Publications of Learning Analytics Lab

Publication Lists 2022

  1. 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-analysis. Educational Research Review, vol. 35, 2022.
  2. M. Saqr, S. López-Pernas, The curious case of centrality measures: A large-scale empirical investigation. Journal of Learning Analytics, In press.
  3. 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 education. IEEE 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 study. International 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 research. Computers 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: 1982-2020. Proceedings 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: 1982-2020. Proceedings 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 program. Computers & Education, vol. 175, 2021.
  9. M. Saqr, S. López-Pernas, Towards self-big data. International 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 study. Proceedings 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 agenda. Computers & Education, 2021.
  12. M. Bermo, M. Saqr, H. Hoffman, D. Patterson, S. Sharar, S. Minoshima, D.H. Lewis, Utility of SPECT functional neuroimaging of brain. Frontiers in Psychiatry, 2021.
  13. J. Jovanović, M. Saqr, S. Joksimović, D. Gašević, Students matter the most in learning analytics: The effect of internal and instructional conditions in predicting academic success. Computers & Education, vol. 172, 2021.
  14. O. Poquet, B. Chen, M. Saqr, T. Hecking, Using network science in learning analytics: Building bridges towards a common agenda. Proceedings of the NetSciLA21 workshop, 2021.
  15. O. Poquet, M. Saqr, B. Chen, Recommendations for network research in learning analytics: To open a conversation. Proceedings 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 programming. Sustainability, 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 tactics. Proceedings of the 2020 STELLA Symposium, 2021
  18. M. Saqr, S. López-Pernas, Idiographic learning analytics: A single student (N=1) approach. Companion 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