Development of Computer Science Disciplines - A Social Network Analysis Approach

In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published JCR (Journal Citation Report). Although this data covers most of important journals, it lacks computer science conference and workshop proceedings. That results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences

All Advanced Placement (AP) Computer Science is Not Created Equal: A Comparison of AP Computer Science A and Computer Science Principles

This article compares the two most prominent courses of Advanced Placement (AP) computer science study offered throughout 9-12 grades in the U.S. The structure, guidelines, components, and exam formats of the traditional AP Computer Science A course and the relatively newer AP Computer Science Principles course were compared to examine differences in content and emphases. A depth-of-learning analysis was conducted employing Bloom’s Revised Taxonomy to examine potential differences in rigor and challenge represented by the two options, particularly as it relates to acquiring computer programming proficiency. Analyses suggest structural differences in both course content and end-of-course exam components likely result in less depth and rigor in the new Computer Science Principles course as compared to the Computer Science A course. A lower minimum standard for learning programming skills in the Computer Science Principles course was observed, making it a less viable option for students looking to acquire skills transferable to future computer science study or employment. The potential implications for students choosing the new course over the traditional offering, as well as for schools opting for the new course as its sole or primary offering are discussed