published by JLA are diverse. While this breadth of work and orientations to LA are enormous assets to the field, they also create challenges for defining and applying common standards of rigour across multiple disciplinary norms. Extending the conversation begun in the 2019 editorial of JLA 6(3), this session will examine (a) indicators of quality that are significant in particular research traditions, (b) indicators of quality that are common across them, and (c) indicators of quality that are distinctive to the field of LA as a confluence of research traditions. This session will comprise brief presentations from 6 expert panellists in the field followed by a lively discussion among them with Q & A. This webinar will lay a foundation for future participatory sessions inviting conversation across the community more broadly.


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Alyssa Wise is Associate Professor of Learning Sciences and Educational Technology at New York University and the Director of LEARN, NYU's pioneering university-wide Learning Analytics Research Network. She holds a Ph.D. in Learning Sciences and an M.S. in Instructional Systems Technology from Indiana University and a B.S. in Chemistry from Yale University. Dr. Wise’s research is situated at the intersection of learning and educational data sciences, focusing on the design of learning analytics systems that are theoretically grounded, computationally robust, and pedagogically useful for informing teaching and learning. Dr. Wise serves as Co-Editor-in-Chief of the Journal of Learning Analytics is a Co-Editor of the Handbook of Learning Analytics.


Xavier Ochoa is Assistant Professor of Learning Analytics in the Department of Administration, Leadership, and Technology of the Steinhardt School of Culture, Education, and Human Development. He is also a member of the Learning Analytics Research Network (LEARN) at NYU. Xavier holds a Ph.D. in Computer Science from the University of Leuven (Belgium), an M.Sc. in Applied Computer Science Sciences from the Vrije Universiteit Brussels (Belgium) and a B.S. in Computer Science from Escuela Superior PolitÚcnica del Litoral (Ecuador).


Simon Knight is a senior lecturer in the University of Technology Sydney, Faculty of Transdisciplinary Innovation. Prior to moving to UTS he completed his PhD in learning analytics at the Open University, UK. Dr Knight is a qualified high-school teacher, with a Bachelors in psychology and philosophy, and Masters degrees in philosophy of education, and research methods in education.




Perspectives on Rigor in Statistical Approaches to Learning Analytics Research


Sandra Milligan is Director and Enterprise Professor at the Assessment Research Centre at the Melbourne Graduate School of Education, University of Melbourne, Australia. Her research areas include assessment, micro-credentialing and using big data to support assessment of higher-order learning skills.


Philip Winne is Distinguished Professor of Educational Psychology and former Canada Research Chair in Self-Regulated Learning and Learning Technologies at Simon Fraser University, Canada. His research interests include self regulated learning, metacognition, motivation and use of adaptive software to promote self regulation.


Perspectives on Rigor in Computational Approaches to Learning Analytics Research


Dragan Ga╣eviŠ is Professor of Learning Analytics in the Faculty of Information Technology at Monash University, Australia and former President of Society for Learning Analytics Research. His research interests include developing computational methods that can advance understanding of information seeking, sense-making, and self-regulated and social learning.


Jelena JovanoviŠ is Associate Professor in the Department of Software Engineering, University of Belgrade, Serbia. Her research interests include the use of statistical / machine learning methods and techniques, semantic technologies, and other computational approaches that combine human and machine intelligence to improve understanding of the learning process.


Perspectives on Rigor in Qualitative Approaches to Learning Analytics Research


Kristine Lund is a Research Engineer in the Human and Social Sciences and the vice-director of the Language Sciences laboratory Interactions, Corpus, Learning and Representations at the University of Lyon, France. Dr. Lund’s research interests include the multimodal co-construction of complex knowledge in goal-oriented computer-mediated human interaction and interactive phenomena such as explanation and argumentation in problem solving situations.


Bodong Chen is Associate Professor in Learning Technologies and the Bonnie Westby Huebner Endowed Chair in Education & Technology at the University of Minnesota. He is also the co-director of Learning Informatics Lab of the College of Education and Human Development. His research interests include the study of computer supported collaborative learning, online learning, knowledge analytics and network analytics.