Enhancing Algorithmic Thinking Through Semantic Waves: Integrating Necessity Learning Design in Computer Science Education

In today's technological age, algorithmic thinking and problem-solving skills are essential. To foster these skills, educators need accessible teaching methods for effective lesson planning. This study presents a teaching approach that employs the theory of the semantic wave to connect abstract concepts with practical applications, with the aim of improving students' algorithmic thinking and digital skills while promoting deeper learning. Tested in a CS teaching-learning lab, pre-service teachers practiced teaching K-12 students and refined their methods for teaching computer science. While previous studies have shown promise, this research advances the approach by integrating Necessity Learning Design (NLD), a method that builds on students' prior knowledge through structured tasks, improving problemsolving skills and significantly increasing algorithmic thinking. This “solve first, teach later” approach aligns with the principles of productive failure, where students initially attempt to solve problems on their own, encountering challenges that prepare them for deeper learning when guidance is later provided. The novelty of this work lies in the application of semantic wave theory with the Necessity Learning Design, which was not explored in CS education so far. Results provide valuable insights for pre-service computer science teachers and demonstrate the potential of this pedagogical approach to enrich both conceptual understanding and practical skills for the challenges of digital transformation.