Project Team
- Firas Moosvi, Lecturer, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science (Lead Applicant)
- Irene Vrbik, Assistant Professor of Teaching, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science
- Jake Bobowski, Professor of Teaching, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science
- Ramon Lawrence, Professor, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science
Other Team members
- Paula Wong-Chung, Undergraduate student, Irving K. Barber Faculty of Science
- Jason Reich, PhD Student, Medical Physics, Medical Physics Irving K. Barber Faculty of Science
- Parsa Rajabi, MSc Student, Computer Science Education, Simon Fraser University
- Michael Kudla, PhD Student, Medical Physics, Medical Physics Irving K. Barber Faculty of Science
Themes
- OER
Year
2022
About the Project
Researchers in education and learning have known since at least the early 1930s that frequent assessments in the classroom result in improved learning and retention. More recent literature has shown demonstrably that infrequent high-stakes assessments (weightings over 20%) such as midterms and exams increase stress and anxiety, favour students that have sustained privilege from higher socioeconomic backgrounds, and make our classrooms less inclusive and less diverse.
However, despite widespread acceptance of this wisdom, it is not feasible or practical for instructors to manually create, administer, and grade many assessments in a semester. For a while, commercial textbook publishers appeared to be our saviours as they heavily promoted online platforms with massive problem banks and instructor-friendly tools to create and manage multiple assessments. Of course, there was a cost to this: instructors had to adopt expensive publisher textbooks and offload the costs to the students. For years we accepted that there was no better way – now, there is.
In this project we are expanding the pre-existing Physics OPB with more problems, integrating high quality hints and feedback to promote student learning, and laying the foundation for expanding this approach to other disciplines (including Data Science). Moreover, through aligned projects, team members engaged in this project will develop of a new data science course (DATA 100), develop extensive TA training, and leverage work that has already been started to complete the transformation of the first-year physics and data science programs towards a more modern, computation-focused curriculum with an open homework system (see: link to Vrbik, 2022 and link to Bobowski, 2022).
Awarded in the 2022 Open Educational Resources Stream