PhD, Professor A.V. Karavan¹
Dr.Hab., Professor R.M. Kadyrov^1
1St. Petersburg State University of Architecture and Civil Engineering, St. Petersburg

Keywords: management, model, academic mass sports, competition, fitness, students.

Background. Research and practical basics for the national physical education and mass popular sports were laid back in 1950-60ies. The content and practical fundamentals of athletic training systems were well developed and since then successfully format and facilitate the sport training practices. It should be mentioned, however, that further theoretical substantiations are needed for progress of some institutional and management issues of the academic athletic progress systems [3], since the popularity of mass sports and competitive process largely depends on how efficient the institutional framework of the system is.

Athletic training process management may be ranked among the most challenging and critical components of the academic education and training system; and when this management component is reasonably efficient, it secures access to academic trainings and competitions for every student. Such process management system shall be designed to effectively advance mass sports by harmonic efforts of the management agencies and the reporting sport organizations. It is natural that the process starts from the mass sport system project design (modeling) by a university

Objective of the study was to develop an academic mass sports management model with the mass sports considered as composed of the mission- and function-specific components.

Methods and structure of the study. Any modeling process traditionally starts from a verbal outline of the subject process; followed by the model digitalizing; and model isomorphism tests; and then the model is tested by practical computation experiments. As underlined by I.I. Michael [4], however, no formal modeling rules have been established as yet albeit the above provisional modeling stages make it possible to simplify the modeling process to a degree.

As demonstrated by A. Tataru [5], a model may be interpreted as a clone of the real system with a limited number of components specified by the modeler. Every modeler assigns values to each component of the system on his own discretion and, hence, model varies in components depending on the goals of the modeling process. Success of a model depends on how well the key components are specified, ranked and addressed in the model design with their interrelations.

If the modern mass sport system is interpreted as a combination of elements each designed for some function, the key functional elements may include planning, sport reserve training, performance testing and accounting elements that collectively ensure the mass sport system operations on a systemic basis. The system description may include the system performance efficiency rating indices with a variety of potential solutions to different situations viewed as components of the system model and its inputs. Potential solutions with the anticipated system performance efficiency rates may be viewed as deliverables of the model, with the model design process being interactive. Upon the model testing experiment with the potential situational solutions, the model may be recommended for implementation.

The mass sport system performance efficiency under the study was rated by the following criteria: numbers of qualifiers classified by the test results; ratio of qualified athletes to the total participation; ratio of the training/ education sessions/ competitions to the qualified athletes etc. It should be mentioned that the set of the mass sport system performance efficiency rating criteria must be dependable and objective [1].

Study findings and discussion. At the first stage of the educational experiment we used the following variable factors: competitive events in the sport groups (х₁); and progress test frequency (х₂) for the study period; versus percentage of qualified athletes for the period as the model efficiency rate.

The equation looks as follows: у = 95,7 + 1,25х₁ + 0,75х₂ + 0,25х₁х₂

The equation shows reduction of the ratios that may be interpreted as indicative of the maximal efficiency rates under the experimental conditions. Given on Figure 1 hereunder is the scheme showing the three stages of the model testing experiment.

Figure 1. Percentage of qualified athletes versus competitions and progress test frequency

Upon the process planning, competency of the model implementing personnel needs to be tested. If the competency meets the standards and requirements, the mass sport system model may be implemented. The system operations must be planned using multifactor plans supported by the qualitative and quantitative performance milestones, with dispersion and multiple regression analyses used to process the flow of multifactor experimental data generated in the process.

Upon completion of the educational experiment, we run a questionnaire survey of the faculty and students to profile their satisfaction with the experimental model of the mass sport system: see the outcome data in Table 1 hereunder.

Table 1. Experimental model of the mass sport system: satisfaction rates

The above data demonstrate fair degrees of satisfaction with the experimental model of the mass sport system classified by the university communities, with the Physical Education Department faculty and students showing the highest satisfaction rates followed by the Department deans and sport service managers. Given in Table 2 hereunder are model design element efficiency self-rates versus the executives’ training efficiency rates

Table 2. Model element efficiency self-rates versus executives’ training efficiency rates, with Spearman ratio

The above data demonstrate the highest progress in the managers’/ executives’ training efficiency rates and progress tests. It may be advantageous, however, to have the competitive process planned by the education groups and courses on their own to step up their responsibility for success of the mass sport model.

Conclusion. The experimental data showed benefits of the proposed experimental model of academic mass sport system that allows to:

• Effectively design and manage the competitive and progress test process;
• Rate the academic progress in Physical Education discipline by the relevant competitive success rates;
• Improve the academic competitive system design.

References

1. Kadyrov R.M., Blakhin G.N. Modeli v teorii fizicheskoy podgotovki [Modeling in physical education theory]. St. Petersburg: Inkeria publ., 2014, 215 p.
2. Kadyrov R.M., Karavan A.V., Get’man V.D. Model samoupravleniya studentami fizicheskoy trenirovkoy [Student's self-guided physical training model]. Teoriya i praktika fiz. kultury, 2015, no. 9, pp. 3-4.
3. Lubysheva L.I., Peshkova N.V. Integratsiya deyatelnosti sportivnogo kluba i kafedry fizicheskoy kultury v kontekste razvitiya studencheskogo sporta [Integration of sports club and Physical Education Department activities in context of university sport development]. Teoriya i praktika fiz. kultury, 2016, no. 5, pp. 90-92.
4. Mikhail I.I. Modelirovanie fizicheskoy podgotovki v voenno-uchebnom zavedenii [Modeling physical education process in military educational establishment]. St. Petersburg: VAS publ., 2011, 178 p.
5. Tataru A.G. Modelirovanie protsessa fizicheskoy podgotovki [Modeling physical education process]. Rostov-on-Don: RMIMF publ., 1996, 245 p.

Corresponding author: fiz.vos@spbgasu.ru

Abstract

The study analyzes academic mass sports management models with relevant competitive and progress test schedules. A high priority given to the subject is due to the key role of the applied academic sports in the academic physical education and sport services. It should be mentioned that the progress of academic mass sports is often hampered by the inefficient sport management traditions. Objective of the study was to develop and offer an academic mass sports management model with the mass sports considered as composed of the mission- and function-specific components. The new academic mass sports management model includes process planning, sport activists’ training, and progress test and accounting elements altogether geared to systematize the academic mass sport process. A special priority in the academic mass sports management model design is given to the model efficiency rates and a variety of probabilistic solutions for the model elements and inputs. The model outputs are determined by the probabilistic solutions versus preset (expected) model efficiency rates – to secure a reasonable customization/ interactivity of the model design. Applied for the model efficiency rating purposes were the following criteria: numbers of classified successful competitors; ratios of successful competitors to the total; total training days to the qualified athletes’ number ratio etc. The model piloting experiment showed benefits of the proposed academic mass sports management model.