APPLICATION OF DATA MINING TO MONITOR THE LEARNING OUTCOMES OF HIGHER EDUCATION STUDENTS
APPLICATION OF DATA MINING TO MONITOR THE LEARNING OUTCOMES OF HIGHER EDUCATION STUDENTS

Summary/Abstract: Analysis of student test results is an important stage in the learning process. It provides valuable information about the effectiveness of the learning process, identifies problem areas and develops measures to address them. The problem of choosing a method for assessing the level of training, the quality of knowledge, skills and abilities is extremely important. In order to improve the efficiency of the educational process in the discipline "Programming", a comprehensive analysis of student test results was carried out. The study included both quantitative data analysis (average score, percentage of correct answers) and qualitative analysis of typical mistakes and difficulties faced by students. Five tests covering the entire spectrum of the C programming discipline were selected for the study. The use of the R programming language allowed for in-depth data analysis and a detailed picture of students’ learning achievements. The study proves that data analysis can be a powerful tool for improving the educational process. A detailed analysis of the test results of students allowed us to identify weaknesses in the curriculum and develop a model that can predict student performance. The analysis can be used to create more individualized curricula and improve the overall efficiency of the educational proces.

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