<![CDATA[This study aims to implement a behavioristic approach in learning algorithms and programming with Python and explore the factors supporting and hindering the process. The research uses a descriptive qualitative method with the subjects being students from the Mathematics Education Study Program. Data were collected through triangulation, which included observation, interviews, and documentation. The results indicate that the implementation of the behavioristic approach significantly improved students' systematic thinking skills, with an average increase of 25% in algorithm understanding and problem-solving skills. Seventy-five percent of students reported increased confidence in solving algorithm problems. These findings support the principle of positive reinforcement in behavioristic learning theory. This study recommends integrating the behavioristic approach into the curriculum and further research to explore individual factors affecting learning outcomes. Thus, this study contributes to the development of more effective teaching methods in mathematics education and programming.]]>
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