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This paper shares results from surveys administered in spring 2018 to a nationally representative sample of nearly 300 U.S. high school computer science teachers. It describes the nature of high school computer science instruction and the extent to which teacher background, classroom factors, and school context predict the type of instruction students experience. Data from the study were analyzed using path modeling-a form of regression analysis that estimates both direct and indirect effects (i.e., through intermediary variables)-to examine relationships between teacher, classroom, and school factors, and the extent to which teachers (1) emphasize reform-oriented instructional objectives (e.g., learning about real-life applications of computer science) and (2) engage students in computer science practices (e.g., recognizing and defining computational problems). Sample findings include that students are most commonly engaged in activities related to testing and refining computational artifacts, but are less often engaged in aspects of computer science related to end users (e.g., create a computational artifact to be used by someone else). The path analysis highlights several factors that are related to greater engagement of students in the computer science practices, including teacher participation in professional development and the use of coherent instructional materials.

Factors Associated with High-Quality Computer Science Instruction: Data from a Nationally Representative Sample of High School Teachers