Rabia Maqsood

Assistant Professor
  • Department Of AI & Data Science
  • rabia.maqsood@nu.edu.pk
  • (041) 111-128-128
  • Ext: 197


Dr. Rabia Maqsood obtained her Ph.D. degree from the University of Milan, Italy, in January 2020. Her research interests encompass Educational Data Mining (EDM), Learning Analytics, and, Educational Process Mining. The prime focus of her research is to comprehend and shape students’ learning behaviors using their logged interactions captured by computer-based learning environments (e.g. LMS, MOOCs, and educational games). In this respect, Dr. Rabia is investigating efficient mechanisms for modeling students’ sequential trajectories so that the raw logged data can be transformed into comprehensible and human-interpretable information. In general, she is interested in other related problems that fall under the domain of EDM. Besides this, she is involved in developing new tools and techniques that offer data-driven learning analytics to students, teachers, and other stakeholders.


During her doctoral studies, Rabia has spent 9-months at the Knowledge Discovery and Intelligent Systems (KDIS) Lab of the University of Cordoba, Spain. She has also worked as a Research Fellow at the EBTIC research center of the Khalifa University (KU), Abu Dhabi (UAE), from October 2019 to December 2019. During her 3-months research stay at the KU, she developed a novel solution for automation and optimization of indoor fire alarm system for the Etisalat telecommunications company. She has been serving as a reviewer for different international conferences and journals.

Previously, Rabia has played a vibrant role as one of the pioneer faculty members at NUCES-FAST, CFD Campus, for more than 4 years starting from August 2012. After completing her doctoral studies, she has re-joined the campus since September 2020.


Educational Data Mining

Learning Analytics

Educational Process Mining

Dynamic Student/User Profiling


Ph.D. (Computer Science), 2020
University of Milan (Milan, Italy)

M.S. (Computer Science), 2010
NUCES-FAST (Lahore, Pakistan)

B.S. (Computer Science), 2007
University of Agriculture Faisalabad (Faisalabad, Pakistan)


  1. Maqsood, R., Ceravolo, P., Ahmad, M., & Sarfraz, M. S. (2023). Examining students’ course trajectories using data mining and visualization approaches. International Journal of Educational Technology in Higher Education, 20(1), 55.(JCR IF=7.611)

  2. Musaddiq, M. H., Sarfraz, M. S., Shafi, N., Maqsood, R., Azam, A., & Ahmad, M. (2022). Predicting the impact of academic key factors and spatial behaviors on students’ performance. Applied Sciences, 12(19), 10112. (IF. 2.838)

  3. Maqsood, R., Ceravolo, P., Romero, C., & Ventura, S. (2022). Modeling and predicting students’ engagement behaviors using mixture Markov models. Knowledge and Information Systems, 64(5), 1349-1384. (JCR IF: 2.531)

  4. Maqsood, R., Ceravolo, P., & Ventura, S. (2019, April). Discovering students’ engagement behaviors in confidence-based assessment. In 2019 IEEE Global Engineering Education Conference (EDUCON) (pp. 841-846). IEEE.

  5. Maqsood, R., & Ceravolo, P. (2019). Corrective feedback and its implications on students’ confidence-based assessment. In Technology Enhanced Assessment: 21st International Conference, TEA 2018, Amsterdam, The Netherlands, December 10–11, 2018, Revised Selected Papers 21 (pp. 55-72). Springer International Publishing.

  6. Maqsood, R., & Ceravolo, P. (2018, July). Modeling behavioral dynamics in confidence-based assessment. In 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT) (pp. 452-454). IEEE.

  7. Maqsood, R., & Durrani, Q. S. (2011, May). Itsas: An approach towards adaptive student assessment. In 2011 IEEE 3rd International Conference on Communication Software and Networks (pp. 649-654). IEEE.

Collaborations at National and International Level

Detail of Funded Projects