Learning by Game-Building in Theoretical Computer Science Education

Abstract

It has been suggested that theoretical computer science (TCS) suffers more than average from a lack of intrinsic motivation. The reasons provided in the literature include the difficulty of the subject, lack of relevance to the informatics degree or future careers, and lack of enjoyment of the learning experience. This thesis presents evidence of these claims derived from the results of an expert survey. Increasing the students’ perceived control of the learning experience has been shown to increase student motivation in numerous different settings. A few of those also showed increased student performance. This thesis proposes that game-building can be such a setting for the area of TCS. Within the area of TCS, the focus will be on the modelling skills (finite state automata, push-down automata, Turing machines, CCS, etc.) since they form the majority of the curriculum at undergraduate level. It will be demonstrated how arbitrary TCS modelling skills can be mapped onto a game-building framework and allow the students to learn about the former by using the latter. It is hypothesized that the success of the approach depends on the amount of control given to the student. To test this claim, two experimental conditions were used in a repeated-measures design: (1) own-game and (2) pre-defined game. In the former, students are asked to write a game of their own, whereas in the latter, they are asked to copy a pre-defined game. A large demand for the own-game context was observed and results of its effect on performance and enjoyment are presented. Although no main effect of the owngame condition versus the pre-defined game condition was found in terms of either enjoyment or performance, some interesting interaction effects between condition and motivational type were unveiled.