Curriculum Analysis of Computer Science Departments by Simplified, Supervised LDA

Abstract

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.

Evolution of data science and its education in iSchools: An impressionistic study using curriculum analysis

“…Using the simplified, supervised LDA method, Matsuda et al (2018) carried out a CA of computer science (CS) of the top-ranked universities worldwide. The CS curricula of the 47 top-ranked universities were mapped to Computer Science Curricula 2013 (CS2013), where the topics are classified into the 18 Knowledge Areas (KAs).…”

Analysis of Cross-Referencing Artificial Intelligence Topics Based on Sentence Modeling

“…More specifically, ten words that could represent each KA in the body of knowledge were extracted and compared based on common words. Another study was based on the CS2013 body of knowledge, which applied an extended method called the simplified, supervised latent Dirichlet allocation (ssLDA) [15]. Syllabus topics from the top 47 universities worldwide in 2014-2015 were quantified.…”

Syllabus Mining for Analysis of Searchable Information

“…The University of Tokyo is one of the top ranked universities in the Japan University Rankings 2018 4 and 2019 5 . The quantity and quality of research achievements in Computer Science, Mathematics, and Engineering at this university were top-ranked in the benchmarking of Japanese universities in 2015 6 . We focused on these subject areas for a detailed analysis of certain experiments conducted in this study, which will be discussed subsequently in this section.…”

“…To address this problem, visualization of educational information to aid in retrieval was suggested by [4] and [5]. Faculty members should analyze syllabus documents when a curriculum is designed or evaluated at a university [6]. Based on these background studies, we elucidated the searchability of syllabi.…”