Learning Analytics

The learning analytics lab was, according to Scopus, Europe’s most productive learning analytics lab in 2021. The lab collaborates widely with world most advanced labs (e.g., Sweden, France, Germany, Denmark, Australia, UK, Australia and Spain). Our work includes a wide array of projects that covers higher education, schools and vocational education.
Learning analytics has emerged as a research endeavor to explore the opportunities of making sense of the big amounts of data available, in order to understand learners and their learning environments  to optimize the learning process through developing actionable insights. Learning analytics was motivated by the availability of massive data records about learners as well as the exponential surge in computing capabilities coupled with bourgeoning artificial intelligence models. Through learning analytics methods, data can be used to offer  customized feedback and corrective strategies customized to the student’s level of activities, goals and performance levels . Since, learning analytics is an iterative process, knowing more about students would help them perform better, and knowing more about which support mechanism has helped will help improve the insights and the support engine.

Reasearch  Fields

  • Learning and social networks in education
  • Temporal methods of learning analytics
  • Engagement trajectories
  • Collaborative learning groups
  • AI in education
  • Understanding science and the science of  scinece

Members

 Mohammed Saqr

As a computer science researcher, Mohammed’s research interests are interdisciplinary and include learning analytics, network science and analysis, education, computer science, assessment, neurology, and psychiatry. He did his PhD at Stockholm University (Sweden) in collaborative predictive learning analytics using network indicators to find a better understanding of the learning process and success. He also works on the temporality of collaborative networks, engagement in small groups, statistical network modeling, resilience, and robustness of learning and social networks. Other research work includes psychological networks, migraine, depression, cerebrovascular reactivity.

Tapani Toivonen

Tapani’s research focuses on explainable AI (XAI), educational data mining (EDM), and AI in education. Tapani is especially interested in transparency of predictive models and how to develop models that would be both accurate and transparent. Tapani is also interested in human — algorithm collaboration in data mining, of which Tapani did his PhD.

Sonsoles López-Pernas

Sonsoles has a remarkable expertise in solving complex problems of massive educational data, and therefore, she collaborates with various departments and schools at UEF in learning analytics, data science and computing education. Her extended network, diverse skills and background has established her as the swiss army knife of all projects that she works on. In her last projects, she turned messy data into beautiful visualizations, delivered deeply meaningful insights and advanced methodological applications.

Ramy Elmoazen

Ramy’s research interest is developing computer-supported collaborative learning and learning analytics including social network and epistemic network analyses in medical education. He is also interested in biostatistics and bioinformatics and their application. He is currently working on the project of European Network for Virtual lab & Interactive SImulated ONline learning (ENVISION) which aims to develop e-learning modules and enhance Bioscience students’ meta-skills through virtual learning collaboration.

Sami Heikkinen

Sami has 15 years of experience in online learning. His research focuses on learning analytics approaches, helping both teachers and students to improve learning processes. His PhD focus on self-regulated learning, learning analytics interventions, and process mining. Other interests include university-industry interaction and design thinking

Rhythm Bhatia

Rhythm is a PhD student at the University of Eastern Finland. Here, she is working on “From big data to person-based learning analytics: exploring the opportunities and challenges” under the guidance of Prof. Matti Tedre, Dr Mohammed Saqr and Dr Sonsoles López Pernas. She is a member of the edTech research group http://www.uef.fi/edtech  and http://impdet.org.  She has also worked on cryptocurrency, life expectancy, stress analysis, music/speech segmentation, bird call generation and segmentation, bird call clustering and big data analytics. Link to her portfolio:

https://rhythmbhatia.com/ .

Maria Miiro Kafuko

Maria Miiro Kafuko is a PhD student at the University of Eastern Finland. She received her Bachelors degree in Business Computing and Master’s degree in Information Technology at Makerere University in Uganda. At UEF, she is working on “Facilitating collaborative group work at scale using Learning Analytics” being supervised by Dr Mohammed Saqr, Dr Jarkko Suhonen, A. Professor Joseph Kizito Bada and Professor Markku Tukiainen. She has done research on Mobile learning in a developing country context and will delve into learning analytics and social network Analysis in collaborative learning groups in her PhD study. She has a passion for educational technologies as well as audio-visual instructional materials and their use in the classroom to aid learning and engage students.

Recent Projects

OAHOT – Utilisation of Learning Analytics Data to Support Self-Regulated Learning During Various Phases of Study Paths
ENVISION- European Network for Virtual lab & Interactive SImulated ONline learning 2027

 

Publications

Publication Lists 2022

  1. I.I. Ismail, M. Saqr, A quantitative synthesis of eight decades of global multiple sclerosis research using bibliometrics. Frontiers in Neurology, In press.
  2. S. López-Pernas, M. Saqr, A. Gordillo, E. Barra, A learning analytics perspective on educational escape roomsInteractive Learning Environments, 2022.
  3. M. Saqr, W. Peeters, Temporal networks in collaborative learning: A case studyBritish Journal of Educational Technology, 2022.
  4. R. Elmoazen, M. Saqr, M. Tedre, L. Hirsto, A systematic literature review of empirical research on epistemic network analysis in educationIEEE Access, 2022.
  5. M. Saqr, R. Elmoazen, M. Tedre, S. López-Pernas, L. Hirsto, How well centrality measures capture student achievement in computer-supported collaborative learning? – A systematic review and meta-analysisEducational Research Review, vol. 35, 2022.
  6. M. Saqr, S. López-Pernas, The curious case of centrality measures: A large-scale empirical investigation. Journal of Learning Analytics, In press.
  7. M. Nissinen, E. Silvennoinen, M. Saqr, Monivalintakysymykset oikeustieteellisen alan yhteisvalintakokeessa – Hitti vai huti? Edilex, 2022.

Publication Lists 2021

  1. Ofir, Z., Tedre, M. Saqr, M.,  Kliukina, S. End of programme evaluation of Sida’s support to the World Academy of Science (TWAS), 2017-2021.
  2. López-Pernas, S., M. Saqr, Bringing synchrony and clarity to complex multi-channel data: A learning analytic study in programming educationIEEE Access, vol.9, pp. 166531-166541, 2021.
  3. M. Saqr, S. López-Pernas, Modelling diffusion in computer-supported collaborative learning: A large scale learning analytics studyInternational Journal of Computer-Supported Collaborative Learning, 2021.
  4. T. Valtonen, S. López-Pernas, M. Saqr, H. Vartiainen, E.T. Sointu, M. Tedre, The nature and building blocks of educational technology researchComputers in Human Behavior, vol. 128, 2021.
  5. M. Apiola, M. Tedre, S. Lòpez-Pernas, M. Saqr, M. Daniels, A. Pears, A scientometric journey through the FIE bookshelf: 1982-2020Proceedings of the 2021 IEEE Frontiers in Education (FIE) Conference, IEEE, 2021.
  6. M. Apiola, M. Tedre, S. Lòpez-Pernas, M. Saqr, M. Daniels, A. Pears, A scientometric journey through the FIE bookshelf: 1982-2020Proceedings of the 2021 IEEE Frontiers in Education (FIE) Conference, IEEE, 2021.
  7. M. Saqr, S. López-Pernas, The dire cost of early disengagement: A four-year learning analytics study over a full program. In De Laet T., Klemke R., Alario-Hoyos C., Hilliger I., Ortega-Arranz A. (eds), Technology-Enhanced Learning for a Free, Safe, and Sustainable World. EC-TEL 2021. Lecture Notes in Computer Science, vol 12884. Springer, Cham, 2021.
  8. M. Saqr, S. López-Pernas, The longitudinal trajectories of online engagement over a full programComputers & Education, vol. 175, 2021.
  9. M. Saqr, S. López-Pernas, Towards self-big dataInternational Journal of Health Sciences, vol. 15, no. 5, pp. 1-2, 2021.
  10. M. Saqr, S. López-Pernas, Idiographic learning analytics: A definition and case studyProceedings of the 2021 International Conference on Advanced Learning Technologies (pp. 163-165), 2021
  11. S. Schöbel, M. Saqr, A. Janson, Two decades of game concepts in digital learning environments – A bibliometric study and research agendaComputers & Education, 2021.
  12. M. Bermo, M. Saqr, H. Hoffman, D. Patterson, S. Sharar, S. Minoshima, D.H. Lewis, Utility of SPECT functional neuroimaging of brainFrontiers in Psychiatry, 2021.
  13. J. Jovanović, M. Saqr, S. Joksimović, D. GaševićStudents matter the most in learning analytics: The effect of internal and instructional conditions in predicting academic successComputers & Education, vol. 172, 2021.
  14. O. Poquet, B. Chen, M. Saqr, T. Hecking, Using network science in learning analytics: Building bridges towards a common agendaProceedings of the NetSciLA21 workshop, 2021.
  15. O. Poquet, M. Saqr, B. Chen, Recommendations for network research in learning analytics: To open a conversationProceedings of the NetSciLA21 workshop, 2021.
  16. S. López-Pernas, M. Saqr, O. Viberg, Putting it all together: Combining learning analytics methods and data sources to understand students’ approaches to learning programmingSustainability, vol. 13, no. 9, 2021.
  17. M. Saqr, O. Viberg, W. Peeters, Using psychological networks to reveal the interplay between foreign language students’ self-regulated learning tacticsProceedings of the 2020 STELLA Symposium, 2021
  18. M. Saqr, S. López-Pernas, Idiographic learning analytics: A single student (N=1) approachCompanion Proceedings of the 11th International Conference on Learning Analytics & Knowledge (LAK21), 2021.
  19. M. Saqr, K. Ng, S.S. Oyelere, M. Tedre, People, ideas, milestones: A scientometric analysis of computational thinking. ACM Transactions on Computing Education, vol. 21, no. 3, 2021.
  20. M. Saqr, J. Nouri, U. Fors, O. Viberg, M. Alsuhaibani, A. Alharbi, M. Alharbi, A. Alamer, How networking and social capital influence performance: The role of long-term ties. In Antonyuk A., Basov N. (eds.) Networks in the Global World V. Net Glow 2020. Lecture Notes in Networks and Systems, vol. 181. Springer, Cham, 2021.
  21. M. Wedberg, O. Viberg, M. Saqr, Facilitating disciplinary-specific knowledge sharing: A usability study of a dementia library. In Proceedings of the 19 World Conference on Mobile, Blended and Seamless Learning, 2021.

Publication Lists 2020

  1. M. Saqr, O. Viberg, “Using diffusion network analysis to examine and support knowledge construction in CSCL settings“, In Alario-Hoyos C., Rodríguez-Triana M., Scheffel M., Arnedillo-Sánchez I., Dennerlein S. (Eds.) Addressing Global Challenges and Quality Education. EC-TEL 2020. Lecture Notes in Computer Science, vol 12315. Springer, Cham, 2020.
  2. M. Saqr, J. Nouri, H. Vartiainen, M. Tedre, “Robustness and rich clubs in collaborative learning groups: A learning analytics study using network science“, Scientific Reports, vol. 10, 2020.
  3. M. Saqr, A. Al-Mohaimeed, Z. Rasheed, “Tear down the walls: Disseminating open access research for a global impact“, International Journal of Health Sciences, vol. 15, no. 5, 2020.
  4. M. Saqr, O. Viberg, H. Vartiainen, “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, 2020.
  5. S. Schöbel, A. Janson, K. Jahn, B. Kordyaka, O. Turetken, N. Djafarova, M. Saqr, D. Wu, M. Söllner, M. Adam, P.H. Gap, H. Wesseloh, J.M. Leimeister, “A research agenda for why, what, and how of gamifications designs: Outcomes of an ECIS 2019 panel“, Communications of the Association for Information Systems, vol. 46, 2020.
  6. M. Saqr, B. Wasson, “COVID-19: Lost opportunities and lessons for the future“, International Journal of Health Sciences, vol. 14, no. 3, 2020.
  7. M. Saqr, J. Nouri, H. Vartiainen, J. Malmberg, “What makes an online problem-based group successful? A learning analytics study using social network analysis”, BMC Medical Education, vol. 20, no. 1, 2020.
  8. M. Saqr, C. Suero Montero, “Learning and social networks – similarities, differences and impact“, Proceedings of the IEEE 20th International Conference on Advanced Learning Technologies (ICALT2020), IEEE, 2020.
  9. M. Saqr, J. Nouri, “High resolution temporal network analysis to understand and improve collaborative learning“, In Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), ACM, 2020.
  10. M. Saqr, O. Viberg, J. Nouri, S.S. Oyelere, “Multimodal temporal network analysis to improve learner support and teaching“, In Proceedings of CrossMMLA in Practice: Collecting, Annotating and Analyzing Multimodal Data Across Spaces Co-located with 10th International Learning and Analytics Conference (LAK 2020), 2020.

Publication Lists 2019

  1. Nouri, J., Saqr, M., Fors, U., “Predicting performance of students in a flipped classroom using machine learning: towards automated data-driven formative feedback“, Journal of Systemics, Cybernetics and Informatics, vol. 17, no. 2, 17-21.
  2. Alsuhaibani, M., Alharbi, A., Inam S.N.B., Alarmo, A., Saqr, M. “Research education in an undergraduate curriculum: Students perspective“, International Journal of Health Sciences, vol. 13, no. 2, 30-34, 2019.
  3. J. Nouri, K. Larsson, M. Saqr, “Identifying factors for master thesis completion and non-completion through learning analytics and machine learning“, In Scheffel M., Broisin J., Pammer-Schindler V., Ioannou A., Schneider J. (Eds.), Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science, vol 11722. Springer, Cham, 2019.
  4. M. Saqr, J. Nouri, I. Jormanainen, “A learning analytics study of the effect of group size on social dynamics and performance in online collaborative learning“, In Scheffel M., Broisin J., Pammer-Schindler V., Ioannou A., Schneider J. (Eds.), Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science, vol 11722. Springer, Cham, 2019.
  5. J. Nouri, K. Larsson, M. Saqr, “Bachelor thesis analysis: Using machine learning to predict dropout and identify performance factors“, International Journal of Learning Analytics and Artificial Intelligence for Education, vol. 1, no.1, 2019.
  6. J. Nouri, M. Ebner, D. Ifenthaler, M. Saqr, J. Malmberg, M. Khalil, J. Bruun, O. Viberg, M. González, Z. Papamitsiou, U. D. Berthelsen, “Efforts in Europe for data-driven improvement of education – A review of learning analytics research in six countries“, International Journal of Learning Analytics and Artificial Intelligence for Education, vol. 1, no.1, 2019.
  7. M. Saqr, M. Tedre, “Should we teach computational thinking and big data principles to medical students?”, International Journal of Health Sciences, vol. 13, no. 4, 2019. https://ijhs.org.sa/index.php/journal/article/view/4310
  8. M. Saqr, A. Alamro, “The role of social network analysis as a learning analytic tool in online problem based learning“, BMC Medical Education, vol. 19, 2019.

Publication Lists 2018

  1. M. Saqr, U. Fors, M. Tedre, J. Nouri, “How social network analysis can be used to monitor online collaborative learning and guide an informed intervention”. PLoS ONE, vol. 13, no. 3, 2018. https://doi.org/10.1371/journal.pone.0194777
  2. M. Saqr, U. Fors, M. Tedre, “How the study of online collaborative learning can guide teachers and predict students’ performance in a medical course”. BMC Medical Education, vol. 18, no.24, 2018. https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-018-1126-1
  3. M. Saqr, U. Forst, M. Tedre, “How learning analytics can early predict under-achieving students in a blended medical education course”, Medical Teacher, vol. 39, no. 7., 2017. https://doi.org/10.1080/0142159X.2017.1309376

 

Contact

Mohammed Saqr (Senior Researcher)

mohammed.saqr@uef.fi