Mohammed Saqr

Associate Professor of Computer Science

Mohammed Saqr is an Academy of Finland researcher who leads the lab of learning analytics at University of Eastern Finland, School of Computing which was, according to Scopus, Europe’s most productive learning analytics lab during the last five years (2019-2023). Mohammed had a PhD in learning analytics from Stockholm University, Sweden, before joining UEF in Finland, Mohammed had a postdoc at University of Paris, France, and holds the title of Docent in learning analytics from the University of Oulu, Finland. Mohammed’s research is interdisciplinary including learning analytics, AI, big data, network science, science of science and medicine. Mohammed has several awards, e.g., the PhD was awarded the best thesis, he also got several international research awards (e.g., best papers), obtained the University of Michigan Office of Academic Innovation fellowship. In 2023, the Society of Learning Analytics Research (SOLAR) awarded Mohammed Europe Emerging Scholar Award for the "noteworthy research leading to significant knowledge and understanding of learning analytics and the impact of research on learning analytics application, adoption, and professional development in Europe. Mohammed obtained funding from prestigious institutions: Academy of Finland (as PI) for Idiographic learning analytic and Swedish Research Council (as Co-PI) as well as several other grants. Mohammed is on the editorial board of several prestigious academic journals e.g., Transactions of Learning Technologies, British journal of Education Technologies and Plos One. Mohammed also organised and contributed to several international conferences and presented several invited keynotes. Mohammed’s current collaboration network includes Finland, Spain, Sweden, Germany, Serbia,Australia, France, Switzerland, UK, and USA and The Netherlands.

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Research Interests

Learning Analytics Artificial Intelligence Network Science Social Network Analysis Educational data mining Longitudinal methods Medical education Bibliometrics CSCL Neurology and Psychiatry Open Science Idiopgraphic & within-person methods

In the grand scheme of things, my interests are interdisciplinary at the intersection of analytics, novel methods, open science and education. My focus is to push the boundaries of methodological advances and unlock the power of big data to bring new insights. In so doing, I explore the temporality, individual differences and heterogeneity as an overarching analytical framework for personalized, equitable and debiasing science. As such, I worked with machine learning, mixture models, network science, sequence analysis, process mining, Markov models, psychological networks, temporal networks, meta-analyses, topic modelling and scientometrics One of my main areas of research centers around networks and network analysis. I published about networks in collaborative learning, in problem-based learning, in language learning and in computer science education. I also worked on finding best methods for network analysis, performed a large scale empirical investigation of centrality measures and did a meta-analysis on centralities to find out which measures capture students’ achievement. Other threads of network research include intervention based on network analysis, modeling diffusion and social contagion of knowledge, modeling conflict and robustness in networks and group influence on collaborative dynamics and success factors.I also contributed to guidelines on best practices in network analysis, finding future agendas, and addressing the challenges that face network analysis in education.... Read more

Selected publications

Saqr M., López-Pernas S., Hrastinski S., Helske S. (2023). The longitudinal association between engagement and achievement varies by time, students’ profiles, and achievement state: A full program study. Computers and Education (in-press), art. no. 104787. doi: 10.1016/j.compedu.2023.104787.

Saqr M., López-Pernas S. (2022). How CSCL roles emerge, persist, transition, and evolve over time: A four-year longitudinal study. Computers and Education, vol. 189, art. no. 104581. doi: 10.1016/j.compedu.2022.104581.

Saqr M., López-Pernas S. (2021). The longitudinal trajectories of online engagement over a full program. Computers and Education, vol. 175, art. no. 104325. doi: 10.1016/j.compedu.2021.104325.

Jovanović J., Saqr M., Joksimović S., Gašević D. (2021). Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success. Computers and Education, vol. 172, art. no. 104251. doi: 10.1016/j.compedu.2021.104251.

Schöbel S., Saqr M., Janson A. (2021). Two decades of game concepts in digital learning environments – A bibliometric study and research agenda. Computers and Education, vol. 173, art. no. 104296. doi: 10.1016/j.compedu.2021.104296.

Saqr M., López-Pernas S., Jovanovic J., Gasevic. G (2023). Intense, turbulent, or wallowing in the mire: A longitudinal study of cross-course online tactics, strategies, and trajectories. The Internet and Higher Education, art. no. 100902. doi: 10.1016/j.iheduc.2022.100902.

Saqr M., López-Pernas S. (2023). The temporal dynamics of online problem-based learning: Why and when sequence matters. International Journal of Computer-Supported Collaborative Learning (in-press). doi: 10.1007/s11412-023-09385-1.

Saqr M., Elmoazen R., Tedre M., López-Pernas S., Hirsto L. (2022). How well centrality measures capture student achievement in computer-supported collaborative learning? – A systematic review and meta-analysis. Educational Research Review, vol. 35, art. no. 100437. doi: 10.1016/j.edurev.2022.100437.

Saqr M., López-Pernas S. (2021). Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study. International Journal of Computer-Supported Collaborative Learning, vol. 16(4), pp. 441-483. doi: 10.1007/s11412-021-09356-4.

Saqr M. (2023). Modeling within-person idiographic variance could help explain and individualize learning. The British Journal of Educational Technology (in-press). doi: 10.1111/bjet.13309.

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Contact me

If you have a research idea, want to collaborate, thinking of a funding proposal, a PhD or a master supervision. You are welcome to connet with me, send me at e-mail at mohammed.saqr@uef.fi.