The transfer of educational activities to the online environment within blended learning, which was also accelerated by the COVID-19 pandemic, increases the risk of growing student procrastination. This article describes the design of the flipped class, which is designed so that students are supported and motivated to continuously perform individual tasks. Great emphasis in the described design of the flipped classroom is placed on supporting students in their activities outside the classroom. It is in this part of blended learning that procrastination is a frequent cause of students’ failure, not just in mathematics. The effectiveness of our proposed inverted class design has been experimentally verified. Statistical analysis of the data showed that students had a statistically significant reduction in procrastination behavior during the course of the experiment. The proposed flipped classroom design has the potential to increase students’ self-regulatory skills, which has been reflected in a change in their approach to learning responsibilities. Students’ approach to online learning outside the classroom has changed, and thus their probability of successfully completing the combinatorics course has increased statistically significantly.

Modernization and the ever-increasing trend of introducing modern technologies into various areas of everyday life require school graduates with programming skills. The ability to program is closely related to computational thinking, which is based on algorithmic thinking. It is well known that algorithmic thinking is the ability of students to work with algorithms understood as a systematic description of problem-solving strategies. Algorithms can be considered as a fundamental phenomenon that forms a point of contact between mathematics and informatics. As part of an algorithmic graph theory seminar, we conducted an experiment where we solved the knight’s tour problem using the backtracking method to observe the change in students’ motivation to learn algorithms at a higher cognitive level. Seventy-four students participated in the experiment. Statistical analysis of the results of the experiment confirmed that the use of the algorithm with decision-making in teaching motivated students to learn algorithms with understanding.

Restricting the movement of students because of COVID-19 requires expanding the offer of online education. Online education should reflect the principles of pedagogical constructivism to ensure the development of students’ cognitive and social competencies. The paper describes the preparatory course of mathematics, realized in the form of MOOC. This course was created and implemented based on the principles of pedagogical constructivism. The analysis of the respondents’ approach to MOOC revealed a difference between bachelor and master students in the use of MOOC. Bachelors found a strong correlation between their approach to MOOCs and the way they are educated in secondary schools. The results of the research point to the need of more emphasis should be placed on advancing the learner’s skills in navigating and analysing information. The questionnaire filled in by the participants also monitored the students’ access to learning. The results of the experiment confirmed the connection between the preferred approach to learning and students’ activities within the MOOC.

To participate fully in society, a person needs to critically evaluate statistical information. To be able to do this, it is appropriate to start developing the statistical literacy of young people in diverse levels in schools. In our article, we present a modified seminar on statistics for prospective humanities teachers. During the seminar, we planned a pedagogical experiment, the aim of which was to statistically verify the students’ own diagnostic competencies as prospective teachers of humanities. The statistical analysis confirmed that the implementation of real research into the course on statistics has a positive impact on the development of statistical literacy but does not change the attitude of students toward statistics and its use in the future teaching practice.

Active cooperation is expected from the student in the education center, which is associated with fears of expressing their opinions, because a possible mistake may result in a negative reaction from the environment. In our research, we investigated the impact of team psychological safety feelings on students' willingness to engage in active learning. 244 students aged 18 and 19 participated in the research. A mixed methods approach was used to obtain the necessary data. By data analysis, we revealed three separate dimensions in respondents' answers to questionnaire items. According to our findings, students' involvement in active learning is strongly supported by their internal motivation to acquire new knowledge and trust in the class collective. A sense of psychological team safety has an important place in encouraging the student to engage in common activities. At the same time, we found that the teacher has a decisive influence on building the student's trust in the class team.

The present contribution is devoted to the theory of fuzzy sets, especially Atanassov Intuitionistic Fuzzy sets (IF sets) and their use in practice. We define the correlation between IF sets and the correlation coefficient, and we bring a new perspective to solving the problem of data file reduction in case sets where the input data come from IF sets. We present specific applications of the two best-known methods, the Principal Component Analysis and Factor Analysis, used to solve the problem of reducing the size of a data file. We examine input data from IF sets from three perspectives: through membership function, non-membership function and hesitation margin. This examination better reflects the character of the input data and also better captures and preserves the information that the input data carries. In the article, we also present and solve a specific example from practice where we show the behavior of these methods on data from IF sets. The example is solved using R programming language, which is useful for statistical analysis of data and their graphical representation.