L’équipe TALENT a eu le plaisir d’accueillir Mohammed Saqr et Sasha Poquet qui ont présenté leurs travaux à l’IRIT le 28 juin 2024.

1 – Learning analytics for social interactions in digital learning

Learning analytics, defined as the collection, analysis, and measurement of data collected in learning settings, have been a part of the scholarly discourse now for over a decade. Against this backdrop, learning analytics researchers aspire to demonstrate how analysis of learner traces can impact teaching and learning. Addressing such a call for impact today is more feasible in some areas of LA, such as predictive modelling, writing analytics, and analytics of self-regulation processes. However, some areas of LA have not advanced to offer the trusted insights. Among areas in need of refinement and rigor in learning analytics is the analysis of interpersonal activity in digital learning. Interpersonal activity among students when they learn, captured through social interactions, is known to contribute to the quality of learning and student experiences. The talk will offer two examples where analytics of learner granular behaviour can inform teaching practice on supporting interpersonal interactions in learning scenarios.

Presenter: Sasha POQUET

Oleksandra Poquet (Sasha Poquet) is a tenure-track professor in learning analytics at the School of Social Sciences and Technology the Technical Univerity of Munich. Prof. Dr Poquet leads a LEAPS research group that studies LEarning Analytics and Practices in Systems. Prof. Dr Poquet is a core member of the Munich Data Science Institute and a thematic lead on the use of process data to inform learning at the TUM’s EdTech Centre. She is also an external affiliate of the Centre for Change and Complexity in Learning, University of South Australia. From 2017-2021, she has served on the Executive Committee at the Society for Learning Analytics Research (SoLAR), as its Vice President in 2019-2021. Dr Poquet has been a convener of the series of workshops on networks in learning analytics and has edited a special issue on this topic for the Journal of Learning Analytics.

Website: https://www.edu.sot.tum.de/leaps/about/prof-dr-oleksandra-poquet/

2 – Precision Education: Mapping the person where the learning process takes place

The emphasis on the “self” or the “person” has been a central concept in several learning theories, methodologies, and approaches for decades and the literature is awash with notions of personalized, adaptive, and student-centered education. Nonetheless, research is commonly conducted using variable-centered methods where data is collected from a “group of others” to devise the average learner’s behavior. Yet, the average is barely representative of the breadth of human diversity. On the contrary, person-specific (or idiographic) methods aim to accurately and precisely model the individual person, create person-specific models, and devise unique parameters for each individual. Modeling the person —where the learning process takes place— paves the way for personalized, accurate, and precise education. The recent advancement in such methods, the expanding repertoire of tools and data collection methods could help answer the pressing question of what works for whom, and when? This presentation discusses the shortcomings of current methods, shows where and when they fall short, and introduces idiographic methods with recent literature.

Presenter: Mohammed SAQR

Mohammed Saqr is an Associate Professor of Computer Science at the School of Computing, University of Eastern Finland and leads UEF’s Learning Analytics Unit. Saqr has a PhD in Learning Analytics from Stockholm University, Sweden, followed by a postdoctoral position at Université Paris Cité, France. Later, he earned the title of Docent in Learning Analytics from the University of Oulu, Finland. His scholarly work is diverse and interdisciplinary and has received several recognitions (e.g., best papers at LAK, ICCE, SITE, TEEM). Mohammed also received the most competitive Swedish and Finnish funding and serves on the board of several academic journals. His current work is focused –among others– on advancing solutions for precision artificial intelligence.

Webiste: https://saqr.me/