Learning Analytics

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.
The learning analytics lab in UEF is one of the most active in Europe and 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.

Recent projects

  • 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

Selected publications

Mohammed Saqr, Kwok Ng, Solomon Sunday Oyelere, and Matti Tedre. 2021. People, Ideas, Milestones: A Scientometric Study of Computational Thinking. ACM Trans. Comput. Educ. 21, 3, Article 20 (March 2021), 17 pages. DOI:https://doi.org/10.1145/3445984
Mohammed Saqr, Jalal Nouri, Uno Fors, Marya Alsuhaibani, Amjad Alharbi, Mohammed Alharbi, Abdullah Alamer and Olga Viberg 2021. How networking and social capital influence performance: the role of long-term ties The Role of Long-. Lecture Notes in Networks and Systems, vol 181. https://doi.org/10.1007/978-3-030-64877-0_22
Mohammed Saqr, Olga Viberg, Ward Peeters (2021). Using psychological networks to reveal the interplay between foreign language students’ self-regulated learning tactics. Proceedings of the 2020 STELLA Symposium 2020.
Sonsoles López-Pernas, Mohammed Saqr. Idiographic Learning Analytics: A Within-Person Ethical Perspective. Responsible Ethics workshop LAK 2021
Mohammed Saqr, Sonsoles López-Pernas Idiographic learning analytics: A single student (N=1) approach. Network Science workshop LAK 2021Saqr, M., Nouri, J., Vartiainen, H., & Tedre, M. (2020). Robustness and rich clubs in collaborative learning groups: a learning analytics study using network science. Scientific Reports, 10(1), 1-16.
Mohammed Saqr, Olga Viberg, Henriikka Vartiainen, 2020. 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(2), 227–248. https://doi.org/10.1007/s11412-020-09322-6
Ward Peeters, Mohammed Saqr and Olga Viberg. 2020 Applying Learning Analytics to Map Students’ Self-Regulated Learning Tactics in an Academic Writing Course. 28TH International Conference On Computers In Education 2020.
Bergdahl, Nina & Nouri, Jalal & Afzaal, Muhammad & Karunaratne, Thashmee & Mohammed Saqr. (2020). Learning Analytics for Blended Learning -A systematic review of theory, methodology, and ethical considerations. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI). 2.
Mohammed Saqr, Jalal Nouri, Henriikka Vartiainen, and Jonna Malmberg. 2020. What makes an online problem-based group successful? A learning analytics study using social network analysis. BMC Medical Education 20, 1: 80. https://doi.org/10.1186/s12909-020-01997-7
Mohammed Saqr and Jalal Nouri. 2020. High resolution temporal network analysis to understand and improve collaborative learning. In Learning Analytics and Knowledge (LAK) Conference 2020, 314–319.
Mohammed Saqr and Calkin Suero Montero. 2020. Learning and Social Networks–Similarities, Differences and Impact. In IEEE 20th International Conference on Advanced Learning Technologies (ICALT), Estonia
Mohammed Saqr, Olga Viberg 2020, Using diffusion network analytics to analyze and support knowledge exchange in CSCL EC-TEL 2020. Lecture Notes in Computer Science, vol 12315. Springer, Cham. https://doi.org/10.1007/978-3-030-57717-9_12
Mohammed Saqr, Olga Viberg, Jalal Nouri, and Solomon Oyelere. 2020. Multimodal Temporal Network Analysis to Improve Learner Support and Teaching. In LAK20 CEUR Proceedings 2020, 30–33.
Mohammed Saqr, Jalal Nouri, and Ilkka Jormanainen. 2019. A Learning Analytics Study of the Effect of Group Size on Social Dynamics and Performance in Online Collaborative Learning. In Lecture Notes in Computer Science, 466–479. https://doi.org/10.1007/978-3-030-29736-7_35
Mohammed Saqr and Ahmad Alamro. 2019. The role of social network analysis as a learning analytics tool in online problem-based learning. BMC Medical Education 19, 1: 1–11. https://doi.org/10.1186/s12909-019-1599-6
Mohammed Saqr, Jalal Nouri, and Marc Santolini. 2019. Towards Group-Aware Learning Analytics: Using Social Network Analysis and Machine Learning To Monitor and Predict Performance in Collaborative Learning. INTED2019 Proceedings 1: 7652–7659. https://doi.org/10.21125/inted.2019.1881
Jalal Nouri, Ken Larsson, and Mohammed Saqr. 2019. Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning. In Lecture Notes in Computer Science, 28–39. https://doi.org/10.1007/978-3-030-29736-7_3.
Marya Alsuhaibani, Amjad Alharbi, S N Bazmi Inam, Ahmad Alamro, and Mohammed Saqr. 2019. Research education in an undergraduate curriculum: Students perspective. International journal of health sciences 13, 2: 30–34.
Jalal Nouri, Ken Larsson, and Mohammed Saqr. 2019. Bachelor Thesis Analytics: Using Machine Learning to Predict Dropout and Identify Performance Factors. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI) 1, 1: 116. https://doi.org/10.3991/ijai.v1i1.11065
Jalal Nouri, Martin Ebner, Dirk Ifenthaler, Mohammed Saqr, Jonna Malmberg, Mohammad Khalil, Jesper Bruun, Olga Viberg, Miguel Ángel Conde González, Zacharoula Papamitsiou, and Ulf Dalvad Berthelsen. 2019. Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Seven Countries. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI) 1, 1: 8. https://doi.org/10.3991/ijai.v1i1.11053
Jalal Nouri, Mohammed Saqr and Uno Fors, and (2019). Predicting performance of students in a flipped classroom using machine learning: towards automated data-driven formative feedback. Journal of Systemics, Cybernetics and Informatics, 17.
Mohammed Saqr. 2018. A literature review of empirical research on learning analytics in medical education. International journal of health sciences 12, 2: 80–85.
Mohammed Saqr, Jalal Nouri, and Uno Fors. 2018. What Shapes the Communities of Learners in a Medical School. In EDULEARN18 Proceedings, 7709–7716. https://doi.org/10.21125/edulearn.2018.1792
Mohammed Saqr, Jalal Nouri, and Uno Fors. 2018. Temporality Matters. a Learning Analytics Study of the Patterns of Interactions and Its Relation To Performance. EDULEARN18 Proceedings 1: 5386–5393.
Mohammed Saqr. 2017. Editorial: Assessment analytics: The missing step. International journal of health sciences 11, 1: 1–2.
Mohammed Saqr. 2017. Editorial: Big data and the emerging ethical challenges. International journal of health sciences 11, 4: 1–2.
Year 2016
Mohammed Saqr. 2015. Editorial: Learning Analytic and Medical Education. International Journal of Health Sciences 9, 4: v–vi.
Osama A Amin and Mohammed Saqr. 2011. Blended learning in orthopedics course: an evaluation study. International journal of health sciences 5, 2 Suppl 1: 40–401.
Mohammed Saqr, Uno Fors, and Matti Tedre. 2017. How learning analytics can early predict under-achieving students in a blended medical education course. Medical Teacher 39, 7: 757–767.
Mohammed Saqr, Uno Fors, and Jalal Nouri. 2019. Time to focus on the temporal dimension of learning: a learning analytics study of the temporal patterns of students’ interactions and self-regulation. International Journal of Technology Enhanced Learning 11, 4: 398. https://doi.org/10.1504/ijtel.2019.10020597
Mohammed Saqr, Uno Fors, Matti Tedre, and Jalal Nouri. 2018. How social network analysis can be used to monitor online collaborative learning and guide an informed intervention. PLoS ONE 13, 3: 1–22. https://doi.org/10.1371/journal.pone.0194777
Mohammed Saqr, Uno Fors, and Matti Tedre. 2018. How the study of online collaborative learning can guide teachers and predict students’ performance in a medical course. BMC Medical Education 18, 1: 1–14. https://doi.org/10.1186/s12909-018-1126-1
Mohammed Saqr, Uno Fors, and Jalal Nouri. 2018. Using social network analysis to understand online problem-based learning and predict performance. PLoS ONE 13, 9: e0203590. https://doi.org/10.1371/journal.pone.0203590

 

Contact

Mohammed Saqr
Senior researcher
mohammed.saqr@uef.fi