Assessing the Knowledge of Students in the Field of Computer Networks Using Associative Experiments

The presented study compares the freeform and directed types of linguistic associative experiments. The objective of these experiments was twofold: firstly, to evaluate the effectiveness of each method in analyzing the level of students' knowledge in a particular subject area, and secondly, to gain insight into their knowledge model. The analysis of the results showed that the directed experiment allows obtaining more accurate reactions characterizing the student's knowledge of the chosen subject area. It is better suited to work with students of profile directions, but requires more resources for conducting and processing the data. The freeform experiment, on the contrary, is less labor-intensive and allows obtaining a wide range of associations reflecting a general picture of students' knowledge. However, its results may not be as structured and require additional interpretation. Both methods have advantages and disadvantages. The choice between them should be based on specific research objectives and available resources. The introduction of modern information and communication technologies, including neural networks, artificial intelligence and machine learning, can greatly simplify data processing and speed up the process of building an infocognitive knowledge model from the collected data.