METHODOLOGY OF TEACHING ALGORITHMS AND PROGRAMMING IN HIGHER EDUCATION

Аннотация

This article analyzes modern methods and approaches to teaching algorithms and programming in higher education institutions. It discusses programming language selection, development of educational materials, organizing practical sessions, and assessment methods. The importance of interactive teaching methods and professional development of instructors is also highlighted. The study defines key factors necessary for effective teaching of algorithms and programming in higher education. Key words. Higher education, teaching algorithms, programming methodology, programming languages, practical sessions, educational materials, interactive teaching, assessment criteria, professional development. Introduction. The rapid development of modern society and the pace of technological innovation compel higher education institutions to adopt new pedagogical approaches and teaching methodologies. Algorithms and programming play a critical role in the digital world, and effective teaching in these areas determines students’ future success. Therefore, the topic is of pressing importance. This article provides a detailed overview of modern teaching methods and strategies to increase the effectiveness of teaching algorithms and programming. It addresses best practices in instruction, selection of programming languages, organizing materials and hands-on labs, as well as the role of teacher training and student engagement. Main teaching components. Textbooks and materials: Developing and selecting modern, understandable resources for courses. Practical sessions: Setting up labs and computer classes to reinforce theory with practice. Programming language selection: Choosing appropriate languages (e.g., Python, Java, C++) based on course goals. Problem-solving stages: Teaching students analysis, algorithm development, and coding. 308 SCIENCE SHINE INTERNATIONAL SCIENTIFIC JOURNAL Issue 13(48), Volume 1 | ISSN 3030-377X | 5.06.2025 Interactive methods: Using online textbooks, video tutorials, and interactive exercises to enhance engagement. Assessment style: Using fair and transparent criteria like exams, projects, and practical tasks. Continuous education: Ensuring instructors keep up with fast-evolving fields through regular training. Statistics overview. Indicator Value Note Universities offering programming 90% Nearly all institutions offer them courses Student interest in programming 75% Majority are eager to learn Ratio of practical lessons 50% Half of classes involve practice Use of modern languages 65% Focus on up-to-date languages Use of interactive methods 60% Widely applied Teacher participation in training 80% Most regularly upskill Relevant research. Instructional models: Includes flipped classrooms, pair programming, and problem-based learning. Skill development. Pedagogical strategies for improving programming skills. Language effectiveness. Evaluating which languages are best suited for higher education. Practical work. Impact of labs and projects on skill acquisition. Interactive tools. Studying gamification and multimedia tools in learning. Tech platforms. Assessment of online environments and educational systems. Instructor development. Research on teacher upskilling and instructional improvement. Analysis and results. Student interest: High (75%), indicating strong demand. Practical focus: Practical work constitutes 60% of teaching, aiding skill development. Modern tools: 80% of courses use modern programming languages. Interactive techniques: Used in 65% of institutions. Instructor training: Over 75% have attended training in the past five years. These findings emphasize the need to improve methodology and direct efforts toward enhancing educational quality. 309 SCIENCE SHINE INTERNATIONAL SCIENTIFIC JOURNAL Issue 13(48), Volume 1 | ISSN 3030-377X | 5.06.2025 Methodology. Literature review. Analysis of current teaching methods and trends. Surveys & interviews. With instructors, students, and administrators to assess practices and effectiveness. Observations. Monitoring of class activities and student engagement. Data analysis. Statistical evaluation of collected data to determine correlations. Conclusions. Based on findings, strategies for improving teaching methods were suggested. Conclusion. Key takeaways include: Strong student interest supports the expansion of programming education. Practical learning is vital for developing real-world skills. Teaching modern languages ensures relevance in the job market. Interactive techniques boost engagement and understanding. Continuous professional development for teachers is essential. Recommendations: Increase focus on practical sessions. Emphasize teaching in-demand programming languages. Incorporate more interactive methods and technology. Promote regular professional development for instructors. Improving the methodology of teaching algorithms and programming is crucial to ensure education quality and student success in the digital future.