Co-mentors: Luc Paquette (Curriculum & Instruction) and Jinming Zhang (Educational Psychology)
Social impact: Education that better meet student needs, via experiment-driven data.
Project description: In this project, students will develop a tool to study momentary time sampling (MTS) of affective (emotional) states in a simulated classroom and to test the effect of different parameters on the accuracy of such a sampling approach. MTS is one of the preferred methods for discontinuous sampling in social sciences, but it is known to sometimes produce a high measurement error based on several factors related to the design of the observation session (e.g., the sampling interval, length of the observation session) and the properties of the behavior or construct being studied (e.g., interactions between the duration of individual behavioral events, as well as the number of instances and the overall prevalence of each behavior during the observation session). The goal of this project is to develop a tool allowing educational researchers to better plan their classroom data collection in order to improve the accuracy of the collected data and increase the statistical power of their experiments.
Students participating in this project will be responsible for the implementation and the design of the MTS tool. Their design will be informed by a previous prototype that was developed with limited functionality. As the goal of this project is to develop a tool that can be used by educational researchers, the resulting product will need to be multi-platform, have a user-friendly interface, and run efficiently on a regular laptop computer. Paquette will overview the development of the tool and will help the students specify its design based on the needs from the research community. Zhang will contribute to the project by offering insights on how to generate random distributions when simulating students in a classroom and on how to evaluate the accuracy of the simulated observations.