Comparative analysis of pseudocode and flowcharts in algorithm development among first-year computer science students

Algorithm development is a fundamental skill in computer science education, yet students struggle to translate problemsolving into structured logic. Flowcharts and pseudocode support algorithmic thinking, but limited research has examined how students perceive and prefer these methods. This study explores first-year computer science students' perceptions and preferences about using flowcharts, pseudocode, or both in algorithm development, and examines how effective these tools are on students’ perceptions. 95 students learned both tools via instruction and exercises, then their perceptions were surveyed. Data analysis included descriptive stats, t-tests, ANOVA, and regression. Results showed no significant perception difference (p > 0.05), indicating equal value for both tools. Regression analysis further showed that the perceived effectiveness of both tools significantly contributed to the students’ perception in learning algorithms development (p-value < 0.05). The study concludes that either approach can effectively support diverse learning styles and enhance algorithmic thinking. It is recommended that future research explore the use of other tools for algorithm development, such as algorithm animation software, block-based programming environments, or interactive visualization platforms, and investigate their longterm impact on programming performance and retention.