MOHAMMED SAQR

Associate Professor

Head of Learning Analytics Unit

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, Serbia, Sweden, Norway, Netherlands, Germany, Australia, Switzerland, UK, and USA. In total, he collaborated with more than 170 researchers from 22 countries.

Research Focus

Mohammed’s research interests have grown into several threads at the intersection of computer science, analytics, and education. Such threads of research revolve around six main intertwined topics which include: methodological innovation, person-centered learning analytics e.g., how can we use data to understand and support individual learners? temporal aspects of learning e.g., How does learning unfold over time? network science e.g., How can we use network analysis to understand learning interactions? Idiographic learning analytics: How can we harness individual variation to improve learning predictions? Replicability and reproducibility and science of science: How can we ensure that our research is rigorous and reproducible?

Person-centered learning analytics

Mohammed’s main focus now is on the project TOPEILA (which is Funded by the Finnish Academy Research). TOPEILA stands for: Towards precision education: Idiographic learning analytics (TOPEILA) and aims to collect data from individual students, analyze the data separately and deliver insights or recommendations based on self-data using explainable artificial intelligence. This is a departure from previous methods that collect data from many people and deliver insights according to the norms of “others”. When the data is very specific, it is expected to deliver specific insights, relevant recommendations.

In a paper he co-authored with colleagues, they used a large dataset they found that students’ internal conditions (e.g., knowledge, self-regulation, and motivation) explained most of the variance in students’ performance where a low proportion of variance was explained by the behavior-based indicators ¹⁰. Recently, he took this approach a step further and published a paper that harnesses the idiographic person-specific variance to improve predictive learning analytics in a single authored paper in the British Journal of Education Technology ¹¹. Several other projects are in the pipeline with more emphasis on individual differences methods as well as a special issue in Learning and Individual Differences.

Temporal aspects of learning

Several threads of his research address learning temporality from different angles and temporal levels. On a longitudinal level, he have several papers that analyzed multiple years of education using novel and advanced methods e.g., sequence analysis, Hidden Markov models and survival analysis which addressed engagement ⁸, collaborative roles ⁹ and learning strategies learning strategies ¹². In another related study with a focus on engagement in the initial courses, he have shown that early disengagement is a critical indicator that need to be promptly addressed on the program-level ¹³. The methods and approach used in these papers lies at the forefront of methodological advances.

On another temporal and fine-grained level, he introduced the psychological networks into the field of learning; he used time-series networks to study the within-person temporal unfolding of self-regulation in a single-student paper using data collected on daily basis (Ecological Momentary Assessment) ¹⁴. Recently, he used multimodal learning analytics to study the within-person second-by-second unfolding of self-regulation ^6,7. With a focus on the emotional aspects of self-regulation and how emotion unfolds in learning, he introduced multi-channel sequence mining in a collaborative paper published in the British Journal of Education Psychology ⁵. Other papers in his research address the temporal process of learning and how it unfolds over a full course e.g., ¹⁵ or the sequence of students interactions while learning programming ¹⁶.

In the same token, he used temporal networks (which was the first time it was introduced) to demonstrate daily monitoring of students’ collaborative learning interactions bridging the gap between relational aspects of learning and time, the latest of these paper was published in the British Journal of Education Technology ^1–3.

Network Science

Networks are at the heart of his research interests, and he am among the most proliferative network researchers in the learning community. his research sought to understand interactions in collaborative learning ^17,18, guide teachers ¹⁹, predict performance ¹⁷ and understand the role of long-term ties in shaping students’ performance ²⁰. Another thread in his network research focuses on understanding social dynamics e.g., how a group size affect students’ interactions ²¹, what makes an interactive group successful ²¹, and what are the differences between students’ interactions in a learning settings and social settings ²².

My recent research focuses on novel concepts such as robustness and rich clubs in collaborative networks, where he show that teachers can be a driving factor behind the formation of rich clubs of well-connected few and less connected many in some cases and can contribute to a more collaborative and sustainable process where every student is included ²³. Other novel concepts that he studies are diffusion which he showed how diffusion models borrowed from contagion epidemiology can be used to study diffusion of ideas ²⁴, the concept has been extended and verified in a massive study that was recently published in the prestigious International Journal of Computer-Supported Collaborative Learning ⁴.

My network research includes traditional social network analysis, inferential network models such as Exponential Random Graph Models e.g., ²², temporal networks ¹, psychological networks ^14,25. An important thread in his network research is his contribution to the methods of network analysis. In a study recently published in International Journal of Computer-Supported Collaborative Learning ²⁶. he studied which methods and measures allow researchers to capture the social and participation dimension of collaborative learning. he co-developed with colleagues a preliminary guidelines aiming at better reporting network research which aims at improving reproducibility ²⁷ as well as a synthesis of issues that faces the field of networks in education ²⁸

My work also includes several important meta-analysis to investigate which centrality measures indicate a collaborative pattern of learning e.g., ^29,30. Both of the aforementioned papers were published in prestigious journals i.e., Education Research Review and Journal of Learning analytics. Similarly, he synthesized five decades of network analysis research ³¹.

Replicability and reproducibility

A fundamental principle of science is the possibility to test, verify, or refute the claims or conclusions reported by other researchers. Reproducible research is more amenable to generalization, emboldens the credibility of research findings, and can translate into a real-life impact. Therefore, an important part of his research is to do large scale replication studies and meta-analysis. In his first replication which appeared the Journal of Learning Analytics, he replicate research findings about centrality measures in education using data from several thousands of students using the novel single paper meta-analysis approach ²⁹. In another study, he replicated and put to the test common findings from the learning analytics field in a large study which was published in Studies in Higher Education ³². In another study in Educational Research Review, he performed a detailed systematic review and meta-analysis of all research about centrality measures and academic achievement ³⁰.
I also contributed to several reviews that assesses the current status of research on e.g., Epistemic Learning Analysis ³³, self-regulation and learning analytics ³⁴ and virtual laboratories and learning analytics ³⁵.

Science of science

Understanding scientific research is one of his passions that helps understand the research traditions, scholarly trends, and future direction of research. he engaged in several interdisciplinary projects to map scientific research using bibliometric methods. For instance, he mapped – with colleagues– the whole field of education technology in a paper published in Computers and Human Behaviour ³⁶. he also analyzed research published on the topic of computational thinking published in ACM Transactions on Computing Education ³⁷, game concepts in education published in Computers and Education ³⁸ as well as several others and a whole book that mapped the past, present and future of computing education research ^31,39–42.

Methodological innovation

References

Saqr, M. & Nouri, J. High resolution temporal network analysis to understand and improve collaborative learning. in Proceedings of the Tenth International Conference on Learning Analytics & Knowledge 314–319 (Association for Computing Machinery, 2020). doi:10.1145/3375462.3375501.
Saqr, M. & Peeters, W. Temporal networks in collaborative learning: A case study. Br. J. Educ. Technol. (2022) doi:10.1111/bjet.13187.
Saqr, M. & López-Pernas, S. Instant or Distant: A Temporal Network Tale of Two Interaction Platforms and Their Influence on Collaboration. in Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption 594–600 (Springer International Publishing, 2022). doi:10.1007/978-3-031-16290-9_55.
Saqr, M. & López-Pernas, S. Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study. Int. J. Comput. Support. Collab. Learn. 16, 441–483 (2021).
Törmänen, T., Järvenoja, H., Saqr, M., Malmberg, J. & Järvelä, S. Affective states and regulation of learning during socio-emotional interactions in secondary school collaborative groups. Br. J. Educ. Psychol. e12525 (2022) doi:10.1111/bjep.12525.
Malmberg, J., Saqr, M., Järvenoja, H. & Järvelä, S. How the monitoring events of individual students are associated with phases of regulation. J. learn. anal. 9, 77–92 (2022).
Malmberg, J. et al. Modeling the complex interplay between monitoring events for regulated learning with psychological networks. in The Multimodal Learning Analytics Handbook 79–104 (Springer International Publishing, 2022). doi:10.1007/978-3-031-08076-0_4.
Saqr, M. & López-Pernas, S. The longitudinal trajectories of online engagement over a full program. Comput. Educ. 175, 104325 (2021).
Saqr, M. & López-Pernas, S. How CSCL roles emerge, persist, transition, and evolve over time: A four-year longitudinal study. Comput. Educ. 189, 104581 (2022).
Jovanović, J., Saqr, M., Joksimović, S. & Gašević, D. Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success. Comput. Educ. 172, 104251 (2021).
Saqr, M. Modelling within‐person idiographic variance could help explain and individualize learning. British Journal of Educational Technology Preprint at https://doi.org/10.1111/bjet.13309 (2023).
Saqr, M., López-Pernas, S., Jovanović, J. & Gašević, D. Intense, turbulent, or wallowing in the mire: A longitudinal study of cross-course online tactics, strategies, and trajectories. The Internet and Higher Education 57, 100902 (2023).
Saqr, M. & López-Pernas, S. The dire cost of early disengagement: A four-year learning analytics study over a full program. vol. 2 (Springer International Publishing, 2021).
Saqr, M. & Lopez-Pernas, S. Idiographic learning analytics: A definition and a case study. in 2021 International Conference on Advanced Learning Technologies (ICALT) 163–165 (IEEE, 2021). doi:10.1109/ICALT52272.2021.00056.
Peeters, W., Saqr, M. & Viberg, O. Applying learning analytics to map students’ self-regulated learning tactics in an academic writing course. in Proceedings of the 28th International Conference on Computers in Education vol. 1 245–254 (2020).
López-Pernas, S., Saqr, M. & Viberg, O. Putting it all together: Combining learning analytics methods and data sources to understand students’ approaches to learning programming. Sustain. Sci. Pract. Policy 13, 4825 (2021).
Saqr, M., Fors, U. & Nouri, J. Using social network analysis to understand online Problem-Based Learning and predict performance. PLoS One 13, e0203590 (2018).
Saqr, M. & Alamro, A. The role of social network analysis as a learning analytics tool in online problem based learning. BMC Med. Educ. 19, 160 (2019).
Saqr, M., Fors, U. & Tedre, M. How the study of online collaborative learning can guide teachers and predict students’ performance in a medical course. BMC Med. Educ. 18, 24 (2018).
Saqr, M. et al. How Networking and Social Capital Influence Performance: The Role of Long-Term Ties. in Lecture Notes in Networks and Systems (eds. Antonyuk, A. & Basov, N.) vol. 181 335–346 (Springer International Publishing, 2021).
Saqr, M., Nouri, J., Vartiainen, H. & Malmberg, J. What makes an online problem-based group successful? A learning analytics study using social network analysis. BMC Med. Educ. 20, 80 (2020).
Saqr, M. & Montero, C. S. Learning and social networks-similarities, differences and impact. in Proceedings – IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020 135–139 (2020). doi:10.1109/ICALT49669.2020.00047.
Saqr, M., Nouri, J., Vartiainen, H. & Tedre, M. Robustness and rich clubs in collaborative learning groups: a learning analytics study using network science. Sci. Rep. 10, 14445 (2020).
Saqr, M. & Viberg, O. Using diffusion network analytics to examine and support knowledge construction in cscl settings. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (eds. Alario-Hoyos, C., Rodríguez-Triana, M. J., Scheffe, l. M., Arnedillo-Sánchez, I. & S.m., D.) vol. 12315 LNCS 158–172 (Springer, C, 2020).
Saqr, M., Viberg, O. & Peteers, W. Using psychological networks to reveal the interplay between foreign language students’ self-regulated learning tactics. in STELLA2020 Proceedings vol. 2828 12–23 (2021).
Saqr, M., Viberg, O. & Vartiainen, H. Capturing the participation and social dimensions of computer-supported collaborative learning through social network analysis: which method and measures matter? International Journal of Computer-Supported Collaborative Learning 15, 227–248 (2020).
Poquet, O., Saqr, M. & Chen, B. Recommendations for Network Research in Learning Analytics: To Open a Conversation. in Proceedings of the NetSciLA21 workshop (2021).
Saqr, M., López-Pernas, S., Hernández-García, Á., Conde, M. Á. & Poquet, O. Networks and learning analytics: Addressing educational challenges. in Companion Proceedings of the 12th International Conference on Learning Analytics & Knowledge (LAK22) 178–181 (2022).
Saqr, M. & López-Pernas, S. The curious case of centrality measures: A large-scale empirical investigation. J. learn. anal. 9, 13–31 (2022).
Saqr, M., Elmoazen, R., Tedre, M., López-Pernas, S. & Hirsto, L. How well centrality measures capture student achievement in computer-supported collaborative learning? – A systematic review and meta-analysis. Educational Research Review 35, 100437 (2022).
Saqr, M., Poquet, O. & Lopez-Pernas, S. Networks in education: A travelogue through five decades. IEEE Access 1–1 (2022) doi:10.1109/access.2022.3159674.
Saqr, M., Jovanovic, J., Viberg, O. & Gašević, D. Is there order in the mess? A single paper meta-analysis approach to identification of predictors of success in learning analytics. Stud. High. Educ. 1–22 (2022) doi:10.1080/03075079.2022.2061450.
Elmoazen, R., Saqr, M., Tedre, M. & Hirsto, L. A systematic literature review of empirical research on epistemic network analysis in education. IEEE Access 10, 17330–17348 (2022).
Heikkinen, S., Saqr, M., Malmberg, J. & Tedre, M. Supporting self-regulated learning with learning analytics interventions – a systematic literature review. Educ. Inf. Technol. (2022) doi:10.1007/s10639-022-11281-4.
Elmoazen, R., Saqr, M., Khalil, M. & Wasson, B. Learning analytics in virtual laboratories: a systematic literature review of empirical research. Smart Learn. Environ. 10, 1–20 (2023).
Valtonen, T. et al. The nature and building blocks of educational technology research. Comput. Human Behav. 128, 107123 (2022).
Saqr, M., Ng, K., Sunday, O. S. & Matti, T. People, Ideas, Milestones: A Scientometric Study of Computational Thinking. ACM Transactions on Computing Education 21, 1–17 (2021).
Schöbel, S., Saqr, M. & Janson, A. Two decades of game concepts in digital learning environments – A bibliometric study and research agenda. Comput. Educ. 173, 104296 (2021).
Ismail, I. I. & Saqr, M. A Quantitative Synthesis of Eight Decades of Global Multiple Sclerosis Research Using Bibliometrics. Front. Neurol. 13, 845539 (2022).
Apiola, M., Saqr, M., López-Pernas, S. & Tedre, M. Computing Education Research Compiled: Keyword Trends, Building Blocks, Creators, and Dissemination. IEEE Access 10, 27041–27068 (undefined 2022).
Tyni, J. et al. Games and Rewards: A Scientometric Study of Rewards in Educational and Serious Games. IEEE Access 10, 31578–31585 (undefined 2022).

42. López-Pernas, Saqr, M. & Apiola, M. Scientometrics: a concise introduction and a detailed methodology for the mapping of the scientific field of computing education. in Past, Present and Future of Computing Education Research (eds. Apiola, M., López-Pernas, S. & Saqr, M.) in–press (Springer, 2022).

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