Project Profile

This project will advance efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase students' motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM) by developing, broadly implementing, and systematically investigating two 10-day computer programming instructional sequences. The new instructional sequences will integrate computer science and science learning experiences through simulated internships for core middle school science classrooms, and are designed to increase student dispositions toward, and capacity for, computer programming and computational thinking. Ultimately, the intervention is designed to support broader participation in computer science (CS) fields of study and careers, with particular emphasis on females. This project seeks to accomplish this goal by: (1) immersing up to 4000 11-14 year old youth (Grades 6-8) students in simulated internships that mirror the collaborative and computational work of practicing scientists, and that can be embedded within a school's core science curriculum; (2) offering a more inclusive model of computer science work that can expand students' perception of the nature and value of computer programming and encourage a broader range of students, and females in particular, to identify as possible programmers; (3) gathering evidence that can advance and deepen the field's understanding of how students' computer science knowledge and practices develop within the context of science learning experiences; and 4) identifying specific factors, designs, and practices likely to engage students in CS, improve student dispositions toward STEM and CS-related occupations, and that are likely to improve the capacity of teachers and districts to support CS education. The project extends prior work aimed at incorporating coding and computational thinking into the school in the following ways: an explicit focus on collaborative discourse and collaborative problem solving, including that within digitally-mediated discussion forums; backend data logging of student interactions within the simulated internships' digital environments in order to analyze how student understanding develops at the intersection of science and computer science; just-in-time teacher learning via an educative curriculum to support system capacity and broader impact; and a research and development model that explicitly incorporates school, district, and state policy level stakeholders in the design process, in order to build an understanding of how the intervention, and those like it, can be successfully and sustainably implemented. The project is also supported by the STEM+Computing program (STEM+C) to advance research on how students' computer science knowledge and practices develop within the context of science learning experiences and improve student dispositions toward STEM and CS-related occupations.

The mixed-methods research agenda for this project will be guided by four questions: 1) What specific design features and instructional strategies of the CS Internships are most important for broadening student participation in CS?; 2) What aspects of the CS Internships are most important to support sustainability of CS and science integration?; 3) What factors, design features, and practices are most important for supporting productive student engagement in, and teacher facilitation of, collaboration and discourse (both in-person and digital) in STEM?; and 4) What aspects of student understanding may be revealed when students are able to manipulate the code behind scientific models? In Year 1, the project will pilot and iteratively develop the first of two Coding Science Internships, with methodology grounded in design-based implementation research. Research activities will include observations of piloted lessons and teacher interviews. Also in Year 1, the project will also begin to examine how students develop computational thinking and computer programming practices, through cognitive interviews with students in pilot classrooms. In Year 2, the project will begin broad implementation and systematic investigation of the internship developed in Year 1, and begin iteratively developing the second internship, which will be broadly implemented in Year 3. Research activities related to broad implementation in Years 2 & 3 will include pre-post measures for students (including scales measuring disposition toward CS, and CS and science practices), and teachers (including scales measuring science and technological pedagogical content knowledge and competency beliefs for CS instruction); daily engagement surveys for students; and daily intervention feasibility and perceived value surveys for teachers. In addition to analyses of variance and covariance, the project will employ mediation analysis to examine interactions among key variables contributing to any observed learning gains. Years 2 & 3 will also feature extensive capture of student data generated through interactions with the digital resources (e.g, "clickstream" and metadata, submission data and discourse within the digital discussion forums). Learning analytics methods (including machine learning, Bayesian network modeling, and Latent Dirichlet Allocation) applied to these massive data sets will be aimed at providing more subtle insight into student development of computational thinking as it applies to science, and possible learning trajectories for the integrated development of computer science and science practices.