Dataset of qualitative coding of 31 Youtube videos on the h-index. The study aimed to characterize educational videos about the h-index to understand available resources and provide recommendations for future educational initiatives. Data.csv: contains the metadata and qualitative coding for 31 videos. ReadMe.csv: contains the codebook including a description of variables. Abstract. The authors analyzed videos on the h-index posted to YouTube. Videos were identified by searching YouTube and were screened by two authors. To code the videos the authors created a coding sheet, which assessed content and presentation style with a focus on the videos’ educational quality based on Cognitive Load Theory. Two authors coded each video independently with discrepancies resolved by group consensus. Thirty-one videos met inclusion criteria. Twenty-one videos (68%) were screencasts and seven used a “talking head” approach. Twenty-six videos defined the h-index (83%) and provided examples of how to calculate and find it. The importance of the h-index in high-stakes decisions was raised in 14 (45%) videos. Sixteen videos (52%) described caveats about using the h-index, with potential disadvantages to early researchers the most prevalent (n=7; 23%). All videos incorporated various educational approaches with potential impact on viewer cognitive load. Most videos (n=21; 68%) displayed amateurish production quality. The videos featured content with potential to enhance viewers’ metrics literacies such that many defined the h-index and described its calculation, providing viewers with skills to recognize and interpret the metric. However, less than half described the h-index as an author quality indicator, which has been contested, and caveats about h-index use were inconsistently presented, suggesting room for improvement. While most videos integrated practices to facilitate balancing viewers’ cognitive load, few (32%) were of professional production quality. Some videos missed opportunities to adopt particular practices that could benefit learning.

This project is funded by Social Sciences and Humanities Research Council of Canada (SSHRC) Insight Grant #435-2021-0108 "Metrics Literacies: Improving the understanding and appropriate use of scholarly metrics in academia"