The protection of students’ privacy in learning analytics (LA) applications is critical for cultivating trust and effective implementations of LA in educational environments around the world. However, students’ privacy concerns and how they may vary along demographic dimensions that historically influence these concerns have yet to be studied in higher education. Gender differences, in particular, are known to be associated with people’s information privacy concerns, including in educational settings. Building on an empirically validated model and survey instrument for student privacy concerns, their antecedents and their behavioral outcomes, we investigate the presence of gender differences in students’ privacy concerns about LA.
We conducted a survey study of students in higher education across five countries (N = 762): Germany, South Korea, Spain, Sweden and the United States. Using multiple regression analysis, across all five countries, we find that female students have stronger trusting beliefs and they are more inclined to engage in self-disclosure behaviors compared to male students. However, at the country level, these gender differences are significant only in the German sample, for Bachelor’s degree students, and for students between the ages of 18 and 24. Thus, national context, degree program, and age are important moderating factors for gender differences in student privacy concerns.
The paper its freely accessible at ACM: René F. Kizilcec, Olga Viberg, Ioana Jivet, Alejandra Martínez Monés, Alice Oh, Stefan Hrastinski, Chantal Mutimukwe and Maren Scheffel. 2023. The Role of Gender in Students’ Privacy Concerns about Learning Analytics: Evidence from five countries. In LAK23: 13th International Learning Analytics and Knowledge Conference (LAK 2023), March 13-17, 2023, Arlington, TX, USA. ACM, New York, NY, USA, 10 Pages.