Development of the Improved Exercise Generation Metaheuristic Algorithm EGAL+ for End Users

Abstract

Exercise generation is a subject worthy of investigation. In our previous papers, a new multi-objective harmony search metaheuristic algorithm called EGAL was presented, designed to address a widely recognised problem: generating diverse exercises to measure students’ knowledge on various topics. An improved metaheuristic algorithm (EGAL+) has since been created, and it is presented in this study. The aim of this research was to further develop EGAL and to investigate the differences between the original and the new algorithm. This newly acquired algorithm preserved the advances of EGAL – the generated exercises cover as many areas of the course as possible, the difficulty of the exercises are equal, and they are diverse. Moreover, the improved algorithm is also usable for non-expert users, since the introduced input fields are restricted to the ones which are freely editable. It is sufficient for the user to be proficient in their own field and to operate the program with subject-specific questions. These statements were confirmed by running EGAL+ on a large number of samples.