Purpose: "One decade ago, NSF funded two efforts to create a computing education research (CER) community in the U.S. and Europe. Nicknamed “Bootstrappers” and “Scaffolders,” these two projects helped create a CER community among existing computing faculty who had an interest in computing education.. This column focuses on the first summit: the most important research questions facing CER" (p. 34). "The accepted white papers... asked research questions primarily in five areas: broadening participation, computing in K–12 (primary and secondary), computing in STEM education, students and learning issues, and tools. A few papers were written at a meta-level, looking at the direction of CER and its impact, and looking more broadly at challenges facing the teaching of computing at all levels" (p. 34). The authors summarize findings across these white papers.
Findings: The focus on increasing access to computing moves a lot of attention in CER into primary and secondary school education. It is the level of schooling where we can reach “all”... [and make it] more accessible to those with disabilities. Because computing is typically an elective subject in primary and secondary school, computing education researchers look for any way they can to slide computing into schools, including integrating through humanities (including creative writing, theatre, and media production), through science classes, and through mathematics classes" (p. 34-35). "Computing education researchers were urged to disaggregate empirical data in CER based on race, ethnicity, gender, and socioeconomic status. Studies at the intersection of race, ethnicity, and gender are needed, to better understand why certain groups choose to enter and persist in computing while others do not" (p. 35). Further, "we know relatively little about the foundations of computing knowledge, and these are more critical issues as we move computing into primary and secondary schools. Foundational questions include “How do students learn to program and what does that development look like?” “What are successful and unsuccessful mental models of challenging computing concepts?” “How do we support successful transfer from beginner programming environments to real-world ones?” and “What are common challenges in conceptual understanding in computing course?”" (p. 36). "Researchers are working to define and validate learning progressions for K–12 computing. A more critical issue for CER today is how to develop enough teachers to support computing education in primary and secondary schools." (p. 36).
Recommendations: "What do computing teachers need to know that’s different from other STEM teachers? That discipline-specific knowledge of teachers is called Pedagogical Content Knowledge. Defining PCK for computing and figuring out how to teach it are major thrusts in CER today" (p. 36). "The hope is to leverage big data to understand better the nature of learning programming and computational problem solving, the problems learners encounter, and the pedagogies and strategies that can be used to address them" (p. 36). Using big data and machine learning analytic methods instead of traditional.