In the same ways, as we pursue innovative research, we seek to innovate in university teaching and learning. We do not see learning as the passive acquisition of knowledge. In fact, as options for e-learning emerged, we questioned how computer science lectures could be transformed (Giabbanelli 2009). Our answer was to adopt a student-centered approach, which rewards creativity and emphasizes hands-on projects starting from the very first year (Giabbanelli 2012). The success of interdisciplinary projects is not limited to undergraduate experiences. Our evaluation of a graduate certificate in modeling complex systems showed that matching computer scientists with subject-matter experts could be highly productive if barriers were appropriately addressed (Giabbanelli et al., 2012). While our teaching journey began as e-learning emerged, we continually need to adapt to shifts in education. Most recently, we assessed how computational modeling could be taught given the recent focus on data science (Giabbanelli & Mago 2016).
- Giabbanelli, P.J. (2009) Why having in-person lectures when e-learning and podcasts are available? In Proceedings of the 14th Western Canadian Conference on Computing Education.
- Giabbanelli, P.J. (2012) Ingredients for student-centered learning in undergraduate computing science courses. In Proceedings of the Seventeenth Western Canadian Conference on Computing Education.
- Giabbanelli, P.J., Reid, A.A., Dabbaghian, V. (2012) Interdisciplinary teaching and learning in computing science: Three years of experience in the mocssy program. In Proceedings of the Seventeenth Western Canadian Conference on Computing Education.
- Giabbanelli, P.J., Mago, V.K. (2016) Teaching Computational Modeling in the Data Science Era. Procedia Computer Science, 80, 1968-1977.