Universitat Politècnica de Catalunya

Purpose This study examines whether game-based assessment (GBA) can improve multidisciplinary team formation in undergraduate project courses. Specifically, it tests whether composing teams using competency profiles from a brief video-game diagnostic of applied mathematics and problem solving (Go Timmy Go, GTG) yields better project outcomes than convenience-based grouping, under normal course conditions. Design/methodology/approach A feasibility-focused, course-embedded intervention compared seven GBA-informed teams with twenty-seven convenience-grouped teams across two paired undergraduate courses (Medicine and Engineering) at a Colombian university. All teams completed the same multidisciplinary project and were evaluated by a panel using a common rubric. Pre-specified, team-level analyses assessed three outcomes (disciplinary performance, interdisciplinary integration (I-level), and transdisciplinary attainment (T-level)) using difference-in-proportions tests, Fisher's exact tests, and standardized effect sizes. Findings GBA-informed teams showed no statistically or practically significant advantage on any outcome. These null results are interpreted through two mechanisms: (a) misalignment between what GTG measures (taskwork competencies) and what transdisciplinary outcomes require (socio-relational capacities such as coordination and communication), and (b) limited diagnostic coverage (19.75% of students received deep profiles), which constrained both the teaming space and statistical power. Qualitative indicators suggest GBA functioned as a formative catalyst (supporting self-efficacy and strengths recognition) rather than as a standalone team-assignment optimizer. Originality/value This is among the first pre-specified evaluations of GBA-informed team assignment under authentic course conditions. By reporting and interpreting null effects transparently, the study reframes GBA from an optimization tool to a formative resource for role negotiation, coaching, and feedback. It offers actionable guidance (including a decision checklist, integration heuristics, and cost–benefit considerations) for educators and administrators considering GBA adoption in higher education teamwork.

In 2013, Bollobás, Mitsche, and Prałat gave upper and lower bounds for the likely metric dimension of random Erdős-Rényi graphs $G(n,p)$ for a large range of expected degrees. However, their results only apply when $d=pn=\omega(\log^5 n)$, leaving open sparser random graphs with $d=O(\log^5 n)$ or $d=o(\log^5n)$. Here we provide upper and lower bounds on the likely metric dimension of $G(n,p)$ in a range of $d$ starting just above the connectivity transition, i.e., where $d=c \log n$ for some constant $c > 1$, up to $d=O(\log^5 n)$. Our lower bound technique is based on an entropic argument which is weaker but more general than the use of Suen's inequality by Bollobás, Mitsche, and Prałat, whereas our upper bound is similar to theirs.