Gamification, Peer Networks, and the Reconstruction of Sociograms: A Social Network Analysis in Higher Education

Background: Gamification is widely used in higher education, yet its consequences for the relational structure of a class are rarely measured, and sociometric data are often analyzed without attention to how the underlying matrix is built. Methods: We report an exploratory, single-case, pre–post study of a gamified master’s course in Didactics and Media Education (N = 19), with a dual aim: to demonstrate a transparent procedure for reconstructing directed, weighted sociograms from raw questionnaire responses, and to explore how the peer-preference network changed over one semester of cooperative group work; inference used permutation methods suited to a small single network. Results: A pre-existing matrix contained roughly one-third of ties with no basis in the data, doubling the apparent centralization and mischaracterizing the least-chosen, most-rejected student as the most popular hub; reconstruction reversed these conclusions. On the reconstructed network, cohesion increased and previously isolated students were integrated, while centralization stayed stable—integration without hierarchy—with a reciprocal, transitive, and highly stable structure and no demographic homophily. Conclusions: Social network analysis (SNA) is a sensitive instrument for the relational evaluation of gamified courses, but the construction of the sociomatrix is itself a substantive analytic decision that must be reported transparently.