Project-based learning (PBL) challenges students to engage in a long-form investigation of a nontrivial problem, in which they work in a team to ask questions, make predictions, collect and analyze data, draw conclusions, and communicate results. PBL has been shown to result in many benefits, including improved conceptual understanding and enhanced skills in communication, teamwork, and creativity. These are widely acknowledged to be core capabilities for engineers, making PBL an attractive proposition for teaching mechanics at the undergraduate level.
Because of its open-ended nature, implementations of PBL frequently rely on large course staffs or small class sizes to be effective. Expanding the use of PBL among faculty requires we begin to understand how to implement PBL at large scales. In this paper, we present results from a recent implementation of PBL in the introductory dynamics course at Midwestern Research University (MRU), where it is currently being used for a half-semester project with 200 to 500 students per semester. The project activity used is having teams of four students challenged to design and implement an experiment to determine the drag coefficient of a ball from a sport of their choice (e.g., ping pong, tennis, soccer) and to match their experimental results to a computer simulation of the experiment.
Two implementation choices were essential for the success of PBL in this large-scale context. First, the primary data collection system was students’ own mobile phones. They were encouraged to use video capture and analysis with open-source software (e.g., Tracker), or to use accelerometer or other sensor data from their phones. This approach of using personal computing devices mirrors the BYOD (Bring Your Own Device) movement commonly seen in both educational and business settings, and allows cost-free scaling of PBL experiments to very large student numbers. Additionally, it is empowering and motivating for students to realize that they personally have the capability to collect and analyze meaningful data using their newly-acquired mechanics knowledge.
Second, peer feedback was used to provide detailed mid-project formative feedback to students, allowing and encouraging them to iterate on their experimental designs and analysis. Using a computer-based peer matching and rubric-based peer feedback system enabled scaling to large student numbers without undue instructor time commitment.
This paper evaluates the success of this PBL implementation using student survey data and feedback from the graduate teaching assistants and undergraduate course assistants who were the primary student-facing instructors for the PBL component of the course.
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