|Title:||Identifying Young Children's Computational Thinking Processes In Visual Programming Environments Using Telemetry-Based Evidence Collection Methods|
|Principal Investigator:||Chung, Gregory||Awardee:||University of California, Los Angeles|
|Program:||Science, Technology, Engineering, and Mathematics (STEM) Education [Program Details]|
|Award Period:||3 years (07/01/2019 - 06/30/2022)||Award Amount:||$1,400,000|
Co-Principal Investigators: Hosford, Grant; Shochet, Joe
Purpose: This project aims to examine associations between computational thinking (CT) processes and external measures of CT, and to examine measures based on children's interaction with the visual programming environment. The importance of introducing computer programming at an early age is less to create young programmers than it is for children to develop new ways of thinking. Programming can develop CT skills by encouraging systematic thinking and problem solving. Elements of CT are powerful cognitive tools applicable to domains beyond computer science that can help individuals solve problems, design systems, understand human behavior.
Project Activities: The research team will use the visual programming platform, The Foos by codeSpark, to explore young children's computational thinking processes in grades 1 and 3. The Foos allows students to program via blocks instead of traditional text-based commands.
Products: Researchers will make the algorithms and models produced from the project publicly available through they Interuniversity Consortium for Political and Social Research (ICPSR) and the Institute for Quantitative Social Science (IQSS) Dataverse Network. In addition, researchers will write articles for peer-reviewed publications, and present at national conferences and meetings.
Setting: This project takes place in urban public elementary school settings in California.
Sample: Grade 1 and grade 3 students from two ethnically diverse urban public school districts will participate in the study. Approximately 25 percent of the student population are designated as English Learners and over 46 percent of students are identified as ethnic minorities.
Malleable Factor: The project will explore young children's computational thinking processes as they interact with a visual programming environment.
Research Design and Methods: Theresearch team will use the visual programming platform, The Foos, to examine CT processes. The Foos allows young children to program via blocks instead of traditional text-based commands. It is a game-based approach to teaching sequencing, loops, conditionals, and events. The game promotes transfer by allowing children to devise multiple ways to solve a puzzle. The use of blocks vs. text allows pre-readers, non-native speakers, and children with reading disabilities to use the platform.
The research team will gather evidence of the computational thinking concepts, skills, and processes in a series of classroom-based data collections. A classroom-based cognitive lab study will examine students' thought processes as they engage in programming activities. Students will play The Foos twice a week for five weeks to cover the 10-unit curriculum. The researchers will also adapt existing measures of CT and define algorithms for the telemetry-based measures, and pilot test the measures in Grade 1 and 3 classrooms.
Control Condition: Due to the nature of this study, there is no control condition.
Key Measures: The research team will examine students' use of sequencing, loops, events, and conditionals within the visual programming environment and adapt existing measures developed for block-based programming. Researchers will derive telemetry-based measures of CT processes from children's interaction with the visual programming environment. They will base the metrics of the programs children create (for example, number of commands) on examination of existing programs and programs from the current study.
Data Analytic Strategy: The research team will use sequence mining of students' programming behavior to identify recurring behaviors. They will use the n-grams of behavior to inform which states and transitions to model as a Markov process (for example, debugging). Qualitative analyses will guide the development of machine learning algorithms and quantitative analyses will examine the extent to which telemetry-based measures are sensitive to instruction, and the associations among the telemetry-based measures, program metrics, and external measures of CT.