
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.
FOS: Computer and information sciences, Artificial intelligence, Table (database), skill requirements, Programming Education, Knowledge management, Open Educational Resources and Learning Object Repositories, Global standards, Computational Thinking in Education, Data science, Information Technology Skills and Education, Psychology, IT skills, Data mining, Visualization, Interpretation (philosophy), curriculum guidelines, Data visualization, Pedagogy, 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
FOS: Computer and information sciences, Artificial intelligence, Table (database), skill requirements, Programming Education, Knowledge management, Open Educational Resources and Learning Object Repositories, Global standards, Computational Thinking in Education, Data science, Information Technology Skills and Education, Psychology, IT skills, Data mining, Visualization, Interpretation (philosophy), curriculum guidelines, Data visualization, Pedagogy, 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
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 16 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
