Imitational modeling method to test progress in academic self-reliant physical trainings

PhD, Professor V.D. Getman
St. Petersburg State University of Architecture and Civil Engineering, St. Petersburg

Keywords: students, physical education, self-reliant physical trainings, imitational modeling, education.

Background Presently the physical education and sport theory and practice gives a high priority to the self-reliant training models with the relevant process design and management competences viewed as the key factor for habitual practices and good progress in the theoretical studies and practical trainings.

The self-reliant physical education may be efficiently designed and managed only when the theoretical and practical training tools are sensitive enough to the students’ values and priorities in the individual health agendas. Due modeling knowledge and skills are needed to select the most efficient physical education tools and design the own physical education progress path. Every student shall be competent enough in own physical education process design domain for success of the trainings [4-6].

Since the valid academic physical education schedule (90-min session a week) is too short for the health protection and improvement purposes, the academic system must provide students with the self-reliant physical training system design skills by a special training course [3]. Such a self-reliant physical training system needs to fully mobilize the students’ knowledgebase, gifts and skills to attain the individual health goals. “The knowledge building model shall include the program framework to spell out the ideological, theoretical and practical provisions for the course, prioritize the learning materials with the relevant knowledge, skills and competence building stages and the goals attainable by the studies. It is clear that such program framework shall model and specify the training process mission and its didactic foundation with the ‘overhead’ structure to be raised on the latter; versions of every specific work program; and the theoretical and practical materials to support the education process" [3].

Objective of the study was to develop a self-reliant physical training model for the beginner (1-year) academic groups.

Methods and structure of the study. A core mission of the self-reliant physical training model is to secure the highest academic performance of the students to make them fit for the future professional service and its responsibilities. Educational provisions for the academic studies imply the students' physical activity being designed on a most effective basis in its every element (beginner, core training and rehabilitation stages). Thus the beginner training stage under the study was intended to improve the performance as soon as possible. Given on Figure 1 hereunder is the logical pattern of the micro-imitational model for the academic performance building in the beginner training stage. First, the pseudo-random numbers generator was used to fix the training system startup moment; and then the exercises were timed to efficiently enhance the central nervous system functionality and encourage the physical activity.

.Figure 1. Micro-imitational physical education model for the beginner training stage

This micro-model specifies only the motor density for each training session versus its goals. When the session completion time is found, it is fixed in the counter of training sessions (k). One of the inputs to the system indicative of the physical fitness is the number of sessions per micro-cycle (n). The current number of classes attended (k) is compared with the preset number (n) and when they match, the next class startup moment is generated. Otherwise, the performance status is updated, with the every exercise time revised so as to attain the target performance. In addition, the system presets the number of classes for the whole training stage. Upon completion of every training micro-stage, the system rates the actual number of classes versus the target value for the micro-stage.

Results and discussion. For the imitational experiment design purposes, we specified the external (exogenous) and internal (endogenous) variables of influence. The external variables (factors) of influence on the training process include the physical activity elements (training scope and intensity), frequencies and durations of the training elements. And the individual performance rates are used as the internal parameters. At the same time, each factor (external variable) can have one of a few values referred to as the levels, with the individual performance rated by a specific list of factor levels. It should be noted that we may choose a few specific workload parameters and arrive to their similar input by means of the imitational experiment.

Of special interest in the practical and theoretical aspects is the issue of how physical workloads shall ideally be controlled in a training process. We believe that a multi-level model is the best solution. Such model may cover the key workload components such as the training time/ scope; intensity; repetitions; rest intervals etc. – that are exogenous quantifiable variables. In our specific case, the independent (exogenous) variables include the training session time; training frequency for a specific period; and the rest breaks between the training sessions. Depending on the model order, different indirect performance rates may be used as the response functions (quantitative parameters of the training effect).

The training process may be presented by the following two formulae:

yе(t) = F(x, z, k, γ, t),

yе – performance rate;

x, z, k, γ – physical workload components per session;

t – system time parameter.

М(t) = F(y, v, h, t),

v, h – training workload parameters.

The first equation identifies the vector function yе (t), and the second uses the produced value y and the second-order exogenous variables v, h to identify the endogenous training variables M (t).

Having found the key values for the training process, we may find their variation area within the relevant range, and then find the specific functional dependencies of the above equations.

The above-described micro-imitational model is a first-order model, with the second-order variables obtainable by multiple runs of the computer code. When we need a longer training system being modeled, we must create a separate control code to automatically rate the workload components, with active experimental tools applied to find the optimal workload control parameters for the training system. This modeling process may be quite challenging due to the nonlinear correlation of the training frequency with the workload intensity plus the wide variability of the indirect performance rates in the training process.

Based on the hierarchy of models and the relevant experiments, the workload controls in the self-reliant physical training system may be classified into the instant, micro-stage- and stage-specific ones, and the experiments need to be designed correspondingly. Thus the instant workload controls imply the short-term workload elements (specific exercises in the training session) being efficiently controlled/ managed to attain the training session goals. Most appropriate in this case is a fractional factorial experiment with minimized tests and with the workload parameters varying in a wide range. Subject to the instant control mechanisms in this case are the indirect performance rates most sensitive to a single impact.

And the micro-stage workload controls may be subject to evolutionary experiments geared to maintain the specific quantitative values at the required levels and/or let them vary as required to attain the training process goals [2].

Conclusion. The study found that the training process intensity is a key factor and greatest contributor to a short-term training system. Optimal quantitative characteristics of the training workloads in the training system need to be found by the imitational modeling tools to find the most effective versions of the training system design.

References

  1. Savelyev D.S., Grigoryev V.I., Gromov M.M. Academic physical education standardization imperatives. Teoriya i praktika fiz. kultury, 2018, pp. 20-22.
  2. Elmurzaev M.A., Panchenko I.A., Pakholkova N.V. Physical recreation service model design options: innovative development vector. Teoriya i praktika fiz. kultury, 2019no. 3. pp. 47-49.
  3. Panchenko I.A., Grigoryev V.I. GTO Complex as a basis for academic physical education efficiency improvement. Teoriya i praktika fiz. kultury, 2017. no. 4.pp. 23-25.
  4. Elmurzaev M.A., Panchenko I.A. Activity and operation in physical recreation. Teoriya i praktika fiz. kultury, 2017, no. 5, pp. 6-8.
  5. Getman V.D. Theory and Methods of Physical Education discipline in physical education specialist training process. St. Petersburg: VTU ZhDV i VOSO publ., 2008. 82  p.
  6. Kadyrov R.M. Physical training design. St. Petersburg: MIPE publ., 2006.178 p.

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Abstract

Presently the physical education and sport theory and practice gives a high priority to the self-reliant training models with the relevant process design and management competences viewed as the key factor for habitual practices and good progress in the theoretical studies and practical trainings. The self-reliant physical education may be efficiently designed and managed only when the theoretical and practical training tools are sensitive enough to the students’ values and priorities in the individual health agendas. Due modeling knowledge and skills are needed to select the most relevant physical education tools and design the own physical education progress path. Every student shall be competent enough in own physical education process design domain for success of the trainings. One of the key goals of the self-reliant physical education process is to secure high academic performance standards to meet the future professional requirements. Academic performance-improvement physical education service shall make an emphasis on the optimal physical activity of the students in every stage of the academic studies and physical fitness step (beginner trainings, intensive training phase and fitness for academic performance). We used an imitational modeling method to test progress in the academic self-reliant physical trainings with the following provisions: (1) Certain institutional limitations on the self-reliant trainings that need to be customized to the regular academic curricula; and (2) shortage of the analytical tools for this form of training service. To secure the physical workloads in the academic studies being well designed and managed, we offered a multilevel training model with the relevant external and internal variables, limitations and specific progress logics.