Alk: A Formal-Methods-based Educational Platform for Enhancing Algorithmic Thinking

Abstract

Algorithm design courses are fundamental to computer science curricula, but fostering algorithmic thinking in students is challenging due to the diverse skills and creativity required. Dedicated teaching support tools can help both course instructors and students in this effort. We have developed the Alk platform to promote algorithmic thinking, leveraging the theoretical foundations of Matching Logic. Alk features an intuitive algorithm language that provides a flexible computational model suitable for analysis, symbolic execution, and checking properties of algorithms. In this paper, we present an overview of the Alk platform tool and demonstrate, through use cases, how it fosters various algorithmic thinking skills. We conclude that the Alk platform is a valuable tool for learning and teaching algorithms, effectively enhancing students’ understanding and skills. Future work will extend its capabilities of supporting symbolic execution, probabilistic algorithms, as well as estimation of execution time, further broadening its impact on computer science education.