Principal Investigator: Gregory Chung
CoPrincipal Investigator(s): Kilchan Choi
Organization: University of California-Los Angeles
NSF Award Information: Identifying and Extracting Meaningful Indicators of Children’s Moment-to-Moment Programming Processes in Scratch
This project aims to serve the national interest in STEM and computational thinking by developing and validating indicators of students’ programming processes. The proposed exploratory research will develop a data logging module for Scratch to collect students’ moment-to-moment programming behavior. The project team will develop data processing rules or algorithms to derive indicators of programming processes (e.g., debugging). The project will advance the understanding of what programming processes students use, how these processes unfold over time, and how these processes relate to measures of programming and computational thinking. The data logging module and algorithms will be distributed to the Scratch community to allow the study of programming processes at scale. The research will also produce a systematic and replicable methodology to accelerate the development of algorithms and widespread dissemination of the tools, techniques, and methods used to study programming processes.
The research will observe novice fifth grade and undergraduate students learning to program in Scratch. Students’ process data will be used to derive indicators of programming processes. The indicators will be compared between age groups, within each age group over time, and to existing external measures of programming concepts and skills. This research will generate insights about what, how, and potentially why students perform the way they do. The capability to derive indicators of programming processes will complement existing methods of scoring static Scratch code. Algorithm development will focus on theoretically-driven, rule-based indicators of programming processes that are directly interpretable and on methods that systematize and accelerate the algorithm development process. The algorithm development methodology will apply to other block-based environments and applications involving process data such as games, simulations, and innovative item types in educational assessment.
This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.