Mohammed Saqr

Mohammed Saqr is an associate professor at the UEF School of Computing’s lab of learning analytics. He holds a PhD in learning analytics from Stockholm University and previously held a post-doc at University of Paris. His research focuses on interdisciplinary areas including learning analytics, big data, network science, and science of science and medicine. He has received several awards for his thesis and research, including the best thesis award and University of Michigan Office of Academic Innovation fellowship. He has also secured funding from institutions like Swedish Research Council and Academy of Finland for his work in Idiographic learning analytics. Mohammed serves as an academic editor for four prestigious journals, has organized and contributed to international conferences, and delivered several invited keynotes. He collaborates with researchers in Finland, Spain, Sweden, Germany, Australia, France, Switzerland, UK, and USA.

Mohammed Saqr interests lie at the crossroads of analytics, methodologies, open science, and education. His goal is to challenge the limits of methodological advancements and unleash the power of big data in education, computer science, and life sciences. The primary areas of his research can be highlighted as follows, with accompanying examples:

Mohammed has extensive experience with network analysis and its applications in education. He has published on the use of networks in collaborative learning, problem-based learning, language learning, and computer science education. His research includes finding the best methods for network analysis, conducting empirical investigations on centrality measures, and performing meta-analyses to determine which measures best capture students’ achievement. He also works on network-based interventions, modeling knowledge diffusion and social contagion, modeling conflict and robustness in networks, and understanding group influence on collaborative dynamics. He has also contributed to best practices in network analysis and addressing the challenges facing the field.

Mohammed is also interested in temporal networks, which is a new paradigm in education. He has published on the applications of temporal networks in education and how they can be used to predict students’ performance and reveal the dynamics of collaborative learning. His latest work explores the use of temporal networks in collaborative learning, temporal network centralities, and knowledge construction pathways. Additionally, Mohammed is studying the complexity of human behavior using psychological networks. He is exploring how self-regulated processes work in language learning and collaborative learning and modeling the interplay between self-regulated learning monitoring events and idiographic processes.

Longitudinal research is a challenge but provides valuable insights into behavior over time. Mohammed has tackled this challenge using large datasets spanning a full program and has explored the longitudinal trajectories of engagement, the evolution of collaborative roles, learning strategies, and their transfer across courses. Mohammed also has worked on improving predictive learning analytics, studied the accuracy of predictive models with regards to individual differences, and used techniques such as sequence mining, process mining, latent class analysis, and graphical Gaussian models.
Lastly, Mohammed has a passion for scientometrics and bibliometrics and has mapped the landscape of research in areas such as computational thinking, games, education technology, and computer science education research. He is also interested in altmetrics and has studied the public reception of research papers in the field of computer science education

For an updated list of Mohammed Saqr’s publications, please see saqr.me