Стратегии на образователната и научната политика

https://doi.org/10.53656/str2024-5-8-int

2024/5, стр. 639 - 654

INTENSIVE WELLNESS PROGRAM – A METHOD FOR OPTIMIZING MOTOR CONDITION

Darinka Ignatova
OrcID: 0000-0002-0564-584X
E-mail: darinka_bg68@yahoo.com
Department for Information and In-Service Training of Teachers
University of Sofia
224 Tzar Boris III Blvd.
1619 Sofia Bulgaria

Резюме: The present material derives theoretical foundations with an emphasis on the development of the motor state, based on the application of an intensive wellness program. The scientific state of teaching methodology in physical education and sports in the first high school stage of the educational degree is monitored. An intensive program for the development of general motor conditions according to the Tabata interval method has been approved in the first high school stage to achieve a high educational standard and increase the efficiency in motor-oriented training. Statistical verification of the effectiveness of a tested intensive wellness program with the organization of an experimental, ascertaining, training, and control stage is presented. Scientifically based conclusions and generalizations are drawn. The purpose of the research is to create an intensive wellness program for the development and improvement of general motor condition, based on the Tabata method in the first high school stage, by implementing and proving its practical effectiveness through testing in the PES 1 training consisting of an author’s complex of motor exercises, for wellness-interval training according to the Tabata method. The subject of analysis is the indicators allowing harmonization and individualization of the differences in the students’ motor potential between the two target groups – experimental and control, as well as establishing the effectiveness of the practical application of the implemented intensive wellness program. Scientifically based conclusions will allow establishing changes in the number of students who have covered a high educational standard, as well as improving the effectiveness of motor-oriented learning. The object of the present experimental research is the learning process in the discipline of physical education and sports in the first high school stage of the educational degree. The research scope of the present study is 50 students from two classes of the eighth grade, divided into 25 students each in two target groups – experimental and control. All examined individuals are in good physical and mental condition. To derive a relationship between tracked motor indicators, an analysis of dynamics was applied through mathematicalstatistical processing to establish variation and correlation coefficients. The application of the innovative technology took place within the academic year 2022/23.

Ключови думи: intensive wellness program, optimizing motor state, interval training, motor activity, motor potential, motor directionality

Introduction

In the largest aspect, the activity of secondary school students, as well as their conscious attitude to the work in the lesson of physical education and sports, depends on the motives that manifest themselves, develop and undergo changes under the influence of various interacting factors, and this includes the purposeful impact of the sports pedagogue (Dimitrova 2019a; 2019; 2021; Tseneva, Ignatov 2002). His abilities and opportunities to communicate with adolescents, to apply various approaches and methods of motivation are essential in order to be able to influence them successfully (Ignatova 2021; Ignatova, Iliev 2022; 2020; Simeonova, Momchilova 2012).

The Tabata method is not part of the curriculum of physical education and sports, but nevertheless it is a new and innovative method, with the application of which significant achievements are observed in the lives of sportsmen. As a teacher of physical education and sport, I have the opportunity to find that students are interested in practicing different exercises in the field of interval training. Some of the schools have a limited sports base, which requires the teacher to show creativity in a limited space, the lesson should have a high motor density. Part of the eighth-graders also practice other sports such as football, volleyball, taekwondo, basketball, baseball. The application of Tabata interval method – an innovative complex of motor exercises helps to improve the motor activity in the respective sport. Due to the topicality and interest of the students in this innovative method, I decided to track what the motor success rate is from the applied innovative technology for the development of motor condition, based on the Tabata method in the first high school stage of the educational degree.

Methods

The object of the present experimental research is the process of learning in the discipline of physical education and sports in the first high school stage of the educational degree. The subject of research are the students’ motor achievements and the effect of the practical implementation of the innovative technology. 50 students from two classes of the eighth grade took part in the research, divided into 25 students in two target groups – experimental and control. All subjects were in good motor condition. The research goal is to create an innovative technology for the development and improvement of general motor condition, based on the Tabata method in the first high school stage, by implementing and proving its practical effectiveness. Based on the goal, the following research tasks were formed:

– Analysis of theoretical literature on the problem.

– An effective approach to improving the motor condition of students through interval training activities.

– Development of an innovative technology for developing general motor condition, based on an innovative complex of motor exercises based on the Tabata method in the first high school stage and proving its practical effectiveness.

– Collection of empirical data in the cognitive stages of experimental research.

– Mathematical-statistical processing of the collected empirical material, as well as construction of correlation-structural models.

– Proving the practical effectiveness of the innovative technology.

– Analyzing the results of the application of the innovative technology.

– Practical application and multiplication of the effect of technology in practice, creation of theoretical and practical-applied contributions.

The working hypothesis of the present study is that if an innovative technology for the development of general motor condition, based on the Tabata interval method, is applied in the first high school stage, the number of students who have covered a high educational standard will increase and the effectiveness of motor training will be improved.

Results and analysis

Both target groups took part in the ascertainment stage. The control and experimental groups are with 25 students in each of them. Participants in both groups performed eight exercises, each lasting 20 seconds, which established the entry level of the study. All motor exercises are aligned with the goals and expected outcomes of the eighth grade physical education and sports curriculum.

In order to better visualize the data obtained from the first experimental study conducted for the two target groups, determining the general motor condition, summarized results were obtained, reflected in two circular quantitative diagrams, according to the level of success – low, average, good, very good and excellent. Figures 1 and 2 show the results of the two target groups according to the levels of their motor achievements.

>ŽǁϭϮйǀĞƌĂŐĞͲϭϲй'ŽŽĚͲϰϴйsĞƌLJŐŽŽĚϭϮйϭϮйdžĐĞůůĞŶƚͲϭϮйKƚŚĞƌͲϮϰйAscertaining stage -EG

Figure 1. EG – ascertaining stage

Figure 2. CG – ascertaining stage

When presenting the graphical image of the obtained results from figure 1, the results of 8a stand out, which generated the same results for: excellent, very good and poor success rate \(-12 \%\) for each group. As the largest relative share, the students from the experimental group – 8th grade, registered results falling to the good level of success rate \(-48 \%\), which is almost half of all examined persons who took part in the ascertaining stage. The students who demonstrated an average level of achievement are a total of \(16 \%\). From the results in Figure 2, the results of the control group - 8b grade stand out, demonstrating a very good level of success – \(44 \%\), which is nearly half of all those who took part in the study. A weak level was registered by \(16 \%\), and by \(8 \%\) more – \(24 \%\) of students from 8b gave an average level. \(12 \%\) of the students showed a good level, and only \(4 \%\) of all got into the excellent level of success. It is positive that the students from both target groups largely covered the standards above the average level. In order for the research to be complete, the parameters of the elements were determined according to their various indicators: central tendency: arithmetic mean \(-\overline{\mathrm{x}}\), median – Me, mode – Mo; distribution: kurtosis – Ex and coefficient of asymmetry – Ka; scatter: standard deviation \(-\sigma\), variance \(-\sigma^{2}\), range-R and coefficient of variation \(-\mathrm{V} \%\).

The summarized data according to different criteria and investigated elements of variation series in ascertaining stage for the two target groups are presented in tables 1 and 2.

Table 1. EG – signs of ascertaining stage

SymbolNameFormulaValueМоModeС най-много повторения (3) ечислото 148Mo= 148MeMedianМе= X13Me= 146
Arithmeticmean value== 146,66= 146,66XmaxMaximum valueXmax {X (1), X (2) ….X(n)}=X(n)Xmax= 182XminMinimum valueXmin {X (1), X (2), ….X(n)}=X (1)Xmin= 120RAmplitudeR= Xmax-XminR = 62Standard deviation= 16,402Dispersion2=2=270,20ExExcessEx= 0,16КаAsymmetry factorКа =Ка= 0,63V%Coefficientof variationV%=11,20

The coefficient of variation \(\mathrm{V} \%\) stands out, which is between 10 and \(30 \%\) \((\mathrm{V} \%=11.20)\), therefore the sample is satisfactorily uniform. After substitution according to the formula for calculating kurtosis – Ex, a result equal to Ex \(=0.16\) is reported, which is a positive value and this gives reason to conclude that the distribution has relatively higher elevation than the normal distribution.

When calculating the coefficient of asymmetry, a positive number \(\mathrm{Ka}=0.63\), \((\mathrm{Ka}\) \( \gt 0)\) is obtained, from which it follows that the peak of the empirical distribution is above the peak of the normal, therefore the distribution is right-skewed.

Table 2. CG – signs of the ascertaining stage

SymbolNameFormulaValueМоModeС най-много повторения (2) са числата127, 155, 166, 167Mo1= 127Mo2= 155Mo3= 166Mo4= 167
MeMedianМе= X13Me= 150Arithmetic meanvalue== 146,66= 146,80XmaxMaximum valueXmax {X (1), X (2) ….X(n)} =X(n)Xmax= 173XminMinimum valueXmin {X (1), X (2), ….X(n)} =X (1)Xmin= 118RAmplitudeR= Xmax-XminR = 55Standard deviation= 17,702Dispersion2=2= 313,30ExExcessEx= -1,32КаAsymmetry factorКа =Ка= -0,26V%Coefficient ofvariationV%=12,10

It is observed that the coefficient of variation \(\mathrm{V} \%\) of the control group is between 10 and \(30 \%(\mathrm{~V} \%=12.10)\), therefore the sample is satisfactorily uniform. After substitution according to the formula for calculating kurtosis – Ex, a result equal to \(\mathrm{Ex}=-1.32\) is reported, which as a result is a negative value and it can be concluded that the distribution has a relatively lower elevation than the normal distribution. When calculating the asymmetry coefficient, a negative number \(\mathrm{Ka}=-0.26(\mathrm{Ka} \lt \) 0) is also obtained, from which it follows that the peak of the empirical distribution is below the peak of the normal, therefore the distribution is left-skewed. In order to present data from results obtained by different indicators, research elements of the variation order from the control stage, determining the initial level of general motor condition of the students from the two target groups, data are summarized according to different researched criteria in tables 3 and 4.

Table 3. Various signs EG – control stage

SymbolNameFormulaValueМоModeС най-много повторения (2)са числата (157, 170, 173, 174,179, 180)Mo1 = 157Mo2= 170Mo3= 173Mo4 = 174Mo5 = 179Mo6 = 180MeMedianМе = X13Me = 176Arithmetic meanvalue== 178,36= 178,36X maxMaximum valueX max {X (1), X (2) ….X(n)} =X(n)Xmax=213X minMinimum valueX min {X (1), X (2), ….X(n)} =X (1)Xmin=152RAmplitudeR= X max – X minR = 61Standarddeviation= 15,72Dispersion2 =2 = 246,9ExExcessEx = 0,03КаAsymmetryfactorКа =Ка = 0,58V%Coefficient ofvariationV% = 8,8

A coefficient of variation \(\mathrm{V} \%\) in the experimental group for the 8th grade is outlined – under \(\mathrm{V} \%=10-8.8 \%\), therefore, the dispersion of the sign is small and a uniform outgrowth is recorded. According to the formula for calculating kurtosis – Ex, a result equal to Ex \(=0.03\) is reported, which as a result is a positive value – the degree of elevation compared to the normal distribution is small. The calculation of the asymmetry coefficient - Ka also gives a positive number \(\mathrm{Ka}=\) 0.58, \((\mathrm{Ka} \gt 0)\), from which it follows that the peak of the empirical distribution is above the peak of the normal, which indicates that the distribution at the control stage is also with right drawn asymmetry.

Table 4. Results of various CG signs – control stage

SymbolNameFormulaValueМоModeС най-много повторения (2)са числата (120, 131, 143, 151)Mo1= 120Mo2= 131Mo3= 143Mo4= 151MeMedianМе= X13Me= 150Arithmeticmean value== 178,36= 147,08XmaxMaximumvalueXmax{X (1), X (2) ….X(n)} =X(n)Xmax= 174XminMinimumvalueXmin{X (1), X (2), ….X(n)} =X (1)Xmin= 120RAmplitudeR= Xmax– XminR = 54Standarddeviation= 16,42Dispersion2=2= 268,7ExExcessEx= -1,01КаAsymmetryfactorКа =Ка= -0,15V%Coefficient ofvariationV%= 11,10

It is distinguished, the coefficient of variation \(\mathrm{V} \%\) in the control group between 10 and \(30 \%(\mathrm{~V} \%=11.10)\), therefore the sample is satisfactorily uniform. After substitution according to the formula for calculating kurtosis – Ex, a result equal to \(\mathrm{Ex}=-1.01\) is reported, which is a negative value, from which it is found that the distribution is relatively less elevated than the normal distribution. When calculating the coefficient of asymmetry – Ka, a negative number \(\mathrm{Ka}=-0.15\), \((\mathrm{Ka} \lt 0)\) is also obtained, from which it follows that the peak of the empirical distribution is below the peak of the normal, which means that the distribution is left-skewed asymmetry.

According to Fechner’s formula, proposed in the experimental study, the correlation coefficient – R between the two variables is determined.

Where: \(\mathrm{R}=\cfrac{\mathrm{a} \cdot \mathrm{d}-\mathrm{b} \cdot \mathrm{c}}{\sqrt{(a+b)(c+d)(a+c)(b+d)}}\)

a – high values for the test and high values for independent work

b – low values for the test and high values for independent work

c – high values for the test and low values for independent work

d – low values for the test and low values for independent work

The strength of correlation dependence between EG – input X and output Y – table 5.

Table 5. Correlation dependence EG – input X / output Y

Input EGFactor (X)Output EGConsequence (Y)X.YX^21220,671520,7118544148841360,751640,7722304184961480,811800,8526640219041330,731670,7822211176891490,821850,8727565222011350,741700,8022950182251440,791800,8525920207361200,661570,7418840144001240,681570,7419468153761320,731630,7721516174241410,771730,8124393198811460,801760,8325696213161430,791740,8224882204491490,821860,8727714222011390,761740,8224186193211630,901950,923178526569
1770,972080,9836816313291480,811730,8125604219041670,921960,9232732278891821,002131,0038766331241430,791700,8024310204491480,811790,8426492219041780,982050,9636490316841470,811790,8426313216091520,841830,862781623104366620,14445920,931854414884Кср= 0,81Кср= 0,84y = 0,8017x + 0,1914R = 0,98R2= 0,9635y=a+b.x

To build the regression model of dependence, the data from table 5 were used and the parameters were estimated by the following regression equation:

\(\mathrm{y}=\mathrm{a}+\mathrm{b} . \mathrm{x}\) where:

y – Dependent variable

a – Free member

b – Regression coefficient

x – Factor variable

The following formulas were used to find the free term ‘a’ and the regression coefficient ‘b’:

\[ \begin{aligned} & \mathrm{b}=\cfrac{n \cdot \sum x \cdot y-\sum x \cdot \sum y}{\mathrm{n} \cdot \sum x^{2}-\left(\sum x\right)^{2}} \\ & \mathrm{a}=\cfrac{1}{\mathrm{n}} \cdot\left(\sum y-b \cdot \sum x\right) \end{aligned} \] Parameter \(1-\mathrm{a}=0.19\)

Parameter \(2-\mathrm{b}=0.80\) (regression coefficient) at \(\mathrm{n}=25\)

In the case of coefficient of determination \(-\mathrm{R} 2=0.96\) and shows how much percentage is due to the change of the factor.

Completed, the regression model looks like this:

\(\mathrm{y}=0.19+0.80 . \mathrm{x}\)

The equation is interpreted as follows: Increasing the scores of the incoming EG examination by one point will increase the score by 0.8 points on the outgoing EG examination score. In order to make the study more precise between the interrelationships of the EG (input level – X) and EG (output level – Y) results, a scatter plot was constructed, on which, along the abscissa axis (X), the results of the input were plotted study, and on the ordinate axis were positioned the results of the exit study of the experimental group. Figure 3 graphically presents the distribution of the empirical data along the two variables X and Y, as well as the correlation found between them.

Figure 3. Scatter plot of the two variables X and Y

From the diagram in Figure 3, it is clearly observed that the variation of the unit definitions is very close, which gives reason to assume a strong correlation dependence between the quantities X and Y. The shape of the scattering cloud resembles the shape of an ellipse whose axis is not parallel on the coordinate axes, which gives reason to conclude that the correlation dependence is unidirectional (ascending) and has a linear form. In order to examine the interrelationships between the results of CG (input level – X) and CG (output level – Y), a scatter diagram was constructed, on which the results of the input study are plotted on the abscissa (X) and the results of the ordinate are plotted on the ordinate axis are the results of the control group’s exit survey. Figure 4 presents the statistics in the form of a graph.

Figure 4. Scatterplot of the two variables X and Y

From the diagram in Figure 4, it is clearly observed that the variation of the unit definitions is very close, which gives reason to assume a strong correlation dependence between the two variable quantities – X and Y. The shape of the scattering cloud resembles the shape of an ellipse, whose axis is not parallel to the coordinate axes, which gives reason to conclude that the correlation dependence is unidirectional (ascending) with a linear form. When examining the interrelationships between the results of EG (input level – X) and CG (input level – Y), a scatter diagram was constructed, on which the abscissa (X) is plotted with the results of the experimental group’s input study, and the ordinate is the results of the entrance examination of the control group are positioned. Figure 5 presents the statistics in the form of a graph.

Figure 5. Scatterplot of the two variables X and Y

From the diagram in Figure 5, it is clear that the variation of the unit definitions is not as close as the previous two comparisons, which suggests a significant correlation between the two variables X and Y. The shape of the scatter cloud resembles the shape of the ellipse, although it is much more elongated, its axis is parallel to the coordinate axes, which gives me reason to state that the correlation dependence is one-way (ascending) and has a linear form.

In the study of the interrelationships between the results of EG (initial level – X) and CG (initial level – Y), a scatter diagram was constructed, on which, on the abscissa axis (X), the results of the initial examination of the experimental group were plotted, and on the y-axis is the outcome of the control group. Figure 6 presents statistical data in the form of graphical interpretation.

Figure 6. Scatter plot of the two variables X and Y

From the diagram in Figure 10, it is clearly observed that the variation of the unit definitions is not as close again as in the third comparison, which suggests a significant correlation dependence between the two variable quantities – X and Y. The shape of the scatter cloud resembles the shape of the ellipse, although it is much more elongated, its axis is parallel to the coordinate axes, which gives reason to state that the correlation dependence is one-way (ascending) and has a linear form.

Discussion

The result obtained is again a positive number \(\mathrm{R}=0.62\) and is classified as a significant correlation (it falls in the range \(-0.5 \lt \mathrm{R} \lt 0.7\)-significant correlation). From the fact that the correlation has a straight dependence and a positive sign, it follows that when X increases, Y increases with the same force. When generalizing the information from regression and correlation studies, it can be concluded that the strongest dependence is at the items at the entry and exit level of the experimental group - with a reported correlation coefficient \(\mathrm{R}=0.98\), which shows once again that the relationship between the items is very strong and with a regression model \(\mathrm{y}=0.19+0.80 . \mathrm{x}\), as well as with a coefficient of determination \(\mathrm{R}^{2}=0.96\), indicating in turn the finding of the highest regression coefficient 0.80, which also marks the highest achievements of the comparisons conducted.

Based on these results, it can be concluded that the level of achievement of EG on this indicator has increased significantly and probably the imposition of the new training method through Tabata has influenced to increase the level of the general motor condition of high school students in EG .

Conclusion

Based on synthesized results from the entry and exit level of the studied contingent from the ascertaining and control stages of the two target mathematical and statistical methods were used in the processing of the data, assuming the proof of the working hypothesis.

According to which, the application of the new innovative technology for development of general motor condition, based on the Tabata method in the high school stage, will increase the number of students who have covered a educational standard and will improve the effectiveness of motor-oriented In order to statistically check whether this is the case, it is necessary to two hypotheses – null and alternative.

The null hypothesis \(\left(\mathrm{H}_{0}\right)\) is essentially an assumption of a null effect or the socalled null difference. It suggests that when empirical data they are due to random factors.

The alternative hypothesis (\(\mathrm{H}_{\mathrm{a}}\) ) claims exactly the opposite – according to it, the differences observed in the empirical data (effect, dependence) are the result of factors acting regularly (Georgieva, Kamenarova 2014). To test both used Student’ below 100. In this particular case, the volumes of both samples are the same – 25 elements each. It is based on the fact that for small and medium samples the variances of the two distributions are close in value. Differences between were compared for hypothesis testing. According to this method, the null is accepted or rejected depending on the obtained difference \(-\mathrm{H}_{0}\) : if \(\left(\mathrm{x}_{-} 0\right)^{-}=\mathrm{x}\); On: if \(\left(\mathrm{x} \_0\right)=\mathrm{x}\);

Legend:

\(\overline{\mathrm{x}}\)– average arithmetic value of the data obtained from the initial measurement of EG

\(\left(\mathrm{x} \_0\right)^{-}\)– average arithmetic value of the data obtained from the input measurement of the EG

\(\sigma \_0\)-standard deviation of the first EG measurement n – number of students (25)

\[ t_{e m}=\cfrac{\bar{x}-\overline{x_{0}}}{\cfrac{\sigma_{0}}{\sqrt{n}}}=\cfrac{178,36-146,64}{\cfrac{16,4}{5}}=\cfrac{31,72}{3,28}=9,67 \]

The critical region of the hypothesis \(\left(\mathrm{H}_{0}\right)\) is two-sided because of the two-way inequality defined in Na. The tabular value is then determined at the selected of error (5%), two-sided critical region, and degrees of freedom: \(\Phi=\mathrm{n}-1=24\), statistical significance level \(\alpha=0.05\) or 0.95 order quantile (certainty level of the result). risk the

From a table of quantiles of a T-distribution: where 1.7109 for a t-distribution

\[ t_{\tau}\left[\begin{array}{c} \alpha=0,05 \\ \text { КО двустранна } \\ \Phi=n-1=24 \end{array}\right]=1,7109 \]

Since, t_ \(\mathrm{T}=1.7109 \lt \mathrm{t}\) _em \(=9.67\), it can beconcluded thatthe alternativehypothesis

(Ha) \(\left(\mathrm{H}_{\mathrm{a}}\right)\) is accepted as valid. A statistically significant difference (dependency) was

observed between the research characteristics at the entry and exit level of the experimental group, due to the application of the innovative technology at the high school stage.

Based on this finding, it can be concluded that the null hypothesis (\(\mathrm{H}_{0}\) ) is rejected and the alternative hypothesis \(\left(\mathrm{H}_{\mathrm{a}}\right)\) is a accepted, that is, the statement that the increase in post-test results is reduced to random factors, decreasing the overall motor activity of the research is due to the applied innovative training technology based on the Tabata interval method.

NOTES

1. Physical Education and Sports - a discipline in the Bulgarian education system

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ИЗСЛЕДВАНЕ ПРИЛОЖИМОСТТА НА БЛОКОВИ ВЕРИГИ ОТ ПЪРВО НИВО (L1) В СИСТЕМА ЗА ЕЛЕКТРОННО ОБУЧЕНИЕ

Андриан Минчев, Ваня Стойкова, Галя Шивачева, Доц Анелия Иванова

ПРЕДИЗВИКАТЕЛСТВА ПРИ ПРОМЯНА НА ПЛАТФОРМИ ЗА ДИСТАНЦИОННО ОБУЧЕНИЕ

Антон Недялков, Милена Кирова, Мирослава Бонева

APPLICATION OF ZSPACE TECHNOLOGY IN THE DISCIPLINES OF THE STEM CYCLE

Boyana Ivanova, Kamelia Shoilekova, Desislava Atanasova, Rumen Rusev

TEACHERS' ADAPTATION TO CHANGES IN AN INCREASINGLY COMPLEX WORLD THROUGH THE USE OF AI

Zhanat Nurbekova, Kanagat Baigusheva, Kalima Tuenbaeva, Bakyt Nurbekov, Tsvetomir Vassilev

АТОСЕКУНДНОТО ОБУЧЕНИЕ – МЕТАФОРА НА ДНЕШНОТО ОБРАЗОВАНИЕ

Юлия Дончева, Денис Асенов, Ангел Смрикаров, Цветомир Василев

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MANAGERIAL ASPECTS OF COOPERATION AMONG HIGHER EDUCATION INSTITUTIONS AND THEIR STAKEHOLDERS

Olha Prokopenko, Svitlana Perova, Tokhir Rakhimov, Mykola Kunytskyi, Iryna Leshchenko

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FORMATION OF PROFESSIONAL SKILLS OF AGRICULTURAL ENGINEERS DURING LABORATORY PRACTICE WHEN STUDYING FUNDAMENTAL SCIENCE

Ivan Beloev, Oksana Bulgakova, Oksana Zakhutska, Maria Bondar, Lesia Zbaravska

ИМИДЖ НА УНИВЕРСИТЕТА

Галя Христозова

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COMPETITIVENESS AS A RESULT OF CREATIVITY AND INNOVATION

Nikolay Krushkov, Ralitza Zayakova-Krushkova

INTELLECTUAL PROPERTY AND SECURITY IN THE INTEGRATED CIRCUITS INDUSTRY

Ivan Nachev, Yuliana Tomova, Iskren Konstantinov, Marina Spasova

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PROBLEMS AND PERSPECTIVES FOR SOCIAL ENTREPRENEURSHIP IN HIGHER EDUCATION

Milena Filipova, Olha Prokopenko, Igor Matyushenko, Olena Khanova, Olga Shirobokova, Ardian Durmishi

2023 година
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DEVELOPMENT OF A COMMON INFORMATION SYSTEM TO CREATE A DIGITAL CAREER CENTER TOGETHER WITH PARTNER HIGHER SCHOOLS

Yordanka Angelova, Rossen Radonov, Vasil Kuzmov, Stela Zhorzh Derelieva-Konstantinova

DRAFTING A DIGITAL TRANSFORMATION STRATEGY FOR PROJECT MANAGEMENT SECTOR – EMPIRICAL STUDY ON UAE

Mounir el Khatib, Shikha al Ali, Ibrahim Alharam, Ali Alhajeri, Gabriela Peneva, Jordanka Angelova, Mahmoud Shanaa

VOYAGE OF LEARNING: CRUISE SHIPS WEATHER ROUTING AND MARITIME EDUCATION

Svetlana Dimitrakieva, Dobrin Milev, Christiana Atanasova

СТРУКТУРНИ ПРОМЕНИ В ОБУЧЕНИЕТО НА МЕНИДЖЪРИ ЗА ИНДУСТРИЯ 5.0

Недко Минчев, Венета Христова, Иван Стоянов

RESEARCH OF THE INNOVATION CAPACITY OF AGRICULTURAL PRODUCERS

Siya Veleva, ; Margarita Mondeshka, Anka Tsvetanova

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ВИДОВЕ ТРАВМИ В ПАРАШУТИЗМА И ПРЕВЕНЦИЯТА ИМ

Капитан III ранг Георги Калинов

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DETERMINING THE DEGREE OF DIGITALIZATION OF A HIGHER EDUCATION INSTITUTION

Acad. Hristo Beloev, Angel Smrikarov, Valentina Voinohovska, Galina Ivanova

ОТ STEM КЪМ BEST: ДВА СТАНДАРТА, ЕДНА ЦЕЛ

Андрей Захариев, Стефан Симеонов, Таня Тодорова

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EFFECT OF RESILIENCE ON BURNOUT IN ONLINE LEARNING ENVIRONMENT

Radina Stoyanova, Sonya Karabeliova, Petya Pandurova, Nadezhda Zheckova, Kaloyan Mitev

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INTELLIGENT ANIMAL HUSBANDRY: FARMER ATTITUDES AND A ROADMAP FOR IMPLEMENTATION

Dimitrios Petropoulos, Koutroubis Fotios, Petya Biolcheva, Evgeni Valchev

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STUDY OF THE DEVELOPMENT OF THE USE OF COMMUNICATIVE TECHNOLOGIES IN THE EDUCATIONAL PROCESS OF ENGINEERS TRAINING

Ivan Beloev, Valentina Vasileva, Sergii Bilan, Maria Bondar, Oksana Bulgakova, Lyubov Shymko

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РАЗПОЛОЖЕНИЕ НА ВИСШИТЕ УЧИЛИЩА В БЪЛГАРИЯ В КОНТЕКСТА НА ФОРМИРАНЕ НА ПАЗАРА НА ТРУДА

Цветелина Берберова-Вълчева, Камен Петров, Николай Цонков

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MODERNIZATION OF THE CONTENT OF THE LECTURE COURSE IN PHYSICS FOR TRAINING FUTURE AGRICULTURAL ENGINEERS

Ivan Beloev, Valentina Vasileva, Vasyl Shynkaruk, Oksana Bulgakova, Maria Bondar, Lesia Zbaravska, Sergii Slobodian

2022 година
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ORGANIZATION OF AN INCLUSIVE EDUCATIONAL ENVIRONMENT FOR THE STUDENTS WITH SPECIAL NEEDS

Halyna Bilavych, Nataliia Bakhmat, Tetyana Pantiuk, Mykola Pantiuk, Borys Savchuk

ДИГИТАЛИЗАЦИЯ НА ОБРАЗОВАНИЕТО В БЪЛГАРИЯ: СЪСТОЯНИЕ И ОБЩИ ТЕНДЕНЦИИ

Теодора Върбанова, Албена Вуцова, Николай Нетов

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ПРАВОТО НА ИЗБОР В ЖИВОТА НА ДЕЦАТА В РЕПУБЛИКА БЪЛГАРИЯ

Сийка Чавдарова-Костова, Даниела Рачева, Екатерина Томова, Росица Симеонова

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DIAGNOSIS AS A TOOL FOR MONITORING THE EFFECTIVENESS OF ADDICTION PREVENTION IN ADOLESCENTS

O.A. Selivanova, N.V. Bystrova, I.I. Derecha, T.S. Mamontova, O.V. Panfilova

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ПУБЛИЧНОТО РАЗБИРАНЕ НА НАУКАТА В МРЕЖОВИЯ СВЯТ

Светломир Здравков, Мартин Й. Иванов, Петя Климентова

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ДИГИТАЛНАТА ИНТЕРАКЦИЯ ПРЕПОДАВАТЕЛ – СТУДЕНТ В ОНЛАЙН ОБУЧЕНИЕТО В МЕДИЦИНСКИТЕ УНИВЕРСИТЕТИ

Миглена Търновска, Румяна Стоянова, Боряна Парашкевова, Юлияна Маринова

2021 година
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SIGNAL FOR HELP

Ina Vladova, Milena Kuleva

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PREMISES FOR A MULTICULTURAL APPROACH TO EDUCATION

Anzhelina Koriakina, Lyudmila Amanbaeva

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ПЪРВА СЕДМИЦА ДИСТАНЦИОННО ОБУЧЕНИЕ В СУ „ИВАН ВАЗОВ“ В СТАРА ЗАГОРА

Тони Чехларова, Динко Цвятков, Неда Чехларова

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METHODOLOGY OF SAFETY AND QUALITY OF LIFE ON THE BASIS OF NOOSPHERIC EDUCATION SYSTEM FORMATION

Nataliia Bakhmat, Nataliia Ridei, Nataliia Tytova, Vladyslava Liubarets, Oksana Katsero

2020 година
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HIGHER EDUCATION AS A PUBLIC GOOD

Yulia Nedelcheva, Miroslav Nedelchev

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НАСЪРЧАВАНЕ НА СЪТРУДНИЧЕСТВОТО МЕЖДУ ВИСШИТЕ УЧИЛИЩА И БИЗНЕСА

Добринка Стоянова, Блага Маджурова, Гергана Димитрова, Стефан Райчев

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THE STRATEGY OF HUMAN RIGHTS STUDY IN EDUCATION

Anush Balian, Nataliya Seysebayeva, Natalia Efremova, Liliia Danylchenko

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МИГРАЦИЯ И МИГРАЦИОННИ ПРОЦЕСИ

Веселина Р. Иванова

SOCIAL STATUS OF DISABLED PEOPLE IN RUSSIA

Elena G. Pankova, Tatiana V. Soloveva, Dinara A. Bistyaykina, Olga M. Lizina

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ETHNIC UPBRINGING AS A PART OF THE ETHNIC CULTURE

Sholpankulova Gulnar Kenesbekovna

2019 година
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EMOTIONAL COMPETENCE OF THE SOCIAL TEACHER

Kadisha K. Shalgynbayeva, Ulbosin Zh.Tuyakova

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УЧИЛИЩЕТО НА БЪДЕЩЕТО

Наталия Витанова

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POST-GRADUATE QUALIFICATION OF TEACHERS IN INTERCULTURAL EDUCATIONAL ENVIRONMENT

Irina Koleva, Veselin Tepavicharov, Violeta Kotseva, Kremena Yordanova

ДЕЦАТА В КОНСТИТУЦИОННИТЕ НОРМИ НА БЪЛГАРИЯ

Румен Василев, Весела Марева

СЪСТОЯНИЕ НА БЪЛГАРСКОТО ОБРАЗОВАНИЕ

Анелия Любенова, Любомир Любенов

ЕДИН НОВ УЧЕБНИК

Ирина Колева

2018 година
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A NEW AWARD FOR PROFESSOR MAIRA KABAKOVA

Irina Koleva, Editor-in-

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BLENDED EDUCATION IN HIGHER SCHOOLS: NEW NETWORKS AND MEDIATORS

Nikolay Tsankov, Veska Gyuviyska, Milena Levunlieva

ВЗАИМОВРЪЗКАТА МЕЖДУ СПОРТА И ПРАВОТО

Ивайло Прокопов, Елица Стоянова

ХИМЕРНИТЕ ГРУПИ В УЧИЛИЩЕ

Яна Рашева-Мерджанова

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2017 година
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ЗНАЧИМОСТТА НА УЧЕНЕТО: АНАЛИЗ НА ВРЪЗКИТЕ МЕЖДУ ГЛЕДНИТЕ ТОЧКИ НА УЧЕНИЦИ, РОДИТЕЛИ И УЧИТЕЛИ

Илиана Мирчева, Елена Джамбазова, Снежана Радева, Деян Велковски

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ОРГАНИЗАЦИОННА КУЛТУРА В УЧИЛИЩЕ

Ивайло Старибратов, Лилия Бабакова

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КОУЧИНГ. ОБРАЗОВАТЕЛЕН КОУЧИНГ

Наталия Витанова, Нели Митева

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ЕМПАТИЯ И РЕФЛЕКСИЯ

Нели Кънева, Кристиана Булдеева

2016 година
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2015 година
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ПРАГМАТИЧНАТА ДИДАКТИКА

Николай Колишев

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2014 година
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КОХЕРЕНТНОСТ НА ПОЛИТИКИ

Албена Вуцова, Лиляна Павлова

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USING THE RESULTS OF A NATIONAL ASSESSMENT OF EDUCATIONAL ACHIEVEMENT

Thomas Kellaghan, Vincent Greaney, T. Scott Murray

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USING THE RESULTS OF A NATIONAL ASSESSMENT OF EDUCATIONAL ACHIEVEMENT

Thomas Kellaghan, Vincent Greaney, T. Scott Murray

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PROFESSIONAL DEVELOPMENT OF UNIVERSITY FACULTY: А SOCIOLOGICAL ANALYSIS

Gulnar Toltaevna Balakayeva, Alken Shugaybekovich Tokmagambetov, Sapar Imangalievich Ospanov

USING THE RESULTS OF A NATIONAL ASSESSMENT OF EDUCATIONAL ACHIEVEMENT

Thomas Kellaghan, Vincent Greaney, T. Scott Murray

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РЕФЛЕКСИЯТА В ИНТЕГРАТИВНОТО ПОЛЕ НА МЕТОДИКАТА НА ОБУЧЕНИЕТО ПО БИОЛОГИЯ

Иса Хаджиали, Наташа Цанова, Надежда Райчева, Снежана Томова

USING THE RESULTS OF A NATIONAL ASSESSMENT OF EDUCATIONAL ACHIEVEMENT

Thomas Kellaghan, Vincent Greaney, T. Scott Murray

2013 година
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QUESTIONNAIRE DEVELOPMENT

ÎÖÅÍßÂÀÍÅÒÎ

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MASS MEDIA CULTURE IN KAZAKHSTAN

Aktolkyn Kulsariyeva Yerkin Massanov Indira Alibayeva

РЪКОВОДСТВО ЗА СЪСТАВЯНЕ НА ТЕСТОВЕ*

Фернандо Картрайт, Джери Мусио

РОССИЙСКАЯ СИСТЕМА ОЦЕНКИ КАЧЕСТВА ОБРАЗОВАНИЯ: ГЛАВНЫЕ УРОКИ

В. Болотов / И. Вальдман / Г. Ковалёва / М. Пинская

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ОЦЕНЯВАНЕ НА ГРАЖДАНСКИТЕ КОМПЕТЕНТНОСТИ НА УЧЕНИЦИТЕ: ПРЕДИЗВИКАТЕЛСТВА И ВЪЗМОЖНОСТИ

Светла Петрова Център за контрол и оценка на качеството на училищното образование

РЪКОВОДСТВО ЗА СЪСТАВЯНЕ НА ТЕСТОВЕ*

Фернандо Картрайт, Джери Мусио

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Уважаеми читатели,

вет, както и от международния борд за предоставените статии и студии, за да могат да бъдат идентифицирани в полето на образованието пред широката аудитория от педа- гогически специалисти във всички степени на образователната ни система. Благодаря за техния всеотдаен и безвъзмезден труд да създават и популяризират мрежа от научни съобщества по профила на списанието и да насърчават научните изследвания. Благодаря на рецензентите от национално представените висши училища, на- учни институции и

РЪКОВОДСТВО ЗА СЪСТАВЯНЕ НА ТЕСТОВЕ

Фернандо Картрайт, Джери Мусио

2012 година
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DEVELOPMENT OF SCIENCE IN KAZAKHSTAN IN THE PERIOD OF INDEPENDENCE

Aigerim Mynbayeva Maira Kabakova Aliya Massalimova

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СИСТЕМАТА ЗА РАЗВИТИЕ НА АКАДЕМИЧНИЯ СЪСТАВ НА РУСЕНСКИЯ УНИВЕРСИТЕТ „АНГЕЛ КЪНЧЕВ“

Христо Белоев, Ангел Смрикаров, Орлин Петров, Анелия Иванова, Галина Иванова

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ПРОУЧВАНЕ НА РОДИТЕЛСКОТО УЧАСТИЕ В УЧИЛИЩНИЯ ЖИВОТ В БЪЛГАРИЯ

* Този материал е изготвен въз основа на резултатите от изследването „Parental Involvement in Life of School Matters“, проведено в България в рамките на проек- та „Advancing Educational Inclusion and Quality in South East Europe“, изпълняван

ВТОРИ ФОРУМ ЗА СТРАТЕГИИ В НАУКАТА

Тошка Борисова В края на 2011 г. в София се проведе второто издание на Форум за страте- гии в науката. Основната тема бе повишаване на международната видимост и разпознаваемост на българската наука. Форумът се организира от „Elsevier“ – водеща компания за разработване и предоставяне на научни, технически и медицински информационни продукти и услуги , с подкрепата на Министер- ството на образованието, младежта и науката. След успеха на първото издание на Форума за стратегии в науката през

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РЕЙТИНГИ, ИНДЕКСИ, ПАРИ

Боян Захариев