We conducted a study to compare the development of algorithmic thinking skills among first-year Hungarian and international computer science students during their Introduction to programming course. The research was carried out in multiple phases, including an initial assessment during the first lecture of the semester, a mid-term evaluation, and a final assessment at the end of the semester.

The sample included 203 Hungarian and 182 international students who came to the university from various parts of the world. During the course, the same conditions were provided for both groups in Hungarian and foreign, respectively, with the goal of the course being to provide students with a solid foundation in programming. The Python programming language was used in the course to introduce basic algorithms and data structures. On the first day of the semester, we conducted a general IT knowledge assessment and a self-evaluation test among the students. The aim was to gain a comprehensive picture of their prior knowledge. Following this, additional tests were conducted throughout the semester to measure their performance. We conducted statistical analyses to compare the results.

The results of our study revealed significant differences between Hungarian and international students in terms of their prior knowledge, engagement with coursework, and overall academic performance. In particular, we observed notable disparities in their familiarity with computational tools, class participation, and grade distribution. These findings suggest that students' educational backgrounds and previous exposure to programming concepts may influence their ability to acquire and apply algorithmic thinking skills. Furthermore, we found a significant difference in attendance statistics between the two groups, with international students having a much higher rate of absenteeism. Several explanations can account for this. As first-year students, some of them start their studies later due to visa issuance delays. Without prior knowledge, it becomes much more difficult for them to catch up on missed content. This may support the higher dropout rate observed among international students. In addition, cultural differences also become apparent over the course of the semester.

In this paper, we present a detailed analysis of the multi-stage assessment process and its outcomes, highlighting key distinctions between the two student groups. By examining these differences, we aim to contribute to a better understanding of the factors that impact students' learning experiences in computer science education and propose potential strategies for improving teaching methodologies to accommodate diverse educational backgrounds. Furthermore, we explore effective approaches to enhancing algorithmic skills development, ensuring that students, regardless of their prior experience, can build a strong foundation in computational problem-solving and logical reasoning.

Keywords: Computer science students, concept of informatics, algorithmic skills, computational problem-solving.