Gamification refers to the use of elements of games in non-game contexts and has been applied in workplace, marketing, health programs and other areas, with mounting evidence of increased interest, involvement, satisfaction and performance of the participants. More recently gamification has been emerging as a teaching method that has a great potential to improve students’ motivation and engagement. Gamification in education should not be confused with playing educational games, as it only uses concepts such as points, leader boards, etc, rather than computer games themselves. In this talk we describe the gamification of a theoretical computer science course we performed in 2014/2015/2016 as well as our experience with two other STEM courses.

Parameterised approximation is a relatively new but growing field of interest. It merges two ways of dealing with NP-hard optimisation problems, namely polynomial approximation and exact parameterised (exponential-time) algorithms.

We explore opportunities for parameterising constant factor approximation algorithms for vertex cover, and we provide a simple algorithm that works on any approximation ratio of the form $\frac{2l+1}{l+1}$, $l=1,2,\dots$, and has complexity that outperforms previously published algorithms by Bourgeois et al. based on sophisticated exact parameterised algorithms. In particular, for $l=1$ (factor-$1.5$ approximation) our algorithm runs in time $\text{O}^*(\text{simpleonefiveapproxbase}^k)$, where parameter $k \leq \frac{2}{3}\tau$, and $\tau$ is the size of a minimum vertex cover.

Additionally, we present an improved polynomial-time approximation algorithm for graphs of average degree at most four and a limited number of vertices with degree less than two.