Introduction 

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 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 Khalifa University (KU), Abu Dhabi (UAE), from October 2019 to December 2019. She has been serving as a reviewer for different international conferences and journals. 

 Research Interests

 Publications

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. 

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. (IF: 2.822)

4. Maqsood, R., Ceravolo, P., & Ventura, S. (2019). 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. (2018). Corrective feedback and its implications on students’ confidence-based assessment. In International Conference on Technology Enhanced Assessment (pp. 55-72). Springer, Cham. 

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. 

 Teaching