1. Introduction
In today’s globalized economy, education is a major driving force for growth and development. This fact is reflected in the international school policy presented by the OECD Directorate for Education and Skills. With regard to the significant challenges education systems worldwide are currently facing, the OECD Directorate for Education and Skills ranks as the key the question of how to improve the quality of teachers, teaching and learning in order to provide the knowledge and skills needed in the 21st century. According to Andreas Schleicher, the OECD director of education and skills, teachers are the key in today’s knowledge economy, in which a good education is an essential foundation for every child’s future success. That is why a quality initial teacher preparation program, which prepares prospective teachers for the challenges of today’s classrooms, is essential to ensure teacher quality [1].
In the literature, one can find different terms used by the authors in relation to the teacher career performance, e.g., qualification, professionalism, expertise, mastery or competence [2,3,4]. A unifying feature of the use of all these different terms is the authors’ consistent attitude that the teacher’s professionalism, qualification, expertise, mastery, and competence significantly impact instruction and students’ achievements. In general, one can state that the success of teaching practice can be measured in terms of teachers’ ability to initiate and support learning processes that enable students to achieve specific pedagogical objectives [5].
According to Baumert and Kunter [6], the notion teachers’ professional competence covers the qualities that teachers need in order to meet the demands of their profession. These are related mainly to teachers’ pedagogical content knowledge, professional beliefs, work-related motivation and self-regulation. Within this context, Kleickmann et al. [7] stress the difference between the quality of teachers’ content knowledge and pedagogical content knowledge, i.e., content knowledge competence and pedagogical content knowledge. Knowledge of the content (subject matter content knowledge) and the teaching of a subject (general pedagogical knowledge) are two key elements of teachers’ professional competence (the key competences, besides the others related to curricular knowledge, the knowledge of learners, the knowledge of the philosophical and historical aims of education, organizational knowledge and counselling knowledge [8,9,10].
In the majority of European countries, the professional preparation of teachers is the responsibility of higher education institutions. This responsibility includes not only the responsibility for teacher pre-service training but also the responsibility for designing the curriculum of the training. In the vast majority of OECD and partner countries, prospective primary and secondary school teachers of general subjects must receive courses in pedagogical/educational sciences studies, including child/adolescent studies, academic subjects and their branch didactics, and they must participate in a teaching practicum [11]. However, changes in the development of society cause an observable constant increase in the requirements imposed on the education process and competencies of teachers. Unlike in the past, teachers are required to teach in the ever-increasing multicultural environment of classes, to integrate pupils with special needs into common teaching, to constantly innovate the teaching process and, last but not least, to use teaching tools based on digital technologies to increase teaching efficiency [12]. However, at the time when many teachers had completed their pre-service training, most of these tools were not even available and teacher training was designed to focus on a narrower range of requirements. In addition, digital technologies are currently undergoing very rapid further development and their applications in school teaching are constantly being innovated [13]. These facts lead both to the need to continuously innovate pre-service teacher preparation in order to create their professional digital literacy and to the need to offer teachers opportunities in practice to further develop and upgrade their digital literacy. Changes will need to be made not only in the conception of what constitutes effective professional practice but also in the purposes, structure and organisation of pre-service and professional learning opportunities [14,15]. In this context, disjointed teacher learning opportunities should be replaced with more integrated continuums of teacher preparation, induction, support and ongoing professional development [16].
As Symeonidis [17] and Krumsvik [18] proclaim, the formation of the professional didactic-technological competencies of teacher trainees in the focus on developing their professional digital literacy and ability to use interactive educational means in school teaching has two dimensions. One dimension is associated with identification of digital means and the acquisition of work which it is necessary to include in the professional training of teachers. The second dimension relates to the identification of aspects of the teaching process, which can be significantly influenced by these means within the teaching of a given subject, i.e., the purpose for which to include these means in teaching in order to increase its effectiveness. The questions are how to design an optimal model of training future teachers in the field of didactic-technological competencies, which specific digital didactic means to use and which aspects of their use in the teaching process should be emphasized.
The quality of higher education was considered as the basic priority of higher education development during the last several decades in many countries of the world. The main principles of quality assurance were formulated in an official document known as the European Standards and Guidelines (ESG–The Standards and Guidelines for Quality Assurance, [19,20,21]. In the context of the ESG, the necessity to upgrade or innovate higher education institution study programs belongs to the key components of the sustainability of higher education institution quality assurance.
The stated facts, together with the approach of the expected time horizon of the complex study program accreditation of universities in the Slovak Republic (2024), were the reasons for the preparation and realization of extensive research aimed at creating a platform for designing an optimal model of pre-service teacher training in didactic-technological competencies, with emphasis on their professional digital literacy. The research in question, the particular results of which are presented in this paper, was carried out in the period 2017–2019 within the national project ‘Innovation of pre-service teacher training in the field of didactic-technological competencies’ (
Due to the common historical development of Slovak and Czech education (within the former Czechoslovakia), it was decided to draw attention not only to the situation at Slovak universities but also to the state of the subject issue at universities in the Czech Republic. The main purpose of the research was to evaluate the state of education in the area of the use of modern didactic means within selected teaching study programs provided by faculties of universities in the Slovak Republic and the Czech Republic in order to optimize the formation and development of didactic and technological competencies from the perspective and needs of their future graduates.
The described research examined the importance of the integration of various kinds of digital didactic tools into pre-service teacher training curricula with regard to the successful performance of the teaching profession.
2. The Background of the Research
As noted before, several studies in the field of education research in Slovakia and the Czech Republic [22,23,24,25] offer a reflection of the current state, but above all they are focused on the perspectives of education. In this context, experts at faculties of education think about and describe what is needed to be done to improve pre-service teacher training, analysing the readiness of their graduates for a career as a teacher from various aspects. Some of these studies are orientated upon the innovation of the relevant part of the curricula of teaching study programs in the focus on creating/developing teacher trainees’ professional digital literacy and ability to use digital educational means in school teaching. The content of each university study program is based on the current requirements of the practice and research results of what the graduates of the relevant study program should be able to perform in the profession for which they are prepared. Therefore, if we want to improve the professional training of future teachers in the field of the formation of their didactic-technological competencies, it is necessary to identify the current requirements placed on teachers, and especially the needs of teachers related to the development and application of their professional digital competencies.
One of the particular tasks of the project, within which the presented research was carried out, was to analyse the ways in which educational disciplines focused on the development and application of digital technologies in educational processes are integrated into the teaching study programs of selected higher education institutions in Slovakia (Faculty of Education, Comenius University in Bratislava; Faculty of Natural Sciences, Constantine the Philosopher University in Nitra; Faculty of Education, Matej Bel University in Banská Bystrica) and the Czech Republic (Faculty of Education, Charles University in Prague; Faculty of Education, University of Hradec Králové; Faculty of Education, Masaryk University in Brno).
The object of the analysis was 25 study subjects of the relevant specialization in the Slovak Republic (two subjects in the master’s degree, 23 in the bachelor’s degree) and 30 subjects in the Czech Republic (four subjects in the master’s degree and 26 in the bachelor’s degree). As it turned out from the analysis, there are only slight differences in the number of the subjects of the mentioned profiling, as well as in their inclusion in the curriculum structures (including their inclusion into the structure of both bachelor’s and master’s degree study programs) and in the scope of their content as well as their time allocation [26]. Regarding the character of the subjects, in Slovakia as well as in the Czech Republic, they are most frequently taught as compulsory educational subjects (40%). In Slovakia, their integration into the group of optional subjects is a frequent occurrence; in the Czech Republic, they are also frequently taught as compulsory optional subjects. As to the time allocation (the numbers of lessons per week) of these subjects, there are no significant differences either within the Slovak Republic or between Slovakia and the Czech Republic. The most frequent time allocation for these subjects is two lessons per week. Regarding the integration of these subjects into the curricula of the study programs of future teachers, in Slovakia they are usually taught in the second and third semesters; in the Czech Republic they are mainly in the third and fourth semesters of the bachelor’s degree.
Another aspect of the analysis was how the subjects are completed and how they are assessed within the European Credit Transfer System (ECTS) (i.e., how many credits students can get for their successful completion). Most of the analysed subjects, specifically 65%, are completed by continuous assessment, while for successful completion of the course the student receives three or four credits. In Slovakia, the most common form of the teaching of these subjects is a seminar (64%) and, to a lesser extent, a combination of lectures and seminars (32%). In the Czech Republic, these subjects are taught mainly as seminars (56%), and less often as a combination of lectures and seminars (10%).
Within the European higher education area, there are several exact studies that describe what specifics should be included in the digital competence of graduates of pedagogical faculties with regard to the successful performance of their future teaching practice [27]. In this regard, Ørnes et al. [28] found that students in teacher training programs consider digital tools to be one of the key tools significantly influencing the quality of education. This is related to the possibilities which these means offer for cooperation between and among students and teachers, the possibilities of the use of online teaching materials, and also of the access to information and professional literature. Tømte et al. [29] found that the development of professional digital competence lacks support in most teacher training curricula, and that most teacher training programs lack a comprehensive approach to the development of these skills. They also found that the academic profiles of institutions dealing with the education of future teachers of primary and secondary education (ISCED 1 to ISCED 3) in this area are not sufficiently developed, and that the professionalism of academics is highly variable. The promotion of the professional digital competence of teacher trainees in many study programs depends only on enthusiasts from the ranks of academic staff. There are relatively few examples of good practice in which primary/secondary schools would indicate how significantly the acquired digital competence of a graduate of a teacher training program can be related to the appropriate effectiveness of teaching the relevant school subject. Tømte et al. [29] also emphasize the need to improve cooperation between faculties of education and training schools in which the practical part of the pre-service teacher training is carried out. Ottestad et al. [30] found, within their research survey, that few newly qualified teachers were satisfied with their knowledge and skills acquired through their pre-service teacher. At the same time, it was found that in-service teachers are very interested in the further development of their digital competencies, even though the schools in which they work do not create clear requirements for the use of digital teaching aids in teaching and educating their pupils and students.
The research results point out [31,32,33] that not all higher education institutions preparing future teachers incorporate the issue of digital literacy into the curricular structure of didactic-technological training with such intensity as would be necessary with regard to the requirements of practice. Digital competence is thus often underestimated or limited to activities related to searching for teaching and learning resources on the Internet. Therefore, after graduation, graduates are usually not erudite enough to integrate digital means into their teaching activities. On the other hand, it is necessary to provide the institutions participating in teacher trainee preparation with a clear concept of how the digital competence of teacher trainees should be formed and developed to meet the requirements of the practice.
3. Methodology of the Research
As mentioned in the introduction, the aim of the project ‘Innovation of pre-service teacher training in the field of didactic-technological competencies’ was to create a platform for the design of an optimal model of pre-service teacher training in didactic-technological competencies, with an emphasis on shaping their professional digital literacy. The main goal was achieved through the fulfilment of the particular goals, which were solved during the succession of the project phases (for more detail, see [34,35,36]).
One of the particular goals of the presented research, carried out within the above stated project, was to assess the importance of integrating different types of digital teaching means into the pre-service teacher training curricula with regard to the success of the teaching profession performance. In order to obtain relevant research data, the method of a questionnaire was used, the respondents of which were teacher trainees. For screening purposes, a questionnaire—developed by us—was used. Together with the questionnaire, we also prepared detailed instructions and rules for its administration and data processing. In order to solve the research problem we used the qualitative method of empirical measurement.
The survey respondents commented on the importance of integrating the issue of working with various types of digital means into the curricula of study programs of teacher trainees in terms of several aspects. Specifically, they assessed the importance of including nine thematic areas, i.e., learning topics focused on working with different types of digital products, in their pre-service training curricula in relation to the success of their future profession performance (i.e., in relation to teachers’ needs and requirements for the successful professional performance of their pedagogical activities). The areas assessed are presented in Table 1 (items B1–B9). The selection of these topics basically reflects the development trends and the situation in primary and secondary schools in Slovakia in terms of their material and technical equipment.
Respondents: Teacher trainees assessed each of the given nine thematic areas—B1–B9 (Table 1)—from five interrelated aspects:
(a). significance, i.e., the importance of including the given topic-software product into the pre-service teacher training study programs (B1.1–B9.1);
(b). the optimal time allocation of the subject within which the given topic would be taught (B1.2–B9.2);
(c). the obligation of teacher trainees to attend a subject within which the issue of the relevant topic-kind of the software product (B1.3–B9.3) would be taught;
(d). the importance of being skilled to work with software products of the relevant kind for a teacher of the particular subjects (for a teacher from the point of view of her/his major, B1.4–B9.4).
(e). the expectations which the respondents associate with passing a subject of the given topic in the frame of the teacher’s pre-service didactic technological preparation (B1.5–B9.5).
In the case of the first point of view, the respondents assessed the significance of including the given topic into the pre-service teacher training study programs using a 6-point scale in which the particular values meant:
6–definitely needed to incorporate,
5–needed to incorporate,
4–probably needed to incorporate,
3–probably not needed to incorporate,
2–not needed to incorporate,
1–definitely not needed to incorporate.
We deliberately did not include the choice of a neutral, emotionally indifferent assessment attitude, as we wanted to obtain from the respondents their clear opinions on the issues under evaluation.
On the extent of the time allocation for the subject within which the given topic would be taught, the teacher trainees stated their opinions through the choice of one of the four offered alternatives. The wording of the questionnaire item in this case was: “To what extent should work with the given software applications be included in the study program of teacher trainees?” Alternative answers were formulated as follows:
(a). together with other topics as a part of a one-semester subject;
(b). within a separate one-semester subject focused only on this issue;
(c). within a separate two-semester subject focused only on this issue;
(d). the teaching of this issue does not need to be included in the study program of teacher trainees.
As to the obligation of teacher trainees to attend a subject within which the issue of the relevant topic-kind of the software product (B1.3–B9.3) would be taught (i.e., to the character of the relevant subject), the respondents were asked to choose one of four alternative answers to the question: “Subject of the teacher training study program, in frame of which the issue of the work with the particular kind of software applications (B1–B9) should be taught, should be incorporated into the group of which subjects?” The alternative answers to this question were the following:
(a). between the compulsory subjects of the teacher trainees’ study programs,
(b). between the compulsory optional subjects of the teacher trainees’ study programs,
(c). between the optional subjects of the teacher trainees’ study programs,
(d). this issue does not need to be incorporated into the teacher trainees’ study programs.
In the first case, the respondents assessed the importance of including the work with the selected software products into the pre-service teacher training study programs from a “general” point of view, i.e., from the point of view of the professional activities of an “as such” (without any respect to his/her majors). In the fourth case, the respondents did the same, but with respect to the potential applicability of the acquired skills to work with the given means specifically in teaching their majors (the school subjects for teaching for which they were going to be qualified). In this case, they did it using a four-point scale with scale values meaning:
4–important, broadly applicable within the teaching of their majors;
3–rather important, applicable within the teaching of their majors in the same cases (situations);
2–rather unimportant, they are not very applicable within the teaching of their majors (they are very rarely applicable),
1–unimportant, not applicable in frame of their majors.
For each of the respondents we recorded a scale value according the level of the positive or negative assessment they stated for the particular item. The scale did not offer a choice of a neutral, emotionally indifferent assessment attitude, as we wanted to obtain the clearly stated opinions of the respondents on the given issue.
The last point of view—the fifth one, from which the respondents were asked to assess each of the selected items B1–B9—was the expectations which the respondents associate with passing a subject of the given topic in the frame of the teacher pre-service didactic technological preparation (responses B1.5–B9.5). This they did by choosing one of the offered alternative answers, i.e., the one which corresponded best with their attitude. The formulation of the questionnaire item was: “The passing of the subject devoted to the given issue I would associate with the fulfilment of my expectations in the area of:”
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(a). applying the acquired skills mainly in teaching my majors;
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(b). applying the acquired skills in teaching in general;
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(c). the general increase of my pedagogical competences (teaching mastery);
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(d). inspirational incentives to my performance of the teaching profession;
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(e). applying the acquired skills in the frame of teaching the extracurricular activities of pupils/students;
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(f). applying the acquired skills in the frame of my own personal spare-time activities.
The particular results of some of the stated survey focuses were already published [34,35,36]. Hereinafter we present the most current results of the research survey focused on the assessment of the thematic areas B1–B9 from the point of view of the significance/importance of including the relevant thematic area (learning topic) in pre-service teacher training study programs (items B1.1–B9.1), and from the point of view of the optimal time allocation of the subject within which the given topic would be taught (items B1.2–B9.2).
The identification data on the respondents were collected through a set of factual items (A1–A5) of the administrated questionnaire. These were the gender of the respondent (A1), the name of the higher education institution attended by the respondent (A2–university, A3–faculty), the attended level of study (A4), and the study program (A5). Factual item A2 also defines the country of the respondent’s study.
The research sample of the survey was created on the basis of a long-term cooperation of the project team members with other higher education institutions also participating in teacher training in the Slovak and Czech Republics. A further aspect was the approachability of these students. The research sample ultimately consisted of teacher trainees from three universities: two in Slovakia and one in the Czech Republic. As the respondents were teacher trainees, each of the three higher education institutions was represented by students from the Faculty of Education of the appropriate university. A detailed description of the research sample—consisting of 280 respondents—is presented in Table 2. As the data presented in Table 2 (processed on the basis of the recorded factual questionnaire items A1–A5) show, the research sample included 205 respondents from Slovakia and 75 from the Czech Republic: 205 students were from the Faculty of Education of Comenius University in Bratislava, 48 students were from the Faculty of Education of Charles University in Prague, and 27 students were from the Faculty of Education of the University of Hradec Králové. There were 49 men and 231 women).
The administration of the questionnaire was carried out parallel in Slovakia and the Czech Republic in the period from March to May 2019.
As part of the statistical data processing, some factual items were used as variables, depending on which results of the query were evaluated. One assumed that the answers of the respondents could be influenced mainly by the variables COUNTRY and GENDER, or their combination COUNTRY × GENDER. These variables represent the de facto segmentation factors depending on which the respondents’ answers were tested. The testing of the dependence of the respondents’ responses on the mentioned segmentation factors was performed by means of an analysis of variance for repeated measurements.
As the used questionnaire was created by us specifically for the research survey’s purposes, we considered it important to verify its reliability. The reliability was proved by the means of its suspicious items identification through the reliability/item analysis. From the total amount of 45 questionnaire items, 18 (ordinal data) of them were included in the statistical measuring in order to prove the reliability of the created data collection tool (items B1.1–B9.1; B1.4–B9.4). The total reliability of the questionnaire was evaluated by means of Cronbach’s alpha coefficient, a standardized alpha coefficient, and correlation. The calculated values (Total Mean = 69.521; Total StDv. = 9.669; Cronbach’s alpha = 0.870; Standardized alpha = 0.874; Average Inter-Item Correlation = 0.286) for the given ordinal items (B1.1–B9.1; B1.4–B9.4) indicated the high internal consistency of the developed tool and guaranteed reliable data collection for the broad-scale research that followed.
When we deleted particular items, the average score did not change significantly in the case of any of them (Table 3–Mean if deleted). None of the items (Table 3) increase the total variability of the questionnaire (StDv. if deleted < Total StDv.). Moreover, both estimations of the questionnaire’s reliability (Cronbach’s alpha, Standardized alpha) are approximately the same, what confirms approximately the same variability of the particular items. None of the items showed a correlation with the total score of the questionnaire less than the value of the average correlation between the items (Itm-Totl Correl. > Average Inter-Item Correlation). The coefficient of determination (Squared Multp. R) achieves approximately the same values for all of the tested items. No one of the items decreases the total reliability of the assessed questionnaire (Alpha if deleted < Cronbach’s alpha), i.e., based on the item analysis, none of the tested items is suspicious.
The measurement procedure as a whole can be—according to Taber [37]—considered to be fairly high, robust and internally consistent (Cronbach’s alpha > 0.8).
4. Results of the Research
The results of the statistical processing of the students’ answers to the question of significance, or the importance of including the given specified educational areas, i.e., the assessed types of software tools B1–B9, in the curricula of study programs of pre-service teacher training are summarized in Table 4. The table contains descriptive characteristics of the achieved average score of the recorded assessments of the items B1–B9 overall, for the whole research sample, and for subgroups of the research sample created depending on the segmentation factors COUNTRY, GENDER, and the combination of the COUNTRY × GENDER factors. Other factors, such as the level of study currently attended and the subject (major) for which the teacher trainee is being prepared were not taken into account. The values of the mean, the standard deviation, the standard error of the mean estimate and the 95% confidence interval of the mean of the scale value are given.
4.1. Overall Results of the Needs Identification
From the data in Table 4, we can see that the average score of the respondents’ responses without their differentiation according to the stated segmentation factors ranges from 4.15 to 5.44, with 6 being the maximum value of the scale, while it reaches significantly the highest value (B9.1–5.44) in case of the item B9. This value indicates that students are aware of the importance of deepening their ability to effectively use the widest possible range of tools of the Microsoft Word text editor in the context of structuring and formatting their own documents with regard to the intentions of their future teaching profession. Analogous positive results are also shown by the results of the responses recorded for the items B1, B6, B7 and B8 (the average score of the responses at level 5 of the scale, i.e., the mentioned learning topics “needed to incorporate” (should be incorporated) in the curricula of teaching study programs). Overall, it can be stated that the average score of the recorded responses did not fall below the value 4 of the scale for any item (i.e., the evaluations of all of the items were at least at the level “probably needed to incorporate” into the curricula of the study programs).
At the same time, the lowest value of the standard deviation (0.96) was recorded for item B9.1, which means that it had the lowest variability in the respondents’ responses (5.32 to 5.55). As evidenced by the values of the standard deviations (given in Table 4), the respondents more or less agreed in their assessments of the particular given items, and no significant differences in their evaluations were recorded for any item. The highest diversity of responses was recorded for item B4.1 (standard deviation value 1.21). At the same time, however, the lowest value of the average score (4.15) was recorded for this item. This situation means that the students have the most significantly different opinions on the need to include the issue of the use of digital testing and voting systems (ActivExpression2, SMART Response 2, QRF700/900, TurningPoint) in their pre-service training, and in in the overall assessment they consider them (among the evaluated items B1–B9) to be the least necessary to include into their professional training.
The results recorded for item B4 can be related to the lack of awareness of the respondents of the possibilities of using voting and testing systems, and especially to the lack of awareness of the respondents of the possibilities of the effective use of these digital systems in teaching practice, e.g., the use of ExpressPoll, Ready Questions, and Questions for Self-paced Processing in the form of interactive educational activities and knowledge-based games supporting activation and keeping pupils’ attention during the lesson, based on the pupils’ natural characteristics, such as competitiveness and playfulness. These are modern didactic tools designed to verify and evaluate students’ knowledge during lessons, which can be used to ask questions of different types to all of the students in the class at once. On the one hand, the students can immediately evaluate their success, or the correctness of their activity, as they have a summary of their results (correct and incorrect answers) immediately available. On the other hand, the teacher obtains immediate information about the students’ knowledge and can discuss with the students the results of the voting before announcing the correct answers.
A higher value of the variability indicator (standard deviation value 1.17) was also recorded in the respondents’ responses to item B6. Based on the interval estimate of the average, the average values of the score of the responses in the scale range from 4.44 to 4.72, which—within the used scale—means assessments from recommendations to incorporate the given topic into the curricula of the teacher training program to the definite requirement to incorporate the topic.
The results of the statistical analysis of the assessment of the significance of the inclusion of the teaching topics B1–B9 into the pre-service teacher trainees study programs from the point of view of teacher trainees for the whole research sample, presented in Table 4, are graphically visualized in Figure 1, which shows dot and interval estimations of the means of the particular items.
The question is whether the differences between the respondents’ responses registered for the individual items (B1.1–B1.9) are random or whether they are statistically significant. We looked for an answer to this question through analysis of variance for repeated measures. The testing of the dependence of the respondents’ responses on their segmentation factors (COUNTRY, GENDER, and their combination COUNTRY × GENDER) was performed subsequently. Primarily, at the beginning of our survey, we wanted to obtain a global answer to the question of how teacher trainees assess the need to incorporate the digital tools B1–B9 into the teaching study programs (global, i.e., without distinguishing the responses of individual groups of the respondents created depending on their segmentation factors).
Based on the results of the descriptive statistics, we formulated a null hypothesis:
There is no statistically significant difference in the assessments B1.1 to B9.1 of the items B1 to B9.
We subsequently tested the Hypothesis 1 at the 5% level of significance (α = 0.05). The testing was performed by analysis of variance for repeated measures.
We used Mauchley’s sphericity test to test the equality of the variance and covariance in the covariance matrix (Table 5).
In the case of the testing of variables B1.1 to B9.1, the test is statistically significant (p < 0.001), which means that the assumption of the equality of the variances is rejected.
Unless the condition of sphericity of the covariance matrix is met, the magnitude of the type I error increases. Therefore, in such cases, the degrees of freedom for the F-test used are adjusted by corrections, thus achieving the declared level of significance. Greenhouse–Geisser and Huynh–Feldt correction for repeated measures of variance analysis were used for the testing due to the violation of the assumption of validity of the analysis of variance (Table 6).
The results of the testing of the respondents’ responses B1.1 to B9.1 to the items B1 to B9 based on the Greenhouse–Geisser and Huynh–Feldt correction (lower bound) for repeated measures of analysis of variance (Table 6) confirmed statistically significant differences in the evaluation of the items (p < 0.001). Based on the results of the analysis for repeated measures, we can see that the achieved value of significance is the same in all three cases, which means that we can reject the above-mentioned null Hypothesis 1 with 99.9% confidence.
As part of the testing, we also applied non-parametric alternatives for repeated measurements of analysis of variance, namely the Friedman test and the Kendall coefficient of agreement. The results of the Friedman test (ANOVA Chi-sqr. (n = 280, df = 8) = 492.6239; p = 0.00000) and the achieved value of the Kendall coefficient of agreement (Coeff. of Concordance = 0.21992) confirmed the results of the Greenhouse–Geisser and Huynh–Feldt corrections (p < 0.001). The results match, so we can consider them robust.
After rejecting the null Hypothesis 1, we found out whether there are statistically significant differences in the evaluation of the particular items and, if so, between which items and which groups of items. The identification of homogeneous groups was achieved by multiple comparisons of individual pairs. The results of the comparisons are shown in Table 7. Within the identified six homogeneous groups, the teacher trainees of the entire research sample (without their differentiation according to the segmentation factors) responded to the individual items almost equally. The statistical significance of the differences in the respondents’ responses to the items within the individual homogeneous groups was not confirmed.
The results of the tests (Table 7) focused on revealing the correlation between the items B1.1 to B9.1 are graphically presented in Figure 1, in which the interval estimate of the average of the scale value for some pairs of variables does not overlap.
4.2. Analysis of the Dependence of the Needs Identification on Respondents’ Segmentation Factors
Next, we dealt with the question of what the divergence of the average values of the score of the responses B1.1 to B9.1 of the respondents depending on the segmentation factors is, i.e., depending on the COUNTRY factor (factor A2), GENDER (factor A1), and a combination of the COUNTRY and GENDER factors (A2 × A1).
The dependence of the respondents’ responses on the COUNTRY, GENDER, and the combination of the COUNTRY and GENDER factors was tested for all items B1.1–B9.1. This means that we have verified the validity of the following three basic null hypotheses, each of which represents nine partial null hypotheses (formulated separately for the particular items B1–B9):
The respondents’ responses B1.1 to B9.1 to the items B1 to B9 do not depend on the COUNTRY factor.
The respondents’ responses B1.1 to B9.1 to the items B1 to B9 do not depend on the GENDER factor.
The respondents’ responses B1.1 to B9.1 to the items B1 to B9 do not depend on the combination of the COUNTRY and GENDER factors.
The dependence or independence on the particular factors stated in the hypotheses 2–4 was tested by means of parametric tests.
Based on the results of the analysis for the repeated measurements summarized in Table 8, we can see that depending on the COUNTRY factor, the value of p is < 0.05, which means that the responses B1.1–B9.1 to the items B1–B9 depend on the respondent’s country of study. Based on this, we reject the null Hypothesis 2 and state that the country in which the respondents are studying has an impact on the respondents’ recorded assessments B1.1 to B9.1.
Adjusted tests of the significance of the differences between the average scores of the responses B1.1 to B9.1 of the respondents to the items B1 to B9 depending on the GENDER factor did not confirm the significance of the tested differences (Table 8), as the value of the variable p is higher than the selected level of significance (5% = 0.05) within Greenhouse–Geisser (p = 0.51) and Huynh–Feldt corrections (p = 0.51).
A similar situation is also the case in the evaluation of the given items depending on the combination of the COUNTRY and GENDER factors. In terms of the combination of these two factors, the B1.1–B9.1 assessments of men from Slovakia and women from Slovakia, as well as men from the Czech Republic and women from the Czech Republic, did not differ significantly. The results of the analysis of variance for repeated measures (Table 8) confirmed that there were no statistically significant differences in the responses to the individual items between the four groups of the respondents (p < 0.05 was not reached). This means that the difference between each pair of the recorded means occurs only as a result of random selection.
The results of the statistical processing of the respondents’ responses B1.1 to B9.1 depending on the segmentation factor COUNTRY are graphically interpreted in Figure 2. The graph shows dot and interval estimates of the average of the recorded assessments of the items B1–B9 separately for a group of Slovak respondents, and separately for a group of Czech respondents. The results of the testing of the differences in the respondents’ responses to the individual items based on the Greenhouse–Geisser and Huynh–Feldt corrections (Lower Bound) for repeated measures of analysis of variance confirmed the statistical significance (p < 0.05) of this factor (Table 8). This fact is visualized on the graph (Figure 2) by the response curves of the respondents in the individual groups, which intersect in some places.
The achieved average score of the respondents’ responses to items B1.1 to B9.1 indicates that, in relation to the formation of their professional didactic-technological competencies, teacher trainees are aware of the importance and usefulness of incorporating the given learning topics into the curricula of their study programs. However, the graphical interpretation of the results of the processing of the responses of the observed groups of the respondents indicates a tendency to feel a higher need to incorporate the given learning topics into the curricula of the study programs of Slovak students than students from the Czech Republic.
4.3. The Issue of the Time Allocation for Subjects Related to Didactic-Technological Training
The results of the determination of the optimal time allocation (number of lessons) of the subject within which the particular topics B1–B9 should be taught are presented in the contingency table of frequencies, Table 9. These are overall results, absolute and relative frequencies of the responses of all of the respondents without their differentiation into groups depending on the segmentation factors. The respondents commented on the issue of the extent of teaching working with the relevant software application by choosing one of the four offered alternative responses (for more details, see the Methodology of the Research sub-chapter) separately for each of the software applications B1–B9.
From Table 9, we can see that the respondents most often assigned the alternative response a to all of the assessed items B1–B9, according to which the issue of working with the given particular application should be taught together with other topics as part of a one-semester subject. In the case of Microsoft Word, Microsoft PowerPoint and Microsoft Excel, however, the number of occurrences of the response b (within a separate one-semester subject focused only on this issue) was also significantly high. From the global point of view of the evaluation, it can be stated that the ratio of the frequency of occurrence of the responses a and b was 2:1. In percentage terms, about a quarter of the respondents declared a requirement for these software applications to include working with them separately, i.e., to teach working with each of these applications within a separate two-semester subject focused only on this issue (choice c).
Regarding the teaching of working with other digital systems and software applications, as shown by the presented results (Table 9), in the case of the digital voting systems ActivExpression2, SMART Response 2, QRF700/900, TurningPoint and the internet application Socrative 2.0, we recorded the lowest level of the requirement for the need to teach them within the study program.
4.4. Analysis of the Dependence of the Time Allocation Requirements on Respondents’ Segmentation Factors
The responses recorded for all items B1.2–B9.2 were tested in terms of their possible dependence on the segmentation factors COUNTRY and GENDER of the respondents.
A null hypothesis was formulated based on the COUNTRY and GENDER factors when testing the dependence of the responses B1.2 to B9.2 of the respondents:
The responses of the respondents B1.2 to B9.2 do not depend on the COUNTRY factor.
The responses of the respondents B1.2 to B9.2 do not depend on the GENDER factor.
As part of the statistical analysis, we verified the validity of the two null hypotheses 5 and 6, each representing nine partial null statistical hypotheses.
In analysing the results recorded for the above-stated nominal items of the questionnaire, a contingency analysis was used to determine the relationships between the two nominal variables. The chi-square test of independence was used within the non-parametric tests based on the contingency table. The only assumption for the validity of the chi-square test is that the expected frequencies are greater than or at most equal to 5. This condition was violated in some cases. For this reason, we did not rely only on the results of the chi-square test but also calculated the contingency coefficients and visualized the dependences between the examined variables. If the polynomials are copied there is no dependence; if they have a different course there is a certain degree of dependence. The degree of statistical dependence between the qualitative features was assessed on the basis of the contingency coefficient C. In accordance with Cohen [38], we used a scale to interpret the correlation coefficients, where values close to 0 indicate a weak dependence and, conversely, values close to 1 indicate a strong dependence.
The tabulation of the contingency coefficients (Table 10) shows that the differences between the responses B1.2–B9.2 of Slovak and Czech students to the question of the optimal time allocation for the teaching of topics relevant to the given software applications and systems B1–B9 were statistically significant only in some cases, specifically in the evaluation of the learning topics B5 (teaching issues focused on the online diagnosis, testing and assessment of pupils and students’ knowledge during or out of teaching through the web application Socrative 2.0), B6 (teaching issues focused on tools for the collaborative creation and management of electronic online documents in the Google Docs environment), B7 (teaching issues focused on the creation of didactic presentations of educational content with the application of feedback and multimedia elements supporting the teacher’s explanation of the subject matter and the systematization of pupils/students’ knowledge through Microsoft PowerPoint) and learning topics B8 (teaching issues focused on the processing of tabulated data usable in the field of work of a teacher through Microsoft Excel tools).
Based on the achieved p-values (Table 10) for items B5.2 (p = 0.013), B6.2 (p = 0.000), B7.2 (p = 0.013) and B8.2 (p = 0.012), we can state that the differences between the individual responses depending on the country of study of the respondents are statistically significant in relation to the value p < 0.05, although the degree of dependence is small in all four cases in relation to the value of the contingency coefficient C (C = 0.19; C = 0.24; C = 0.19; C = 0.19; C ∈ (0.1–0.29)). Based on this result, we reject the null statistical Hypothesis 5, which means that students’ responses to the items B5.2, B6.2, B7.2 and B8.2 (under the given conditions) depend on the COUNTRY factor.
Despite the achieved small degree of dependence of the individual responses on the respondent’s affiliation to one of the groups according to the country of study, the highest value of the contingency coefficient (C = 0.24; p < 0.001) was recorded in the item B6.2 compared to the remaining contingency coefficients (B1.2 to B9.2 × COUNTRY). Therefore, we can declare that the responses to item B6.2 show the greatest statistical dependence on the COUNTRY factor.
The results of the chi-square test of items B5.2, B6.2, B7.2 and B8.2, depending on the segmentation factor COUNTRY shown in Table 10, are visualized in the graphs in Figure 3, Figure 4, Figure 5 and Figure 6. From the individual graphs of the interaction frequencies, we can see that the curves of the results of the responses recorded for the items B5.2, B6.2, B7.2 and B8.2 in the groups of Slovak and Czech students are not copied, which confirms the results of the chi-square test, and that the responses of the respondents depend on their classification into the appropriate group according to nationality (i.e., they confirm their dependence on the COUNTRY factor).
As the results of the testing of the differences in the responses to the question of the optimal time allocation of the subject within which the relevant learning topic would be taught show (Table 10), between the group of Slovak and the group of Czech respondents, these differences proved to be statistically significant in assessing four thematic areas, specifically the areas B5, B6, B7 and B8.
The contingency table of observed frequencies (Table 11) shows the interaction frequencies of the students’ responses separately for the item B5.2 (the assessment of teaching issues focused on the online diagnosis, testing and assessment of pupils and students’ knowledge during or out of teaching through the web application Socrative 2.0), B6.2 (the assessment of the teaching issues of tools for the collaborative creation and management of electronic online documents in the Google Docs environment), B7.2 (teaching issues focused on the creation of didactic presentations of educational content with the application of feedback and multimedia elements supporting the teacher’s explanation of the subject matter and the systematization of pupils/students’ knowledge through Microsoft PowerPoint) and B8.2 (the assessment of teaching issues focused on the processing of tabulated data usable in the field of work of a teacher through Microsoft Excel tools) depending on the COUNTRY factor (B5.2 x COUNTRY; B6.2 x COUNTRY; B7.2 x COUNTRY; B8.2 x COUNTRY).
It can be seen in Table 12 that the responses of male and female respondents recorded for items B1.2 to B9.2 do not differ statistically significantly in relation to the achieved values of p > 0.05. The values of the contingency coefficient are statistically insignificant based on the results of the chi-square test. Based on this result, we do not reject the null Hypothesis 6, which means that students’ responses to the monitored items B1.2–B9.2 do not depend on the GENDER factor.
To illustrate, we also present a graphical visualization of the results of the processing of the results of the respondents’ answers recorded under items B1.2 and B4.2, depending on the GENDER factor. The chi-square test results of questionnaire items B1.2 and B4.2 depending on the GENDER factor, as shown in Table 12, are visualized using graphs of interaction frequencies (Figure 7 and Figure 8). The response curves of male and female respondents are more or less copied at each stage, so the graphs in Figure 7 and Figure 8 confirm the results of the chi-square test.
5. Discussion
The presented results show that, with regard to their future professional activities, teacher trainees consider it most important to incorporate the issues of the effective processing and formatting of their own text documents usable in the work of teaching staff (Microsoft Word), the creation of didactic presentations of educational content supporting teacher interpretation (Microsoft PowerPoint), and the processing of tabulated data usable in the field of work of a pedagogical employee (Microsoft Excel) into pre-service teacher training. At this point, it should be mentioned that students are usually familiar with the basics of working with relevant software applications at lower levels of education, as other research results confirm. As examples of such research studies, [39,40] focused on the content analysis of upper secondary school curricula of informatics. Based on the results of these analyses, it was expected that the respondents in their opinions would not state the need to incorporate Microsoft Office tools into their study programs. From the point of view of the education process innovation, our findings differ from those stated by Wash [41] and Lengyelfalusy [42]. According to these authors, the way of teaching when a teacher’s explanation of the subject matter is accompanied only by a “traditional” presentation of the given topic carried out in the environment of Microsoft PowerPoint application was already “a closed chapter”.
However, as the results of the screening show, the scope and level of ability to work with the relevant software applications that students acquire at the secondary level of education do not provide them with a sense of comfort in their application in their (future) profession. At this point, it may be argued that this result may be a deliberate effort or a subconscious tendency of the respondents to “deliberately manipulate” the results of screening so that the curricula created based on the results would allow students to study “as easily as possible”. However, the validity of this objection is refuted by the analyses of students’ opinions on the extent to which the teaching of this issue should be incorporated in teaching study programs and the character (category) of the subject within which the relevant issues should be taught. The results of the screening of the respondents’ opinions on items B1–B9 from the aspect of the character of subjects (compulsory, compulsory optional, optional subject) within which the given issues would be taught are not presented in this article due to limited space (for more detail, see [43]).
The respondents most often assigned to basically all of the assessed software applications (Table 1) the alternative response according to which the issue of working with the application should be taught together with other topics as part of a one-semester course (alternative a). In the case of Microsoft Word, Microsoft PowerPoint and Microsoft Excel, however, the occurrence of response b (within a separate one-semester subject focused only on this issue) was also significantly high. From the global point of view of the evaluation, it can be stated that the ratio of the frequency of occurrence of the responses a and b was 2:1. In percentage terms, about a quarter of the respondents declared a requirement for these software applications to include working with them separately, i.e., working with each of these applications within a separate two-semester subject focused only on this issue (alternative answer c).
From the respondents´ answers, it is clear that they are consistent with the long-term constant trend that the user applications of the Microsoft Office software package still almost monopolise implementation in the field of education at every level. Concepts focused on development of informatization and digitalization in the field of education in the period up to 2020 prepared by the Ministry of Education, Science, Research and Sports of the Slovak Republic were based on previous experience just with the integration of these tools [44]. In the context of the this concept, there is an obvious commitment to ensure the professional development of primary and secondary school teachers, as well as further education of university teachers, and to support the implementation of this education. Educational institutions are due to (more or less) standard uniform software support provided by the state, which are compatible with each other. One can state that this concept is an aspect seriously reflected in the teacher trainees’ awareness.
Despite the fact that the requirement or requirements to incorporate work with each of the given digital applications as a one or two-semester subjects focused exclusively on working with these software products can be assessed as relatively significant, we are aware that it should be assessed with considerable caution. The projection of this requirement into the study program would lead to a disproportionate increase in the subjects included in the study program (focused exclusively on the formation of didactic-technological competencies of teacher trainees) and at the same time to a disproportionately exaggerated content of these subjects. Furthermore, we do not consider it appropriate that the pre-service teacher training in the field of developing teacher trainees’ didactic-technological competencies should focus on the basic skills of working with these software tools, as students should acquire the basics of these skills during their secondary school studies. In the case of reporting an insufficient level of these abilities, the elimination of these deficiencies should be a matter of, e.g., the self-education of students, or the completion of additional courses, but it should certainly not be the focus of the subjects of the relevant study programs core (as follows from the currently identified requirements of students). The content focus of the subjects of the relevant study programs core must focus on the didactic aspects of the use of the software applications in the teaching of the particular subjects.
Regarding the incorporation of the teaching of the use of the digital voting systems ActivExpression2, QRF700/900, SMART Response 2 and the Internet application Socrative 2.0 into the study programs of teachers, the lowest level of demand for their teaching was recorded in these two cases. Nevertheless, these systems belong to the most up-to-date third-generation voting tools to check and test pupils’ and students’ knowledge in an on-line form, either within the face-to face or distance teaching process. We consider the recorded result to be a consequence of the fact that students (teacher trainees) are not sufficiently acquainted with the possibilities of their use, or with the possibilities of using electronic voting systems/devices in school practice in general. Therefore, we consider it to be important that in the curricula of subjects forming didactic-technological competencies of future teachers, in addition to training in voting systems, the issue of the ways of their use is emphasized with regard to increasing the effectiveness of teaching the subject (the use of voting systems in teaching as a means of increasing pupils/students’ activity, the interest and attractiveness of the studied curriculum, or teaching situations, etc.). In this point, we are consistent with Aljaloud, Gromik, Billingsley and Kwan [45]. According their research results, the efficiency of teaching does not depend only on the use of the voting systems themselves, but more significantly it depends on the ways of their use, i.e., in order to increase teaching efficiency through the use of voting systems, it is necessary to choose the appropriate methods of their application within the teaching process (see also [46]). To apply voting systems in education is not the goal of teaching. It is a means to make teaching more attractive for pupils and students (either while teaching at schools or within home schooling) and to ensure that pupils and students acquire new knowledge in a more active way [47,48]. Böhm and Jermář [49], Sung, Chang and Liu [50], Draper and Brown [51] and many others state that the use of voting systems in teaching enables (or sometimes even imposes) the active involvement of all of the students in the activities undertaken during the lesson.
Using digital voting systems within teaching at schools, as well as using them within home preparation for the next lessons, has already become a common practice in most European developed countries (see e.g., [52]). However, these systems are only gradually entering into the environment of Slovak and Czech primary and secondary schools. This is mainly due to voting system prices, which are not low. From this reason, schools in Slovakia and the Czech Republic usually gain these systems in frame of various European projects [44].
A more available, more reasonably priced alternative to expensive clicker voting systems looks to be the use of students’ smart phones and tablets. The advantages of this alternative to the clicker systems are proved in the case study JIM of the authors Feierabend, Plankenhorn and Rathgeb [53]. According to these authors, smart phones and tablets are an integral part of youths’ lives, and so teachers should give a chance to this method to check and test student knowledge. Moreover, the use of smart phones and tablets is not limited to school premises (classrooms). In this way, students can be liberated from the rigid teaching bound to school rooms, and are allowed to move to mobile rooms and contexts [54].
The platforms Socrative 2.0 and SMART Response 2 belong to the most well known interactive cloud applications. They offer many intuitive tools equally both for teachers and for students. Thanks to them, it is possible to achieve the stated intentions and to increase the online interactivity of education. This was also proved in the frame of the research performed by Lim [55] and Wasch [41]. The importance of the use of these digital didactic means was multiplied under the influence of the current world-wide coronavirus pandemic, under which all education institutions were forced to transit to a distanced (on-line) form of education [56].
Nowadays, most teacher training study programs emphasize working with Microsoft Word, Excel, or PowerPoint. The teaching of the relevant subject or subjects focuses on the development of general user competencies (basic skills related to the possibilities that these products provide), while within the curricula of these subjects there is no focus on the specific possibilities of the application of these computer products in the teaching of the particular subjects. To a large extent, this fact is a consequence of the heterogeneity of the students in individual groups (i.e., the heterogeneity of the specializations of their study of different subjects, i.e., the teacher trainees’ majors). As the presented research results indicate, it would be appropriate to adjust the offer of subjects in the focus of their content within the possibilities so that it also reflects the subject specialization of the majoring of individual groups of students [57].
In order to meet the presented expectations of students, it will be necessary to change not only the content of the subjects developing the didactic-technological competencies of teacher trainees but also the inclusion of the relevant subjects in their study plans. This is mainly related to the interdisciplinary connection of these subjects with branch didactics. The subjects of branch didactics are incorporated into teaching study programs in higher years (usually only in the master’s degree), while subjects focused on the development of the didactic-technological competencies of teacher trainees (with regard to emphasizing the development of the general user digital literacy of students) are included in lower years (usually in bachelor’s degrees). In the current classification of these subjects (essentially in reverse relation to each other), quite naturally, within the didactic-technological training of teacher trainees, the “student” rather than “teacher” view of the possibilities of using these means prevails (i.e., the assessment of working with these technologies from the student’s point of view rather than from the teacher’s point of view). The elimination of this problem could be achieved by moving the subjects of didactic-technological training to higher years of study.
6. Conclusions and Recommendations
As the results of different researchers show [58,59,60,61], the success of the use of technology in teaching depends mostly on the teacher’s personality and his/her skills. This is why it is very important to retain the sustainably quality of teachers’ professional didactic-technological competences. This task includes the need to innovate continuously the pre-service training of teacher trainees. The results of the carried-out research indicate some requirements which should be currently reflected in the innovation of the curricula of this part of the training.
The main aim of the project within which the presented research was carried out was to create a platform for the design of an optimal model of pre-service training on shaping the students’ professional digital literacy. This platform was created as a proposal of the following recommendations:
The recommendations concerning the content (curricula) of the relevant subjects are:
The curricula of the relevant subjects need to incorporate the issue of the effective processing and formatting of teachers’ own text documents and tabulated data usable in the field of work of pedagogical staff, as well as the creation of didactic presentations of educational content supporting the teacher’s interpretation in teaching.
In connection with the training in working with digital voting systems, it is necessary to emphasize various possibilities of their use with regard to increasing the effectiveness of teaching the particular subjects.
The recommendations concerning the scope of teaching the relevant issues are:
Critically consider the inclusion of teaching the use of Microsoft Word, Microsoft PowerPoint and Microsoft Excel as separate one-semester subjects focused only on this issue (student requirement), or as additional courses or optional subjects (with regard to the declared profiles of high school graduates according to which students, i.e., secondary school graduates, should have acquired the basic skills of working with these applications already during their secondary school studies).
Consider the possibility of teaching the use of all of the assessed software applications, as the optimal form of teaching was the most frequently chosen alternative alternative, i.e., a–together with other topics as part of a one-semester subject.
The findings point out the necessity to adjust the offer of subjects so that it would reflect, to a larger degree, the subject specialization of teacher trainees (their majors), and so that it can be provided to specific teacher training in the field of didactic-technological competencies separately for each relevant group of teacher trainees (i.e., differentiated for teacher trainees of social science subjects, science subjects, foreign languages, art and educational subjects, and professional and vocational subjects). At present, within the curricula of these subjects, there is no focus on the specific possibilities of the application of relevant software products to the teaching of the particular subjects.
The recommendations concerning the incorporation of the relevant subjects into the teacher training study programs are:
In order to meet the expectations of students regarding the completion of the relevant subjects, it is necessary not only to innovate the content of these subjects but also to change their incorporation into the study plans. Specifically, it is recommended to move the subjects of didactic-technological preparation to higher years of study (to the master’s degree).
The recommendations concerning the incorporation of new subjects into the teacher training study programs are:
In addition to the above-mentioned recommendations, we also suggest incorporating new subjects, namely compulsory or compulsory–optional subjects—the creation of interactive forms of teaching materials, interactive technologies of voting and evaluation in teaching, and applications of pedagogical software in teaching—into the general basis of the teacher trainees’ study programs.
7. Remark at the Conclusion—Limitations of the Research Survey
The respondents of the presented research survey were teacher trainees, most of whom had not passed their pre-service teaching practice. This fact could be perceived as a limitation to the generalization of the research results. Teaching practice gives the teacher trainees the opportunity to analyse a teacher’s professional performance not only from a student’s point of view but also from a future teacher’s point of view (there are different points of view on the use of didactic technological means in teaching: that of the student as the percipient and that of the future teacher as the education professional). However, despite this fact, the obtained findings can serve as a platform to innovate and set the goals and content of the subjects focused on the development of teacher trainees’ didactic technological competence and professional digital skills.
The internal criteria set by the particular higher education institutions (offering teaching study programs) on the pre-gradual teacher training should also reflect national as well as international strategies regarding school policy. From the proclamations of these strategies, a tendency to incorporate the development of didactic-technological competences with focus on digital competence can be derived.
The most significant limitation, which has to be mentioned, is connected with the time period when the research was carried out. The presented survey was carried out in the academic year 2018/2019, before the coronavirus pandemic occurred. It was carried out at a time when we had no experience of teaching in coronavirus pandemic conditions. In that time, the core of the teacher training related to the use of digital technologies was the appropriate implementation of different software applications into teaching. Nowadays, in the conditions of the pandemic, we can see that what may be even more important is to train teachers to work with different online systems. The pandemic has caused education to move into virtual reality. For pupils, students and their teachers too, a new situation has arisen. We are all online. In order to support the distant form of education at all of its levels, digital multiplatform tools are used. For many teachers, this form of education is no novelty. This form has already been used in specific situations (for example, in the case of pupils/students’ illness) for a long time. On the other hand, the majority of teachers have not come into contact with this form of education in their teaching practice yet. From the position of the teacher, they are not familiar with the methodology of online teaching. As such, beside the above-mentioned research results, the new (corona) conditions should be taken into consideration with respect to upgrading the teacher training in the area of their didactic-technological competencies. Taking into consideration the situation under the corona pandemic means that the issue of teaching through online systems (such as e.g., Microsoft Teams, Zoom, Cisco Webex, GoToMeeting, BlueJeans) should be included in the curricula of teaching study programs.
Author Contributions
Conceptualization, J.Z., A.H., A.P. and M.M.; methodology, J.Z., A.H. and M.M.; software, M.M.; validation, M.M.; formal analysis, J.Z., A.H. and A.P.; investigation, J.Z., A.H., and A.P.; resources, J.Z., A.H. and A.P.; data curation, J.Z. and A.H.; writing—original draft preparation, J.Z., A.H. and A.P.; writing—review and editing, J.Z. and A.H.; visualization, J.Z. and M.M.; supervision, A.H. and M.M.; project administration, J.Z. and A.P.; funding acquisition, J.Z., A.H., A.P. and M.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Acknowledgments
This work has been supported by the Cultural and Educational Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic under the project No. KEGA 066UK-4/2021—‘Electronic support for undergraduate training of teacher students in the field of classroom management—creation of a web portal and university textbook‘. The authors of the paper wish to acknowledge the valuable contributions and support of the partners of the Faculty of Education, Comenius University in Bratislava, the Faculty of Education and the Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Charles University in Prague and the University of Hradec Králové.
Conflicts of Interest
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Figures and Tables
Enlarge this image.Figure 1. Visualization of the differences between the average score values of the items B1.1 to B9.1 presented by means of dot and interval estimations of the average.
Enlarge this image.Figure 2. Average dot and interval score of the items B1.1 to B9.1 depending on the COUNTRY factor.
Enlarge this image.Figure 3. Interaction graph of the frequencies of the responses a–d for the item B5.2 according to the COUNTRY factor.
Enlarge this image.Figure 4. Interaction graph of the frequencies of the responses a–d for the item B6.2 according to the COUNTRY factor.
Enlarge this image.Figure 5. Interaction graph of the frequencies of the responses a–d for the item B7.2 according to the COUNTRY factor.
Enlarge this image.Figure 6. Interaction graph of the frequencies of the responses a–d for the item B8.2 according to the COUNTRY factor.
Enlarge this image.Figure 7. Interaction graph of the frequencies of the responses a–d for the item B1.2 according to the GENDER factor.
Enlarge this image.Figure 8. Interaction graph of the frequencies of the responses a–d for the item B4.2 according to the GENDER factor.
Overview of the selected thematic areas (learning topics) assessed by the teacher trainees.
| Designation of the Topic | Kind of Software Product/Thematic Area (Learning Topic) |
|---|---|
| B1 | ActivInspire, SMART Notebook or Flow!Works |
| B2 | Prezi |
| B3 | FreeMind, Mindomo, XMind |
| B4 | ActivExpression2, SMART Response 2, TurningPoint |
| B5 | Socrative 2.0 |
| B6 | Google Docs |
| B7 | Microsoft PowerPoint |
| B8 | Microsoft Excel |
| B9 | Microsoft Word |
Composition of the research sample according the respondents’ characteristics.
| Factor (Item) | Group | Frequency |
% |
|---|---|---|---|
| GENDER (A1) | Male | 49 | 17.5 |
| Female | 231 | 82.8 | |
| UNIVERSITY (A2) | Comenius University in Bratislava | 205 | 73.2 |
| Charles University in Prague | 48 | 17.2 | |
| University of Hradec Králové | 27 | 9.6 | |
| COUNTRY (A2) | Slovakia (SR) | 205 | 73.2 |
| Czech Republic (CR) | 75 | 26.8 | |
| LEVEL |
Bachelor’s degree | 254 | 90.7 |
| Master’s degree | 26 | 9.3 |
Item analysis.
| Mean If Deleted | StDv. If Deleted | Itm-Totl Correl. | Squared Multp. R | Alpha If Deleted | |
|---|---|---|---|---|---|
| B1.1 | 64.843 | 9.115 | 0.508 | 0.484 | 0.863 |
| B1.4 | 66.514 | 9.278 | 0.514 | 0.499 | 0.864 |
| B2.1 | 65.139 | 9.103 | 0.460 | 0.545 | 0.865 |
| B2.4 | 66.693 | 9.254 | 0.493 | 0.559 | 0.864 |
| B3.1 | 65.100 | 9.043 | 0.505 | 0.598 | 0.863 |
| B3.4 | 66.639 | 9.246 | 0.454 | 0.591 | 0.865 |
| B4.1 | 65.375 | 8.933 | 0.550 | 0.582 | 0.861 |
| B4.4 | 66.929 | 9.103 | 0.544 | 0.616 | 0.861 |
| B5.1 | 65.232 | 8.959 | 0.551 | 0.577 | 0.861 |
| B5.4 | 66.732 | 9.159 | 0.536 | 0.577 | 0.862 |
| B6.1 | 64.939 | 9.023 | 0.493 | 0.549 | 0.864 |
| B6.4 | 66.596 | 9.216 | 0.461 | 0.520 | 0.865 |
| B7.1 | 64.271 | 9.067 | 0.499 | 0.673 | 0.863 |
| B7.4 | 65.996 | 9.312 | 0.480 | 0.447 | 0.865 |
| B8.1 | 64.464 | 9.062 | 0.500 | 0.531 | 0.863 |
| B8.4 | 66.361 | 9.275 | 0.436 | 0.401 | 0.866 |
| B9.1 | 64.086 | 9.187 | 0.443 | 0.652 | 0.865 |
| B9.4 | 65.954 | 9.355 | 0.405 | 0.452 | 0.867 |
Results of the descriptive statistics of the respondents’ answers B1.1–B9.1 depending on the factors COUNTRY, GENDER, and their combination, COUNTRY × GENDER.
| Level of Factor | B1.1 | Confidence Interval for the Mean | B2.1 | Confidence Interval for the Mean | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Mean | Std.Dev. | Std.Err. | –95% | +95% | Mean | Std.Dev. | Std.Err. | –95% | +95% | |
| Total | 4.68 | 0.99 | 0.06 | 4.56 | 4.79 | 4.38 | 1.09 | 0.07 | 4.25 | 4.51 |
| SR | 4.74 | 0.93 | 0.07 | 4.61 | 4.87 | 4.35 | 1.04 | 0.07 | 4.20 | 4.49 |
| CR | 4.52 | 1.11 | 0.13 | 4.27 | 4.77 | 4.48 | 1.21 | 0.14 | 4.20 | 4.76 |
| F | 4.66 | 0.96 | 0.06 | 4.54 | 4.79 | 4.40 | 1.06 | 0.07 | 4.26 | 4.54 |
| M | 4.76 | 1.09 | 0.16 | 4.44 | 5.07 | 4.29 | 1.22 | 0.17 | 3.93 | 4.64 |
| SR × F | 4.76 | 0.91 | 0.07 | 4.62 | 4.89 | 4.40 | 1.01 | 0.08 | 4.25 | 4.55 |
| SR × M | 4.62 | 1.08 | 0.20 | 4.21 | 5.03 | 4.03 | 1.18 | 0.22 | 3.59 | 4.48 |
| CR × F | 4.36 | 1.08 | 0.15 | 4.07 | 4.66 | 4.42 | 1.21 | 0.16 | 4.09 | 4.75 |
| CR × M | 4.95 | 1.10 | 0.25 | 4.44 | 5.46 | 4.65 | 1.23 | 0.27 | 4.08 | 5.22 |
| Level of Factor | B3.1 | Confidence Interval for the Mean | B4.1 | Confidence Interval for the Mean | ||||||
| Mean | Std.Dev. | Std.Err. | –95% | +95% | Mean | Std.Dev. | Std.Err. | –95% | +95% | |
| Total | 4.42 | 1.11 | 0.07 | 4.29 | 4.55 | 4.15 | 1.21 | 0.07 | 4.00 | 4.29 |
| SR | 4.53 | 1.01 | 0.07 | 4.39 | 4.67 | 4.16 | 1.22 | 0.08 | 3.99 | 4.33 |
| CR | 4.12 | 1.31 | 0.15 | 3.82 | 4.42 | 4.11 | 1.21 | 0.14 | 3.83 | 4.39 |
| F | 4.40 | 1.11 | 0.07 | 4.25 | 4.54 | 4.14 | 1.21 | 0.08 | 3.99 | 4.30 |
| M | 4.53 | 1.12 | 0.16 | 4.21 | 4.85 | 4.16 | 1.23 | 0.18 | 3.81 | 4.52 |
| SR × F | 4.51 | 1.01 | 0.08 | 4.36 | 4.66 | 4.17 | 1.20 | 0.09 | 3.99 | 4.35 |
| SR × M | 4.66 | 1.01 | 0.19 | 4.27 | 5.04 | 4.10 | 1.35 | 0.25 | 3.59 | 4.62 |
| CR × F | 4.04 | 1.33 | 0.18 | 3.68 | 4.40 | 4.05 | 1.27 | 0.17 | 3.71 | 4.40 |
| CR × M | 4.35 | 1.27 | 0.28 | 3.76 | 4.94 | 4.25 | 1.07 | 0.24 | 3.75 | 4.75 |
| Level of Factor | B5.1 | Confidence Interval for the Mean | B6.1 | Confidence Interval for the Mean | ||||||
| Mean | Std.Dev. | Std.Err. | –95% | +95% | Mean | Std.Dev. | Std.Err. | –95% | +95% | |
| Total | 4.29 | 1.17 | 0.07 | 4.15 | 4.43 | 4.58 | 1.17 | 0.07 | 4.44 | 4.72 |
| SR | 4.33 | 1.14 | 0.08 | 4.17 | 4.49 | 4.65 | 1.12 | 0.08 | 4.49 | 4.80 |
| CR | 4.17 | 1.23 | 0.14 | 3.89 | 4.46 | 4.40 | 1.29 | 0.15 | 4.10 | 4.70 |
| F | 4.32 | 1.17 | 0.08 | 4.16 | 4.47 | 4.54 | 1.18 | 0.08 | 4.39 | 4.69 |
| M | 4.16 | 1.16 | 0.17 | 3.83 | 4.50 | 4.78 | 1.12 | 0.16 | 4.45 | 5.10 |
| SR × F | 4.34 | 1.13 | 0.09 | 4.17 | 4.51 | 4.61 | 1.13 | 0.09 | 4.44 | 4.78 |
| SR × M | 4.28 | 1.25 | 0.23 | 3.80 | 4.75 | 4.90 | 1.01 | 0.19 | 4.51 | 5.28 |
| CR × F | 4.24 | 1.30 | 0.18 | 3.88 | 4.59 | 4.33 | 1.31 | 0.18 | 3.97 | 4.68 |
| CR × M | 4.00 | 1.03 | 0.23 | 3.52 | 4.48 | 4.60 | 1.27 | 0.28 | 4.00 | 5.20 |
| Level of Factor | B7.1 | Confidence Interval for the Mean | B8.1 | Confidence Interval for the Mean | ||||||
| Mean | Std.Dev. | Std.Err. | –95% | +95% | Mean | Std.Dev. | Std.Err. | –95% | +95% | |
| Total | 5.25 | 1.08 | 0.06 | 5.12 | 5.38 | 5.06 | 1.09 | 0.07 | 4.93 | 5.19 |
| SR | 5.40 | 0.91 | 0.06 | 5.28 | 5.53 | 5.14 | 0.97 | 0.07 | 5.01 | 5.28 |
| CR | 4.83 | 1.38 | 0.16 | 4.51 | 5.14 | 4.83 | 1.34 | 0.15 | 4.52 | 5.13 |
| Ž | 5.26 | 1.09 | 0.07 | 5.12 | 5.40 | 5.05 | 1.10 | 0.07 | 4.90 | 5.19 |
| M | 5.20 | 1.04 | 0.15 | 4.91 | 5.50 | 5.10 | 1.05 | 0.15 | 4.80 | 5.40 |
| SR × F | 5.44 | 0.88 | 0.07 | 5.31 | 5.57 | 5.15 | 0.95 | 0.07 | 5.01 | 5.29 |
| SR × M | 5.21 | 1.05 | 0.19 | 4.81 | 5.61 | 5.10 | 1.11 | 0.21 | 4.68 | 5.53 |
| CR × F | 4.69 | 1.46 | 0.20 | 4.30 | 5.09 | 4.73 | 1.45 | 0.19 | 4.34 | 5.12 |
| CR × M | 5.20 | 1.06 | 0.24 | 4.71 | 5.69 | 5.10 | 0.97 | 0.22 | 4.65 | 5.55 |
| Level of Factor | B9.1 | Confidence Interval for the Mean | ||||||||
| Mean | Std.Dev. | Std.Err. | –95% | +95% | ||||||
| Total | 5.44 | 0.96 | 0.06 | 5.32 | 5.55 | |||||
| SR | 5.52 | 0.82 | 0.06 | 5.40 | 5.63 | |||||
| CR | 5.21 | 1.26 | 0.14 | 4.92 | 5.50 | |||||
| F | 5.42 | 1.00 | 0.07 | 5.29 | 5.55 | |||||
| M | 5.49 | 0.77 | 0.11 | 5.27 | 5.71 | |||||
| SR × F | 5.52 | 0.82 | 0.06 | 5.39 | 5.64 | |||||
| SR × M | 5.52 | 0.83 | 0.15 | 5.20 | 5.83 | |||||
| CR × F | 5.13 | 1.40 | 0.19 | 4.75 | 5.51 | |||||
| CR × M | 5.45 | 0.69 | 0.15 | 5.13 | 5.77 | |||||
Note: n = 280; SR–Slovak Republic; CR–Czech Republic; F–female; M–male.
Table 5Mauchly’s sphericity test.
| Area of the Survey/Items | W | Chi-sqr. | df | p |
|---|---|---|---|---|
| B1–B9/(B1.1–B9.1) | 0.3401 | 294.6061 | 35 | 0.00000 *** |
Note: W/Chi-sqr.–test statistics; df–degrees of freedom; p value; *** p < 0.001.
Table 6Greenhouse–Geisser and Huynh–Feldt correction for repeated measures of variance analysis.
| df | F | p | G-G | G-G | G-G | G-G | |
|---|---|---|---|---|---|---|---|
| Epsilon | Adj. df1 | Adj. df2 | Adj. p | ||||
| Item | 8 | 33.0787 | 0.00000 *** | 0.7774 | 6.2192 | 1716.4940 | 0.00000 *** |
| Error | 2208 | ||||||
| H-F | H-F | H-F | H-F | ||||
| Epsilon | Adj. df1 | Adj. df2 | Adj. p | ||||
| 0.8059 | 6.4473 | 1779.4680 | 0.00000 *** | ||||
Note: df–degrees of freedom; F/Epsilon–test statistics; p value; G–G–Greenhouse–Geisser correction; H–F–Huynh–Feldt correction; *** p < 0.001.
Table 7Identification of the homogeneous groups.
| Item | Mean | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|---|
| B4.1 | 4.15 | **** | |||||
| B5.1 | 4.29 | **** | **** | ||||
| B2.1 | 4.38 | **** | **** | **** | |||
| B3.1 | 4.42 | **** | **** | ||||
| B6.1 | 4.58 | **** | **** | ||||
| B1.1 | 4.68 | **** | |||||
| B8.1 | 5.06 | **** | |||||
| B7.1 | 5.25 | **** | **** | ||||
| B9.1 | 5.44 | **** |
Note 1: The statistically significant differences between the averages of the values of the responses of students B1.1 to B9.1 are identified at the significance level 0.05. Note 2: **** homogeneous groups.
Table 8Greenhouse–Geisser and Huynh–Feldt corrections (Lower Bound) for repeated measures of variance analysis.
| Lower Bound |
Lower Bound |
Lower Bound |
Lower Bound |
|
|---|---|---|---|---|
| ITEM | 0.7774 | 6.2192 | 1716.4940 | 0.00000 *** |
| ITEM × COUNTRY | 0.7774 | 6.2192 | 1716.4940 | 0.04157 * |
| ITEM × GENDER | 0.7774 | 6.2192 | 1716.4940 | 0.50782 |
| ITEM × COUNTRY × GENDER | 0.7774 | 6.2192 | 1716.4940 | 0.37174 |
|
Lower Bound
|
Lower Bound
|
Lower Bound
|
Lower Bound
|
|
| ITEM | 0.8059 | 6.4473 | 1779.4680 | 0.00000 *** |
| ITEM × COUNTRY | 0.8059 | 6.4473 | 1779.4680 | 0.03938 * |
| ITEM × GENDER | 0.8059 | 6.4473 | 1779.4680 | 0.51072 |
| ITEM × COUNTRY × GENDER | 0.8059 | 6.4473 | 1779.4680 | 0.37207 |
Note: The lower Bound for Greenhouse–Geisser and Huynh–Feldt corrections; Epsilon–test statistics; df–degrees of freedom; p value; * p < 0.05; *** p < 0.001.
Table 9Absolute and relative frequencies of the responses a–d recorded for the items B1.2–B9.2.
| Item/Answer | a | b | c | d |
|---|---|---|---|---|
| B1.2 | ||||
| ∑ | 122 | 112 | 34 | 12 |
| 43.57% | 40.00% | 12.14% | 4.29% | |
| B2.2 | ||||
| ∑ | 146 | 70 | 21 | 43 |
| 52.14% | 25.00% | 7.50% | 15.36% | |
| B3.2 | ||||
| ∑ | 142 | 66 | 34 | 38 |
| 50.71% | 23.57% | 12.14% | 13.57% | |
| B4.2 | ||||
| ∑ | 122 | 75 | 16 | 67 |
| 43.57% | 26.79% | 5.71% | 23.93% | |
| B5.2 | ||||
| ∑ | 130 | 69 | 26 | 55 |
| 46.43% | 24.64% | 9.29% | 19.64% | |
| B6.2 | ||||
| ∑ | 157 | 63 | 19 | 41 |
| 56.07% | 22.50% | 6.79% | 14.64% | |
| B7.2 | ||||
| ∑ | 146 | 88 | 25 | 21 |
| 52.14% | 31.43% | 8.93% | 7.50% | |
| B8.2 | ||||
| ∑ | 157 | 83 | 23 | 17 |
| 56.07% | 29.64% | 8.21% | 6.07% | |
| B9.2 | ||||
| ∑ | 155 | 89 | 23 | 13 |
| 55.36% | 31.79% | 8.21% | 4.64% |
Note: a–together with other topics as part of a one-semester subject; b–within a separate one-semester subject focused only on this issue; c–within a separate two-semester subject focused only on this issue; d–the teaching of this issue does not need to be included in the study program of teacher trainees.
Table 10Results of the chi-square test of the independence of the items B1.2 to B9.2 from the COUNTRY factor.
| ITEM/FACTOR | Pearson’s Chi-sqr. Test | Contingency Coefficient C | Cramer’s V | ||
|---|---|---|---|---|---|
| Chi-sqr. (χ2) | df | p | |||
| B1.2 (4)/COUNTRY (2) | 2.658154 | 3 | 0.44739 | 0.0969749 | 0.0974341 |
| B2.2 (4)/COUNTRY (2) | 2.288455 | 3 | 0.51474 | 0.0900378 | 0.0904050 |
| B3.2 (4)/COUNTRY (2) | 4.505057 | 3 | 0.21184 | 0.1258361 | 0.1268444 |
| B4.2 (4)/COUNTRY (2) | 4.155158 | 3 | 0.24519 | 0.1209250 | 0.1218189 |
| B5.2 (4)/COUNTRY (2) | 10.69667 | 3 | 0.01348 * | 0.1918246 | 0.1954544 |
| B6.2 (4)/COUNTRY (2) | 16.47730 | 3 | 0.00091 *** | 0.2357476 | 0.2425850 |
| B7.2 (4)/COUNTRY (2) | 10.76917 | 3 | 0.01304 * | 0.1924496 | 0.1961156 |
| B8.2 (4)/COUNTRY (2) | 10.90740 | 3 | 0.01224 * | 0.1936347 | 0.1973702 |
| B9.2 (4)/COUNTRY (2) | 2.976719 | 3 | 0.39523 | 0.1025637 | 0.1031074 |
Note: df–degrees of freedom; p value; * p < 0.05; *** p < 0.001.
Table 11Contingency table of the frequencies of responses a–d for items B5.2, B6.2, B7.2, B8.2 according to the COUNTRY factor.
| COUNTRY | B5.2 a | B5.2 b | B5.2 c | B5.2 d | ∑ |
|---|---|---|---|---|---|
| SR | 103 | 52 | 13 | 37 | 205 |
| Row% | 50.24 | 25.37 | 6.34 | 18.05 | |
| Total% | 36.79 | 18.57 | 4.64 | 13.21 | 73.21 |
| CR | 27 | 17 | 13 | 18 | 75 |
| Row% | 36.00 | 22.67 | 17.33 | 24.00 | |
| Total% | 9.64 | 6.07 | 4.64 | 6.43 | 26.79 |
| COUNTRY | B6.2 a | B6.2 b | B6.2 c | B6.2 d | ∑ |
| SR | 126 | 46 | 8 | 25 | 205 |
| Row% | 61.46 | 22.44 | 3.90 | 12.20 | |
| Total% | 45.00 | 16.43 | 2.86 | 8.93 | 73.21 |
| CR | 31 | 17 | 11 | 16 | 75 |
| Row% | 41.33 | 22.67 | 14.67 | 21.33 | |
| Total% | 11.07 | 6.07 | 3.93 | 5.71 | 26.79 |
| COUNTRY | B7.2 a | B7.2 b | B7.2 c | B7.2 d | ∑ |
| SR | 119 | 57 | 16 | 13 | 205 |
| Row% | 58.05 | 27.80 | 7.80 | 6.34 | |
| Total% | 42.50 | 20.36 | 5.71 | 4.64 | 73.21 |
| CR | 27 | 31 | 9 | 8 | 75 |
| Row% | 36.00 | 41.33 | 12.00 | 10.67 | |
| Total% | 9.64 | 11.07 | 3.21 | 2.86 | 26.79 |
| COUNTRY | B8.2 a | B8.2 b | B8.2 c | B8.2 d | ∑ |
| SR | 122 | 62 | 13 | 8 | 205 |
| Row% | 59.51 | 30.24 | 6.34 | 3.90 | |
| Total% | 43.57 | 22.14 | 4.64 | 2.86 | 73.21 |
| CR | 35 | 21 | 10 | 9 | 75 |
| Row% | 46.67 | 28.00 | 13.33 | 12.00 | |
| Total% | 12.50 | 7.50 | 3.57 | 3.21 | 26.79 |
Note: a–together with other topics as part of a one-semester subject; b–within a separate one-semester subject focused only on this issue; c–within a separate two-semester subject focused only on this issue; d–the teaching of this issue does not need to be included in the study program of teacher trainees.
Table 12Chi-square results of the test of items B1.2 to B9. The independence according to the GENDER factor.
| ITEM/FACTOR | Pearson’s Chi-sqr. Test | Contingency Coefficient C | Cramer’s V | ||
|---|---|---|---|---|---|
| Chi-sqr. (χ2) | df | p | |||
| B1.2 (4)/GENDER (2) | 0.052532 | 3 | 0.99685 | 0.0136959 | 0.0136972 |
| B2.2 (4)/GENDER (2) | 1.287538 | 3 | 0.73209 | 0.0676558 | 0.0678111 |
| B3.2 (4)/GENDER (2) | 3.324484 | 3 | 0.34425 | 0.1083229 | 0.1089640 |
| B4.2 (4)/GENDER (2) | 0.809948 | 3 | 0.84709 | 0.0537059 | 0.0537836 |
| B5.2 (4)/GENDER (2) | 2.083801 | 3 | 0.55520 | 0.0859487 | 0.0862679 |
| B6.2 (4)/GENDER (2) | 1.404074 | 3 | 0.70458 | 0.0706366 | 0.0708135 |
| B7.2 (4)/GENDER (2) | 4.188129 | 3 | 0.24185 | 0.1213967 | 0.1223013 |
| B8.2 (4)/GENDER (2) | 3.171651 | 3 | 0.36591 | 0.1058322 | 0.1064299 |
| B9.2 (4)/GENDER (2) | 2.933161 | 3 | 0.40205 | 0.1018183 | 0.1023502 |
Note: df–degrees of freedom; p value.
© 2021 by the authors.
Details
; Hašková, Alena 2 ; Poliaková, Adriana 3 ; Munk, Michal 4
1 Department of Didactics of Natural Sciences in Primary Education, Faculty of Education, Comenius University in Bratislava, Račianska 59, 813 34 Bratislava, Slovakia
2 Department of Technology and Information Technologies, Faculty of Education, Constantine the Philosopher University in Nitra, 949 74 Nitra, Slovakia;
3 Department of Pedagogy and Social Pedagogy, Faculty of Education, Comenius University in Bratislava, Račianska 59, 813 34 Bratislava, Slovakia;
4 Department of Informatics, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, 949 74 Nitra, Slovakia;