Toward the visual understanding of computing curricula

• DOI: 10.1007/s10639-020-10127-1
• Publication Date: 2020-04-02T16:19:42Z
• Authors: Shingo Takada; Ernesto Cuadros-V...; John Impagliazzo; Steven Gordon; Linda Marshall; Heikki Topi; Gerrit van der Veer; Leslie Waguespack
• Keywords: FOS: Computer and information sciences; Artificial intelligence; Table (database); skill requirements; Programming Education; Knowledge management; 02 engineering and technology; Open Educational Resources and Learning Object Repositories; Global standards; Computational Thinking in Education; Data science; Information Technology Skills and Education; 0202 electrical engineering, electronic engineering, information engineering; Psychology; IT skills; Data mining; Visualization; Interpretation (philosophy); curriculum guidelines; Data visualization; Pedagogy; 4. Education; Computing education; 303; Computer science; Mathematics education; Computer Science Applications; Programming language; Computational Thinking; FOS: Psychology; Computer Science; Physical Sciences; Curricular visualization; Curriculum; Computing competency; SDG 4 - Quality Education; Software; Information Systems

AbstractVarious computing subdisciplines, such as computer science and software engineering, each have their own curricular guidelines. They can be very difficult to understand and compare for people such as prospective students, industry personnel, and even faculty members. This is compounded by a lack of information surrounding undergraduate computing curricular topics via visual methods. This paper describes two experimental activities where the objective is to explore the possibility of obtaining quantitative data sets necessary for visualization, one based on competencies and the other based on knowledge areas. Both activities were based on surveys. The results from the first activity showed that a consensus interpretation could be obtained for the knowledge, skills, and dispositions implied by the competency descriptions, although not as strongly for dispositions. The second activity resulted in a table of knowledge areas with minimum and maximum weights for six computing subdisciplines. Finally, this paper also shows two examples of how users can explore the various curricular guidelines through visualization.