Evaluating Publications' Keywords in Computer Science Education Research: A Bibliometric NLP Approach Conference

Zhu, J, Zahedi, L, Ross, MS. (2021). Evaluating Publications' Keywords in Computer Science Education Research: A Bibliometric NLP Approach .

cited authors

  • Zhu, J; Zahedi, L; Ross, MS

fiu authors

abstract

  • This work demonstrated how evaluating publication keywords in the Computer Science Education Research (CSER) could bring conceptual and functional insights by combining the bibliometric approach and natural language processing (NLP). The collection of publication keywords represents the knowledge landscape of the research domain. Using proper keywords will improve publication visibility in research networks, contributing to the overall research impact. We gathered bibliometric data and the strategic directions in two CSER publication venues from 2015 to 2019 to capture the research foci of CSER and evaluate alignment between 1) selected keywords and publication venue mission statements; 2) keywords and abstract content. By applying the NLP techniques, our results revealed that the most prevalent research foci represented by the most commonly used CSER keywords were teaching learning and broadening participation, which aligned with the corresponding strategic directions. However, our analysis also suggested a misalignment between keywords identified by authors and the topics presented in the abstracts. With our work, we hope to motivate scholars to carefully evaluate and select keywords for indexing publications so as to improve the research topic relevancy and publication visibility for broader impact.

publication date

  • July 26, 2021