A comparison of the core aspects of the ACM/IEEE Computer Science Curriculum 2013 Strawman report with the specified core of CC2001 and CS2008 review

Marshall, Linda

doi:10.1145/2421277.2421281

Cited by 21 publications

(8 citation statements)

References 5 publications

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“…It would be even more difficult to recognize differences between curricula modelled using the visualization of the graph-based structure. Thus, Marshall (2012Marshall ( , 2014 took an algorithmic approach to reduce the complexity of the graph-based curriculum models (as seen in Fig. 5) to scalar values on spokes of a radar chart as shown in Fig.…”

Section: Visualization Of Curricular Guidelinesmentioning

confidence: 99%

Toward the visual understanding of computing curricula

et al. 2020

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Various 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.

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Section: Visualization Of Curricular Guidelinesmentioning

confidence: 99%

Toward the visual understanding of computing curricula

et al. 2020

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“…Though a number of studies have been made on methodologies and tools for analyzing curricula by using statistically processed syllabus data [8], [12], [28], they can not compare the different curricula quantitatively. Marshall tried to quantify the changes in the CS structure of the ACM/IEEE curricula series [10] and visualized the structure of a curriculum by modeling the KAs as a network graph. Gluga et al developed a web-based system called PROGOSS that maps curricula learning goals and mastery levels to individual assessment tasks across entire degree programs [6].…”

Section: Related Workmentioning

confidence: 99%

“…There are many previous works such as design tools [27], the repository [24], course-knowledge unit relations [14], [25], the change visualization [10]. On the other hand, we have employed a data science approach using a statistical machine learning method [16], [17], [19].…”

Section: Introductionmentioning

confidence: 99%

Curriculum Analysis of Computer Science Departments by Simplified, Supervised LDA

Matsuda

Sekiya

Yamaguchi

2018

Journal of Information Processing

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The design of appropriate curricula is one of the most important issues in higher educational institutions, and there are many features to be considered. In this paper, the two key features ("locality bias" and "combination of two simple factors") were discovered by investigating the actual computer science (CS) curricula of the top-ranked universities on the basis of Computer Science Curricula 2013 (CS2013), where the CS topics are classified into the 18 Knowledge Areas (KAs). We applied a machine learning method named simplified, supervised latent Dirichlet allocation (ssLDA) to the actual syllabi of the CS departments of the 47 top-ranked universities. ssLDA estimates the relative weights of the KAs of CS2013 in each syllabus. Then, each CS department was characterized as the averaged weights of the KAs over its included syllabi. We applied the three well-known data analysis methods (hierarchical cluster analysis, principle component analysis, and non-negative matrix factorization) to the averaged weights of each department and found the above two key features quantitatively and objectively.

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“…Their conclusions depict the whole picture but are not enough for understanding how disciplinary concepts evolve. Marshall [10] makes a comparison to CC2001, CS2008 and CS2013 by modeling their knowledge structure as trees. That paper discusses the knowledge unit at topic level, whose granularity is too low comparing to concept level.…”

Section: Related Workmentioning

confidence: 99%

TACE: A Toolkit for Analyzing Concept Evolution in Computing Curricula

Luo

Zhang

Yang

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Effective teaching and learning requires to knowing whether some concepts are needed to grasping. Our investigation for ACM Computing Curricula from 1991 to 2013 shows that the numbers of concepts are increased by 5 times. That phenomenon makes learners harder to distinguish which concept is update or not. In this paper, we develop a solution to explore the body of knowledge of computing. The proposed toolkit uses a graph model to represent the disciplinary knowledge structure. The analytic results by TACE give us insight for the various subjects in computing discipline. Our findings show that 61.3% concepts in CC1991 are obsolete. In CC2001, the proportion of obsolete concepts drops to 11.5%, and in CS2008 it is 16.8%. The OS, IM, DS, AL's knowledge areas are more stable than CN, NC, GV, AR. The TACE's framework is highly modular, adaptive and extendible for analyzing other discipline's curricula.

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A comparison of the core aspects of the ACM/IEEE Computer Science Curriculum 2013 Strawman report with the specified core of CC2001 and CS2008 review

Cited by 21 publications

References 5 publications

Toward the visual understanding of computing curricula

Toward the visual understanding of computing curricula

Curriculum Analysis of Computer Science Departments by Simplified, Supervised LDA

TACE: A Toolkit for Analyzing Concept Evolution in Computing Curricula

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