The Role of Gender in Citation Practices of Learning Analytics Research

Mounting evidence indicates that modern citation practices contribute to inequalities in who receives citations. In response to this evidence, our paper investigates citation practices in learning analytics (LA). We analyse citations in papers published over ten years at the Learning Analytics and Knowledge conference (LAK). Our analysis examines the gender composition of authored and cited papers in LA, estimating various factors that explain why one paper cites another, and if the citation rates differ across different author teams. Results indicate an overall increase in the number of women authors at LAK, while the ratio of men to women remains stable. Citation patterns in LAK are influenced by the seniority of authors, paper age, topic, and team size. We found that LAK papers with women as the last author are under-cited, but papers where the first author is a woman and the last author is a man are over-cited. Author teams with different gender composition also vary in who they over- and under-cite. Upon presenting the empirical results, the paper reflects on the role of mindful citation practices and reviews existing measures proposed to promote diversity in citations.