Integrated Experiential and Active Learning Model of Teaching a Multidisciplinary Engineering Capstone

Mohsen Dorodchi
Professor
University of Northern Carolina at Charlotte
Location
Virtual
Organizer
Aaron McPherson

Join us on Zoom: https://ucsc.zoom.us/j/97472270498?pwd=NnRHREFuRlJ6ZGxBeXJTUytrRGFMQT09

Description: 
Active learning model of teaching seems to provide a solid framework for teaching project-based courses which are heavily built around teamwork. On the other hand, experiential learning has proven to be an effective learning model for engineering courses. In this model of capstone, we assume multidisciplinary teams of students working on real-world projects such as hybrid/electric cars, green-energy cloud services, solar-panel farms for energy efficient water treatment plants, etc. Then, the course would provide the project design and management tools as well as necessary practices based on experiential and active learning models to students to prepare them for implementing the project as a major learning experience in their degrees. The course activities include, requirements engineering, agile project management, task identification and estimation, etc. as well as modeling of the problem and possible solutions. This teaching presentation would include some sample activities for the audience as well.

Speaker Bio: Mohsen Dorodchi earned his Ph.D. in December of 2000 in electrical engineering and computer science. He has worked as software developer and engineer and senior system engineer for a number of years before starting his academic career. He has been an educator in computer science for over 22 years teaching diverse courses in computer science including introductory programming, data structures, software engineering, databases, etc. Dr. Dorodchi’s has been extensively working on evidence-based teaching innovation, computer science education research, educational tool development, and K12 outreach curriculum development and broadening participation in computing. His recent research includes predictive and learning analytics focusing on students’ success and software tool development to support students' success has been supported by NSF, State of North Carolina, and industry supports.