  Cybersecurity games are an attractive and popular method of active learning.
However, the majority of current games are created for advanced players, which
often leads to frustration in less experienced learners. Therefore, we decided
to focus on a diagnostic assessment of participants entering the games. We
assume that information about the players' knowledge, skills, and experience
enables tutors or learning environments to suitably assist participants with
game challenges and maximize learning in their virtual adventure. In this
paper, we present a pioneering experiment examining the predictive value of a
short quiz and self-assessment for identifying learners' readiness before
playing a cybersecurity game. We hypothesized that these predictors would model
players' performance. A linear regression analysis showed that the game
performance can be accurately predicted by well-designed prerequisite testing,
but not by self-assessment. At the same time, we identified major challenges
related to the design of pretests for cybersecurity games: calibrating test
questions with respect to the skills relevant for the game, minimizing the
quiz's length while maximizing its informative value, and embedding the pretest
in the game. Our results are relevant for educational researchers and
cybersecurity instructors of students at all learning levels.
