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Teaching algorithms plays a crucial role in developing computational thinking among learners in the core curricula of the Moroccan education system. However, many students face significant challenges when testing and applying algorithms. This situation hinders their understanding of basic programming concepts and slows their overall progress in computer science education. Mobile learning can significantly enhance computer science teaching methods by improving the process of converting algorithms to programs using teacher-developed software. This approach allows students to overcome the obstacle of translating algorithms into code and data, enhancing their learning of programming and contributing to improved academic outcomes in computer science. This study aimed to investigate the impact of integrating mobile learning programs into the algorithm-to-programming process in C++ programming education for computer science students. The research was conducted on a sample of 112 students (aged 15–17) from three grades. A descriptive correlational approach was used to explore the relationship between M-learning-based learning and student performance. Pearson's correlation coefficient was applied to analyze students' scores on the algorithm processing test using teacher-developed software, as well as their responses to a questionnaire to assess their learning progress. Data were processed and analyzed using the Statistical Package for the Social Sciences (SPSS). The results highlight the potential benefits of integrating AlgoScan technology into programming education, which enhances students' computational thinking, encourages them to design advanced algorithms, and improves their ability to handle complex programs. Although implementing this educational approach requires smaller student groups, the results indicate that it can be useful in various classroom activities, given the widespread use of smartphones in education.
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DOI: 10.1109/icoa66896.2025.11236928
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