OSI Reference Model. Process approach to IT management. Reference Models Ideal Reference Model

Model classification

The problem of classifying models, like any fairly complex phenomena and processes, is complex and multifaceted. The objective reason for this is that the researcher is only interested in one property (or several properties) of the system (object, process, phenomenon), for which the model was created. Therefore, the classification can be based on many different classification features: description method, functional purpose, degree of detail, structural properties, scope, etc.

Let's consider some of the most frequently used classes (types) of models (Table 1.4.1).

Table 1.4.1

Classification attribute	Types of models
Essence of the model	- material (physical) - ideal (imaginary) - informational (theoretical, abstract)
Characteristics of the simulation object	- appearance model - structure model - behavior model
Degree of formalization	- non-formalized - partially formalized - formalized
Purpose of the model	- research:. descriptor. cognitive. conceptual. formal - educational - workers:. optimization. managerial
Role in object management	- recording - reference - prognostic - simulation - optimization
Time factor	- static - dynamic

Material(physical, real) models - models built by means of the material world to reflect its objects, processes.

Ideal(imaginary) models - models built by means of thinking on the basis of our consciousness.

Information(abstract, theoretical) models - models built on one of the languages \u200b\u200b(sign systems) for coding information.

Material Modelsare real, material constructions that serve to replace the original in a certain respect. The main requirement for the construction of this class of models is the requirement of similarity (similarity, analogy) between the model and the original. There are several types of similarity - geometric, physical, analogy, etc.

Geometric similarityis the main requirement for the construction of geometric models, which are an object geometrically similar to its prototype and serving for demonstration purposes. Two geometrical figures are similar if the ratio of all respective lengths and angles is the same. If you know the coefficient of similarity - the scale, then simply multiplying the size of one figure by the scale value determines the size of the other figure. In the general case, such a model demonstrates the principle of operation, the relative position of parts, the process of assembly and disassembly, the layout of the object and is intended to study properties that are invariant (independent) from the absolute values \u200b\u200bof the linear dimensions of the object. Examples of geometric models are: car models, mannequins, sculptures, prostheses, globes, etc. They depict the prototype not in all the variety of its properties, not in any qualitative boundaries, but in purely spatial boundaries. Here there is a similarity (similarity) not in general between things, but between special types of things - bodies. This is the limitation of this class of models. Note that direct similarity is realized here.

Physical similarityrefers to the model and the original of the same physical nature and reflects their similarity in the same ratios of the same physical variables in the corresponding space-time points. Two phenomena are physically similar if, according to the given characteristics of one, the characteristics of the other can be obtained by simple recalculation, which is analogous to the transition from one system of units of measurement to another. Geometric similarity is a special case of physical similarity. With physical similarity, the model and the original can be in more complex geometric relationships than linear proportionality, since the physical properties of the original are not proportional to its geometric dimensions. It is important here that the physical variable space of the model is similar to the physical variable space of the original. In this case, the physical model in relation to the original is an analogy of the type of isomorphism (one-to-one correspondence). The central problem is the problem of correct recalculation of the results of a model experiment for the results of testing the original in real conditions. The similarity is based on meeting some physical criteria.

Ideal(imaginary) models are ideal constructions in our minds in the form of images or ideas about certain physical phenomena, processes, objects, systems (geometric point, infinity, etc.).

Abstract(theoretical, informational) models - models representing objects of modeling in figurative or symbolic form.

Examples of abstract models are some hypothesis 1 about the properties of matter, assumptions about the behavior of a complex system under uncertainty, or a new theory about the structure of complex systems.

On abstract models and on speculative analogy (similarity) between the model Mand original Sabstract (theoretical) modeling is being built.

A prominent representative of abstract and symbolic modeling is the mathematical model.

Mathematical model– it is a set of mathematical formulas, equations, relations that describe the properties of the object of modeling that are of interest to the researcher.

To investigate each aspect of modeling (type, structure, behavior) or their combination, appropriate models can be used: appearance models, structure models, behavior patterns.

Appearance modelmost often comes down to a listing of the external features of the modeling object and is intended for identification (recognition) of the object.

Structure modelis a list of the constituent elements of the modeling object with an indication of the links between these elements and is intended for visual display, study of properties, identification of significant connections, study of the stability of the object of modeling

Behavior modelis a description of changes in the appearance and structure of a modeling object over time and as a result of interaction with other objects. The purpose of the behavior models is to predict the future states of the modeling object, manage objects, establish links with other objects external to the modeling object.

Objectively, the levels of our perceptions, the levels of our knowledge about various phenomena, processes, systems are different. This is reflected in the ways of representing the phenomena under consideration.

TO informal models can include displays (images) obtained using various forms of thinking: emotions, intuition, figurative thinking, subconsciousness, heuristics as a set of logical techniques and rules for finding the truth. In non-formalized modeling, the model is not formulated, but instead some fuzzy mental reflection (image) of reality is used, which serves as the basis for decision-making.

An example of vague (intuitive) ideas about an object is a fuzzy description of a situation based on experience and intuition.

TO formalized models include figurative models, when the models are built from any visual elements (elastic balls, fluid flows, trajectories of bodies, etc.).

Formalizable abstract models include sign models, including mathematical constructions, programming languages, natural languages, together with the rules for their transformation and interpretation.

By their purpose, the models are designed to solve many problems:

research(descriptor, cognitive, conceptual, formal) models are designed to generate knowledge by studying the properties of an object;

educationalmodels are designed to transfer knowledge about the studied object;

workers(optimization, management) models are designed to generate the right actions in the process of achieving the goal.

TO research models include semi-natural stands, physical models, mathematical models. Note that research models can act as training models if they are intended to transfer knowledge about the properties of an object. Examples of working models are: a robot; autopilot; a mathematical model of an object built into a control or monitoring system; artificial heart, etc. At the same time, research and teaching models should be close to reality, and working models should reflect this reality. There is no clear line between these models. For example, a research model that adequately reflects the properties of an object can be used as a working one.

Research models are carriers of new knowledge, teaching models combine old knowledge with new.

Working models idealize accumulated knowledge in the form of ideal actions to perform certain functions that would be desirable to perform.

Descriptor models- descriptive models are designed to establish the laws of change in the parameters of these processes and are implementations of descriptive and explanatory meaningful models at the formal level of modeling

As an example of such a model, we can cite the model of the motion of a material point under the action of applied forces, using Newton's second law. By specifying the position and speed of the point at the initial moment of time (input values), the mass of the point (model parameter) and the law of variation of the applied forces (external influences), it is possible to determine the speed and coordinates of the point at any subsequent moment in time (output values).

Cognitive(mental, cognitive) model - models, which are a kind of mental image of an object, its ideal model in the head of the researcher, obtained as a result of observing the original object.

Forming such a model, the researcher, as a rule, seeks to answer specific questions, therefore, everything unnecessary is cut off from the infinitely complex structure of the object in order to obtain its more compact and laconic description.

Cognitive models are subjective, as they are formed speculatively based on all the previous knowledge and experience of the researcher. One can get an idea of \u200b\u200bthe cognitive model only by describing it in a sign form. The representation of a cognitive model in natural language is called meaningful model .

Cognitive and content models are not equivalent, since the former may contain elements that the researcher cannot or does not want to formulate.

Conceptual modelit is customary to call a meaningful model, in the formulation of which the concepts and representations of subject areas of knowledge are used that study the object of modeling.

In a broader sense, a conceptual model is understood as a meaningful model based on a certain concept or point of view.

Formal modelis a representation of a conceptual model using one or more formal languages \u200b\u200b(for example, mathematical theory languages, universal modeling language, or algorithmic languages).

In the humanities, the modeling process in many cases ends with the creation of a conceptual model of the object.

In natural science and engineering disciplines, as a rule, it is possible to build a formal model.

Thus, cognitive, content and formal models make up three interrelated levels of modeling.

Optimization models- models designed to determine the optimal (best) parameters of the simulated object from the point of view of some criterion, or to search for the optimal (best) control mode for a certain process.

As a rule, such models are built using one or more descriptive models and include some criterion that allows you to compare different options for sets of values \u200b\u200bof output quantities with each other in order to choose the best one. Restrictions in the form of equalities and inequalities related to the features of the object or process under consideration can be imposed on the range of values \u200b\u200bof the input parameters.

An example of an optimization model is modeling the process of launching a rocket from the Earth's surface in order to lift it to a given height in minimaltime under restrictions on the magnitude of the impulse of the engine, the time of its operation, the initial and final mass of the rocket. In this case, the mathematical relations of the descriptive model of the rocket motion appear in the form of constraints such as equalities.

Note that for most real processes, structures, it is required to determine the optimal parameters at once according to several criteria, i.e. we are dealing with the so-called multicriteria optimization problems.

Management models- models used to make effective management decisions in various areas of purposeful human activity.

In general, decision-making is a process comparable in complexity to the process of thinking in general. However, in practice, decision-making is usually understood as the choice of some alternatives from a given set of them, and the general decision-making process is presented as a sequence of such choices of alternatives.

Unlike optimization models, where the selection criterion is considered definite and the desired solution is established from the conditions of its extremality, in management models it is necessary to introduce specific optimality criteria that allow comparing alternatives with various uncertainties of the problem. The type of the optimality criterion is not fixed in advance in management models. This is the main feature of these models.

Registration modelsare models designed to register properties and qualities of interest to the researcher that are not available for direct registration at the modeling object.

When solving problems of managing complex dynamic objects, reference and prognostic models are used, which are a formalized display of the desired characteristics of a controlled object for the purposes of current or future object management.

Reference modelIs a model that describes, in one form or another, the desired (idealized) properties of the object of modeling (control).

Predictive models- models designed to determine futurestates ( the futurebehavior) of the modeling object.

Simulation modelsIs a set of descriptions of system elements, interrelationships of elements with each other, external influences, algorithms for the functioning of the system (or rules for changing states) under the influence of external and internal disturbances.

Simulation models are created and used when the creation of a unified model of a complex system is impossible or involves very great difficulties, the available mathematical methods do not allow obtaining satisfactory analytical or numerical solutions of the problems under consideration. But the presence of descriptions of elements and algorithms of functioning allows simulating the process of functioning of the system and measurementscharacteristics of interest.

It can also be noted that simulation models can be created for a much wider class of objects and processes than analytical and numerical models. In addition, since for implementation, as a rule, computing means (computers and other means) are used, universal or special algorithmic languages \u200b\u200bserve as means of formalized description of simulation models.

Simulation modeling when studying large (complex) systems

remains practically the only available method of obtaining information about the behavior of a system under conditions of uncertainty, which is especially important at the stage of its design. Using this method, one can select the structure, parameters and control algorithms of the synthesized system, evaluate their efficiency, and also simulate the behavior of the system under conditions that cannot be reproduced on a real prototype (for example, accidents, failures, emergencies, etc.). When the behavior of the system under the action of random factors is studied in imitation modeling with subsequent statistical processing of information, it is advisable to use the method of static modeling as a method of machine implementation of the simulation model. In this case, the statistical test method (Monte Carlo method) is considered as a numerical method for solving analytical problems.

A special class of models are cyberneticmodels that reflect the management aspects of the behavior of complex systems based on information exchange between its elements. The very physical nature of cybernetic models differs from the physical nature of the prototype and its elements. A feature of cybernetic models is the possible presence in them, in addition to the control mechanism, also mechanisms of self-organization, learning, adaptation, etc., and in more complex systems - and artificial intelligence.

Taking into account the time factor in modeling leads to the use of static and dynamic models.

Static modelsreflect the steady-state (equilibrium) operating modes of the system;

Static modes of operation of elements, objects, systems are reflected in their static characteristics (linear, nonlinear) and are described by the corresponding algebraic functional dependencies.

Dynamic modelsreflect unsteady (nonequilibrium, transient) modes of system operation.

Differential equations or systems of differential equations are most often used to describe non-equilibrium (transient) modes of system operation.

Let us consider some of the properties of models that allow, to one degree or another, either to distinguish or to identify a model with an original (object, process). It is customary to distinguish the following properties of models: adequacy, complexity, finiteness, truth, proximity.

Adequacy.Under adequacyit is customary for models to understand the correct qualitative and quantitative description of an object (process) for a selected set of characteristics with some reasonable degree of accuracy.

Adequacy is the most important requirement for a model; it requires the model to correspond to its real object (process, system, etc.) with respect to the selected set of its properties and characteristics. In this case, we mean the adequacy not in general, but the adequacy for those properties of the model that are essential for the researcher. Full adequacy means the identity between the model and the prototype.

A mathematical model can be adequate with respect to one class of situations (state of the system + state of the external environment) and not adequate with respect to another. The use of an inadequate model can lead either to a significant distortion of the real process or properties (characteristics) of the studied object, or to the study of nonexistent phenomena, properties and characteristics.

You can introduce the concept of the degree of adequacy, which will vary from 0 (lack of adequacy) to 1 (full adequacy). The degree of adequacy characterizes the proportion of the truth of the model relative to the selected characteristic (property) of the object under study. Note that in some simple situations, the numerical assessment of the degree of adequacy is not particularly difficult. The difficulty in assessing the degree of adequacy in the general case arises from the ambiguity and vagueness of the criteria of adequacy themselves, as well as because of the difficulty of choosing those signs, properties and characteristics by which the adequacy is assessed.

The concept of adequacy is a rational concept, therefore, increasing its degree should also be carried out at a rational level. The adequacy of the model should be checked, monitored, refined constantly in the process of research on particular examples, analogies, experiments, etc. As a result of the adequacy check, it is found out what the assumptions made lead to: either to an acceptable loss of accuracy, or to a loss of quality. When checking the adequacy, it is also possible to justify the legitimacy of the application of the accepted working hypotheses in solving the problem or problem under consideration.

Simplicity and complexity.The simultaneous demand for simplicity and adequacy of the model is controversial. In terms of adequacy, complex models are preferable to simple ones. In complex models, you can take into account a larger number of factors that affect the studied characteristics of objects. Although complex models more accurately reflect the simulated properties of the original, they are more cumbersome, difficult to visualize and inconvenient to use. Therefore, the researcher seeks to simplify the model, since it is easier to operate with simple models. When striving to build a simple model, the basic model simplification principle:

you can simplify the model as long as the basic properties, characteristics and patterns inherent in the original are preserved.

This principle indicates the limit of simplification.

Moreover, the concept of simplicity (or complexity) of a model is a relative concept. A model is considered simple enough if modern research tools (mathematical, informational, physical) make it possible to carry out qualitative and quantitative analysis with the required accuracy. And since the capabilities of research tools are constantly growing, those tasks that were previously considered difficult can now be classified as simple.

A more difficult task is to ensure the simplicity / complexity of the model of a complex system consisting of separate subsystems connected to each other in some hierarchical and multi-connected structure. Moreover, each subsystem and each level have their own local criteria of complexity and adequacy, different from the global criteria of the system.

In order to reduce the loss of adequacy, it is more expedient to simplify models:

1) at the physical level while maintaining the basic physical relationships,

2) at the structural level while maintaining the basic systemic properties.

Simplification of the models at the mathematical level can lead to a significant loss of the degree of adequacy. For example, truncation of a high-order characteristic equation to the 2nd - 3rd order can lead to completely incorrect conclusions about the dynamic properties of the system.

Note that simpler models are used to solve the synthesis problem, and more complex exact models are used to solve the analysis problem.

Finite models.It is known that the world is infinite, like any object, not only in space and time, but also in its structure (structure), properties, relations with other objects. Infinity manifests itself in the hierarchical structure of systems of various physical nature. However, when studying an object, the researcher is limited to a finite number of its properties, connections, resources used, etc. He, as it were, “cuts out” from the infinite world some finite fragment in the form of a specific object, system, process, etc. and tries to cognize the infinite world through the finite model of this fragment.

The finiteness of systems models lies, first, in the fact that they reflect the original in a finite number of relations, i.e. with a finite number of connections with other objects, with a finite structure and a finite number of properties at a given level of study, research, description, available resources. Secondly, the fact that the resources (information, financial, energy, time, technical, etc.) of modeling and our knowledge as intellectual resources are finite, and therefore objectively limit the possibilities of modeling and the very process of cognizing the world through models. Therefore, the researcher (with rare exceptions) deals with finite-dimensional models.

The choice of the model dimension (its degrees of freedom, state variables) is closely related to the class of problems to be solved. The increase in model dimension is associated with problems of complexity and adequacy. In this case, it is necessary to know what is the functional relationship between the degree of complexity and the dimension of the model. If this dependence is power-law, then the problem can be solved by using computer systems. If this dependence is exponential, then the “curse of dimension” (R. Kalman 1) is inevitable and it is practically impossible to get rid of it.

As noted above, an increase in the dimension of the model leads to an increase in the degree of adequacy and, at the same time, to a complication of the model. Moreover, the degree of complexity is limited by the ability to operate with the model, i.e. by the means of modeling available to the researcher. The need to move from a rough simple model to a more accurate one is realized by increasing the dimension of the model by attracting new variables that are qualitatively different from the main ones and which were neglected when constructing a rough model. These variables can be classified into one of the following three classes:

1) fast flowingvariables, the extent of which in time or space is so small that, when roughly considered, they were taken into account by their integral or averaged characteristics;

2) slow flowingvariables, the extent of change of which is so great that in rough models they were considered constant;

3) small variables(small parameters), the values \u200b\u200band effects of which on the main characteristics of the system are so small that they were ignored in rough models.

Note that the division of the complex motion of the system in terms of speed into fast and slow motion makes it possible to study them in a rough approximation independently of each other, which simplifies the solution of the original problem. As for small variables, they are usually neglected when solving the synthesis problem, but they try to take into account their influence on the properties of the system when solving the analysis problem.

When modeling, one strives, if possible, to single out a small number of main factors, the influence of which is of the same order and is not too difficult to describe mathematically, and the influence of other factors can be taken into account using averaged, integral or "frozen" characteristics.

Approximation of models.It follows from the above that the finiteness and simplicity (simplification) of the model characterize qualitythe difference (at a structural level) between the original and the model. Then the approximation of the model will characterize quantitativeside of this difference.

You can introduce a quantitative measure of approximation by comparing, for example, a rough model with a more accurate reference (full, ideal) model or with a real model. Model closeness to the original inevitable, exists objectively, since the model as another object reflects only certain properties of the original. Therefore, the degree of approximation (proximity, accuracy) of the model to the original is determined by the statement of the problem, the purpose of modeling.

Excessive striving for increased model accuracy leads to its significant complication, and, consequently, to a decrease in its practical value. Therefore, apparently, the principle of L. Zadeh is true 1 that when modeling complex (human-machine, organizational) systems, accuracy and practical meaning are incompatible and exclude each other. The reason for the inconsistency and incompatibility of the requirements for the accuracy and practicality of the model lies in the uncertainty and vagueness of knowledge about the original itself - its behavior, its properties and characteristics, about the behavior of the environment, about the mechanisms of goal formation, ways and means of achieving it, etc.

The truth of the models.Each model has a grain of truth, i.e. any model in some way correctly reflects the original. The degree of truth of the model is revealed only when it is practically compared with the original, because only

practice is the criterion of truth.

On the one hand, any model contains the unconditionally true, i.e. definitely known and correct. On the other hand, the model also contains the conditionally true, i.e. true only under certain conditions. A typical mistake in modeling is that researchers apply certain models without checking the conditions for their truth, the limits of their applicability. This approach will lead to incorrect results.

Note that any model also contains the supposedly true (plausible), i.e. something that can be either true or false under uncertainty. Only in practice is the actual relationship between true and false in specific conditions established. Thus, when analyzing the truth level of the model, it is necessary to find out:

1) accurate, reliable knowledge;

2) knowledge that is reliable under certain conditions;

3) knowledge assessed with a certain degree of uncertainty;

4) knowledge that cannot be estimated even with a certain degree of uncertainty;

5) ignorance, i.e. what is unknown.

Thus, the assessment of the truth of the model as a form of knowledge is reduced to identifying the content in it of both objective reliable knowledge that correctly reflects the original, and knowledge that roughly estimates the original, as well as what constitutes ignorance.

The primary idea of \u200b\u200bthe professiogram of a particular profession is given by its structural content. In the description of the professional professions, including the following sections - general characteristics of the profession, its meaning; description of the labor process, work performed; the requirements of the profession for the individual; working conditions; necessary knowledge; required skills and abilities; where you can get a specialty; economic working conditions.

There is also a professiographic method for studying the personality and activities of a modern teacher.

Professiogram is an ideal model of a teacher, teacher, class teacher, teacher, a sample, a standard, which presents:

The main personality traits that a teacher must have;

Knowledge, abilities, skills to perform the functions of a teacher.

Based on this understanding of the meaning of the concept of "professiogram", we can talk about the professiographic method of studying personality, in which the teacher's knowledge, skills and abilities are compared with those that he might have in accordance with the ideal model. It is not difficult to imagine that this method allows you to design the personal and professional growth of a teacher.

At the same time, the teacher's professiogram is a document that gives a full qualification characteristic of a teacher from the standpoint of the requirements for his knowledge, skills and abilities, for his personality, abilities, psychophysiological capabilities and level of training.

Such an idea of \u200b\u200bthe professiogram was formed in the previous decades. So, we can talk about the professiogram of the class teacher, compiled by N.I.Boldyrev.

N.I.Boldyrev highlighted the priority qualities of the class teacher's personality: ideological, moral and civic maturity, social activity, passion for the profession of an educator, love for children, humane, caring attitude towards them, high demands on oneself and students, communicativeness, friendly disposition, politeness in communication, psychological compatibility with other members of the teaching staff and others necessary for an ideal specialist.

To perform a wide variety of functions, the teacher, according to N.I.Boldyrev, needs the following skills:

establish business relations with the school administration, with parents, the public (communication skills, according to today's ideas, are close to communicative);

information skills and skills;

the ability to vividly, expressively, logically express their thoughts (according to today's ideas - didactic and speech);

the ability to convince, attract to oneself, make one a supporter (according to today's ideas - didactic, communicative).

To implement these skills, it is necessary to create a high emotional attitude, to ensure the business nature of life and work.

N.I.Boldyrev attributed an important role to personality traits, which, in addition to priority ones, would be nice for a teacher (class teacher): tact, endurance, self-control, observation, sincerity, resourcefulness, firmness, consistency in words and actions, accuracy, external neatness ...

It is important for the class teacher to know the basics of the theory and methods of education, to be able to:

work with parents (public); plan educational work;

select, on the basis of diagnostics of collectives (groups), individuals, the necessary activities;

correctly take into account and evaluate the results of education; identify and organize an asset;

monitor and assist in the execution of orders.

To perform complex and diverse functions, it would be good for a teacher to master some applied creative artistic skills:

draw (pictorial);

play musical instruments, sing (musical); read expressively (fiction and literary); dance (choreographic);

go hiking (sports and tourism or sports and labor).

A. S. Makarenko in his introductory speech to the "Book for Parents" wrote: "The ability to educate is still an art, the same art as playing the violin or piano well, painting well, being a good milling machine operator or turner."

If we go from the functional principle, that is, from those actions of the functions that the teacher should perform, then we can enumerate the functions of the teacher. So, one of the first (in 1971) identified eight functions of a teacher in school A.I.Shcherbakov, N.A. Rykov. They own the following classification of teacher functions:

Informational (the teacher broadcasts this or that information);

Developing (develops thinking, imagination, certain skills, speech, etc.);

orienting (orientates in the variety of information, moral values);

mobilization (mobilizes to perform exercises, tasks, affairs);

design (constructs a lesson, extracurricular activities, multi-level tasks, independent work, communication and much more);

communicative (the function of communicating with parents, other teachers, administration, psychologists, valeologists, etc.);

organizational (organizes students, other teachers, parents, himself, and also organizes lessons, extracurricular activities that he conducts);

research (knows how to explore both an individual person, a group of students - a team, and the training and education of students, etc.).

The mention of the latter function, from our point of view, allows us to speak about the functions of not only the teacher, but also the teacher - in the broad sense of the word.

In the textbooks of pedagogy of past years, the authors distinguish the functions of an educator, class teacher:

organizational (organizes all educational influences and interactions in teams, including in the form of educational affairs - excursions, trips, meetings, class hours, questionnaires as research, etc.);

educational (as a result of which the upbringing, formation and development of personality traits inherent in a student as a member of a children's collective, a family man, a citizen of Russia, a citizen of the World, a creative personality and individuality are carried out in different ways and means);

stimulating (as a result of which the stimulating activity of students, children's collective, parents, the public, etc. is carried out);

coordination (as a result of which the activities of both children, when necessary, and teachers working in the same class are coordinated, parallels, and also communication with the outside world can be carried out if the educational institution is considered as an open system;

work with documents (magazines, student diaries, their personal files, various plans).

There are a lot of functions that teachers, educators, class teachers should perform. What knowledge and skills should they have for this? The concept of the professiogram, which we discussed above, gives an idea of \u200b\u200bthe skills and abilities that both teachers and class teachers should possess. However, just the knowledge and skills mentioned earlier are not enough. According to psychologists, a lot depends on the natural prerequisites, the inclinations of the personality (which can develop into certain abilities), on the psychological readiness of the individual, his desire (desire) to perform these functions well. Much is brought up, developed only as a result of long-term work on oneself; the main thing in self-education is patience and control over your behavior.

Psychologist V. A. Krutetsky in the textbook "Psychology" offers a structure of professionally significant personality traits and skills that a teacher must have. If we, following V.A.Krutetsky, represent the professionally significant qualities of a teacher's personality as a set of four blocks (parts or substructures) (1. Worldview of a person; 2. Positive attitude to pedagogical activity; 3. Pedagogical abilities; 4. Professional pedagogical knowledge, abilities and skills), then we will get a fairly holistic idea of \u200b\u200bthe requirements that apply to the teaching profession and other pedagogical professions.

Let us consider these blocks of professionally significant qualities of a teacher's personality in more detail.

1st block. Humanistic worldview (we are talking about those convictions, ideals that are inherent in the teacher-educator; educate only the one who was brought up himself; it is desirable that the educator has a high level of general culture and high moral character, and most importantly - would love other people).

2nd block. A positive attitude towards pedagogical activity (we are talking about the pedagogical orientation of the individual, pedagogical inclinations as a stable desire and desire to devote oneself to pedagogical activity; one who is indifferent to his work cannot be a good teacher; children unmistakably identify those teachers who do not like them or do not like teaching in general).

3rd block. Pedagogical abilities (based on natural prerequisites, under certain conditions, they are realized - or not - in professional pedagogical knowledge, skills, skills, in other words, pedagogical abilities) is a generalized set of individual psychological characteristics and professionally significant personality traits that correspond to requirements of pedagogical activity, ensure the achievement of high results in it, determine the success of the teacher as a whole in mastering this activity (for more details see Chapter 1).

4th block. Professional pedagogical knowledge, abilities, skills (we are talking about knowledge in the field of the taught subject and teaching technology).

V. A. Sukhomlinsky mentions four signs of pedagogical culture. Briefly, his thoughts can be expressed as follows. It is necessary: \u200b\u200b1) that the teacher has academic knowledge so that he can appeal to the mind and heart of the pupil; 2) that the teacher read literature (pedagogical, psychological, journalistic, etc.); 3) so that the teacher knows the richness of methods for studying the child; 4) possessed a speech culture.

So, experts believe that those who have good prerequisites for becoming a teacher.

The proposed BPM (Business Process Management) reference model is based on the following chain of prerequisites:

Increasing the productivity of an enterprise as a complex system requires its rational construction, and process management is the most modern concept for such a construction;

BPM (as a discipline) offers a systematic approach to the implementation of process management;

Each process-driven enterprise has its own BPM-system - a portfolio of all business processes, as well as methods and tools to guide the development, execution and development of this portfolio;

The flexibility of an enterprise BPM system is a major factor in its success;

A specialized software platform (BPM suite) for implementing an enterprise BPM system is necessary, but not sufficient, since BPM has a special place in the enterprise architecture.

Goal: increase the productivity of the enterprise

To manage their productivity, most enterprises use the feedback principle (Fig. 1), which allows them to adapt to the external business ecosystem by performing a certain sequence of actions:

Measuring the progress of production and economic activities (usually such measurements are presented in the form of various metrics or indicators, for example, the percentage of returning customers);

Isolation of events important for the enterprise from the external business ecosystem (for example, laws or new market needs);

Determination of the business development strategy of the enterprise;

Implementation of the decisions taken (by making changes to the business system of the enterprise).

According to the classic recommendation of Edward Deming, the author of numerous works in the field of quality management, including the famous book "Overcoming the Crisis", all improvements should be carried out cyclically, continuously and with verification at each cycle. The extent and frequency of these improvements will depend on the specific situation, but it is recommended that such cycles be kept fairly compact. Various improvements can affect different aspects of the enterprise. The question is, how can a company achieve the best results in each specific case? There are two objective prerequisites for optimizing the activities of an enterprise as a whole:

Providing management with appropriate information and decision-making tools;

Ensuring that an enterprise's business system is capable of making the necessary changes at the required pace.

The most modern concept of organizing the work of an enterprise is process management, in which processes and services become explicit.

Process management

The business world has long understood (see techniques such as TQM, BPR, Six Sigma, Lean, ISO 9000, etc.) that services and processes are the basis for the functioning of most enterprises. Many enterprises use process management to organize their production and economic activities, as a portfolio of business processes and methods of managing them.

Process management, as a management concept, postulates the expediency of coordinating the activities of individual services of an enterprise in order to obtain a certain result using explicitly and formally defined business processes. Moreover, services are operationally independent functional units; an enterprise can have many elementary nanoscale services, which are organized into a mega-service (the enterprise itself).

Using an explicit coordination definition allows you to formalize the interdependencies between services. The presence of such formalization makes it possible to use various methods (modeling, automated checkout, version control, automated execution, etc.) to improve business understanding (for making better decisions) and increase the speed of development of business systems (for faster implementation of changes ).

In addition to processes and services, enterprise business systems work with events, rules, data, performance indicators, roles, documents, etc.

To implement process management, enterprises use three popular disciplines for continuous improvement of business processes: ISO 9000, Six Sigma, and "lean" or "lean" production (Lean production). They affect various areas of the business system of the enterprise, however, it is always provided for the collection of data on the actual work done and the use of a certain model of business processes for decision-making (although sometimes this model is only in someone's head). At the same time, they offer different and complementary methods for determining exactly what changes are needed to improve the functioning of an enterprise's business system.

What you model is what you do

In fig. 2 shows a generalized model of a process-controlled enterprise.

What is the main difficulty in optimizing the activities of such an enterprise? Different parts of a business system use different descriptions of the same business process. Usually these descriptions exist separately and are developed by different people, are updated at different rates, do not exchange information, and some of them are simply not explicitly. The presence of a unified description of the business processes of the enterprise makes it possible to eliminate this drawback. This description must be explicitly and formally defined in order to simultaneously serve as a model for modeling, an executable program and documentation that can be easily understood by all employees involved in the business process.

This description is the foundation of the BPM discipline that allows you to model, automate, execute, control, measure, and optimize workflows that span software systems, employees, customers, and partners within and outside the enterprise. The BPM discipline considers all operations with business processes (modeling, execution, etc.) as a whole (Fig. 3).

At the moment, the BPM industry has not yet developed a proper system of standards for the formats of formal description of business processes. The three most popular formats: BPMN (Business Process Modeling Notation, graphical representation of business process models), BPEL ( Business Process Execution Language , formalizing the execution of interaction between Web services) and XPDL (XML Process Description Language, www.wfmc.org, a specification for exchanging business process models between different applications) have been developed by different groups and for different purposes and, unfortunately, do not adequately complement each other.

The situation is aggravated by the fact that different manufacturers are behind different formats and each is trying to "push" his solution to the market. As it has been repeated many times, in such a struggle, the interests of the end user are little taken into account - today there is no sufficiently powerful organization representing the interests of the end user of BPM (by analogy with the group of standards for HTML, the success of which is explained by the adoption of a single test ACID3 by all developers of Web browsers for comparison their products). The ideal situation in BPM would be a standard definition of execution semantics for a BPMN-like description of business processes. It is the standard execution semantics that would guarantee the same interpretation of business processes by any software. Additionally, such a description should allow adaptation of the degree of description of business processes for the needs of a particular consumer (for example, a user sees a rough diagram, an analyst - a more detailed one, etc.).

All this does not mean that BPEL or XPDL will become unnecessary - their use will be hidden, as is the case in the field of preparation of electronic documents. The same electronic document can simultaneously exist in XML, PDF, PostScript, etc., but only one basic format (XML) is used to modify the document.

BPM Discipline in Enterprise Culture

In addition to processes and services, enterprise business systems work with such additional artifacts as:

developments(events) - phenomena that have occurred within and outside the boundaries of the enterprise, to which a certain reaction of the business system is possible, for example, when receiving an order from a client, it is necessary to start a service business process;

objects (data and documents objects) - formal informational descriptions of real things and people forming a business; this is information at the input and output of a business process, for example, the business process of servicing an order receives at the input the actual order form and information about the client, and at the output generates a report on the execution of the order;

activities(activities) - small work that transforms objects, for example, automatic activities such as checking a customer's credit card or human activities such as endorsement of a document;

regulations (rules) - restrictions and conditions under which the enterprise operates, for example, the issuance of a loan for a certain amount must be approved by the general director of the bank;

role (roles) - Concepts that represent the relevant skills or responsibilities required to perform certain actions, for example, only a senior manager can sign a specific document;

audit trails (audit trails) - information about the execution of a specific business process, for example, who did what and with what result;

key performance indicators (Key Performance Indicator, KPI) - a limited number of indicators that measure the degree of achievement of the set goals.

Figure: 4 illustrates the distribution of artifacts between different parts of an enterprise business system. The expression "processes (as templates)" means abstract descriptions (models or plans) of processes;

the expression "processes (as instances)" refers to the actual results of executing these templates. Typically, a template is used to create many instances (like a blank that is copied many times for different people to fill out). The expression "services (as interfaces)" refers to the formal descriptions of the services that are available to their consumers; the expression "services (as programs)" refers to the means of executing services — such means are provided by service providers.

To successfully work with the entire complex set of interdependent artifacts, any process-driven enterprise has its own BPM system - this is a portfolio of all business processes of the enterprise, as well as methods and tools for guiding the development, execution and development of this portfolio. In other words, an enterprise BPM system is responsible for the synergistic functioning of various parts of an enterprise business system.

BPM-system, as a rule, is not ideal (for example, some processes can exist only on paper, and some details "live" only in the minds of certain people), but it does exist. For example, any implementation of ISO 9000 can be considered an example of a BPM system.

Improving the BPM system of an enterprise, in addition to purely technical aspects, must take into account socio-technical issues. An enterprise BPM system has many stakeholders, each of whom solves their own problems, perceives the BPM discipline in their own way, and works with their artifacts. For the successful development of an enterprise BPM system, it is necessary to pay special attention to the problems of all stakeholders and explain to them in advance how improving the enterprise BPM system will change their work for the better. It is imperative to achieve a common understanding of all artifacts among all stakeholders.

Specialized software for the implementation of BPM systems

The growing popularity and great potential of BPM have led to the emergence of a new class of enterprise software - BPM suite, or BPMS, containing the following typical components (Fig. 5):

Process modeling tool - a graphical program for manipulating artifacts such as events, rules, processes, activities, services, etc .;

Testing tool (Process testing tool) - a functional testing environment that allows you to "execute" a process in various scenarios;

Repository of templates (Process template repository) - a database of templates of business processes with support for different versions of the same template;

Process execution engine;

Repository of instances (Process instance repository) - a database for running and already completed instances of business processes;

Work list - the interface between the BPM suite and the user performing some activities within one or more business processes;

Dashboard - an interface for operational control over the execution of business processes;

Process analysis tool - an environment for studying the trend in the execution of business processes;

The Process simulation tool is an environment for testing the performance of business processes.

The need for interoperability between the BPM suite and enterprise software that supports other artifacts has given rise to a new class of enterprise software - the Business Process Platform (BPP). Typical BPP technologies (Fig. 6):

Business Event Management (BEM) - real-time analysis of business events and launching relevant business processes (BEM is associated with Complex Event Processing (CEP) and Event Driven Architecture (EDA));

Business Rules Management (BRM) - explicit and formal coding of business rules that can be modified by users;

Master Data Management (MDM) - simplification of working with structured data by eliminating chaos when using the same data;

Enterprise Content Management (ECM) - management of corporate information intended for a person (generalization of the concept of a document);

Configuration Management Data Base (CMDB) - a centralized description of the entire information and computing environment of an enterprise, used to link BPM to information and computing resources of an enterprise;

Role-Based Access Control (RBAC) - control access to information in order to effectively separate control and executive powers (separation of duty);

Business Activity Monitoring (BAM) - operational control of the functioning of the enterprise;

Business Intelligence (BI) - analysis of the characteristics and trends of the enterprise;

Service-Oriented Architecture (SOA) is an architectural style for building complex software systems as a set of universally accessible and interdependent services that is used to implement, execute and manage services;

Enterprise Service Bus (ESB) is a communication environment between services within an SOA.

Thus, the BPM discipline is able to provide a uniform, formal and executable description of business processes that can be used in various tools of the BPM suite, with real data collected during the execution of business processes. At the same time, the high flexibility of an enterprise BPM system is not automatically guaranteed after purchasing a BPM suite or BPP - the ability of a particular BPM system to develop at the required pace must be designed, implemented and constantly monitored. Like human health, none of this can be bought.

BPM in enterprise architecture

The need to involve almost all corporate software in a single logic for improving an enterprise BPM system raises the question of the role and place of BPM in the enterprise architecture (Enterprise Architecture, EA). EA is the established practice of IT departments today to streamline the information and computing environment of the enterprise. EA is based on the following rules:

The current situation with the information and computing environment of the enterprise is carefully documented as an as-is starting point;

The desired situation is documented as an endpoint to-be;

A long-term plan for transferring the information and computing environment of an enterprise from one point to another is being built and implemented.

All of this would seem to be quite reasonable, but you can immediately see the difference with the approach of small improvements that underlies process management. How can these two opposing approaches be reconciled?

The BPM discipline can solve the main problem of EA - to give an objective assessment of the production and economic capabilities (and not just information and computing) of what will be at the point to-be. Despite the fact that EA describes the full range of artifacts of an enterprise (its genotype), it cannot reliably say what changes in this genotype affect specific production and economic characteristics of the enterprise, that is, the phenotype of the enterprise (a set of characteristics inherent in an individual at a certain stage of development ).

For its part, the BPM discipline structures the interdependencies between artifacts in the form of explicit and executable models (a business process is an example of interdependencies between artifacts such as events, roles, rules, etc.). The presence of such executable models allows, with a certain degree of reliability, to assess the production and economic characteristics of an enterprise when the genotype of the enterprise changes.

Naturally, the more interdependencies between artifacts are modeled and the more reliable these models are, the more accurate such estimates are. Potentially, the symbiosis of the nomenclature of enterprise artifacts and the formally defined interdependencies between them gives an executable model of the enterprise at a specific point in time. If such executable models are built on common principles (for example, krislawrence.com), then it becomes possible to compare the effect of applying different enterprise development strategies and the emergence of more systematic and predictable technologies for transforming some executable models into others.

In a sense, the EA + BPM combination can become a kind of navigator that provides guidance and practical assistance in business and IT development while implementing the general line of the enterprise.

It's no secret that software vendors today define and develop BPM in different ways. However, the more promising path for BPM development is end-user BPM, and the BPM reference model is the first step in creating a common understanding of BPM among all stakeholders.

The reference model proposed in the article is based on the author's practical experience in the design, development and maintenance of various corporate solutions. In particular, this model was used to automate the annual production of more than 3,000 complex electronic products with an average lead time of several years. As a result, maintenance and development of this production system required several times less resources than with the traditional approach. n

Alexander Samarin ([email protected]) - corporate architect of the IT department of the government of the canton of Geneva (Switzerland).

Process Frameworks for BPM

An approach to the implementation of business process management technologies, which simplifies the implementation of BPM systems, implies a clear definition of the business task and the corresponding business processes; implementation of these processes in a period of no more than three months in order to demonstrate the value of this approach; further expansion of the implementation to the main business objectives. However, the main challenge along the way is misunderstanding and lack of alignment between the business and IT departments. Specialized reference models (Process Frameworks) can significantly simplify the implementation project and reduce costs.

Reference model - a package of analytical and software resources, consisting of descriptions and recommendations for organizing a high-level structure of a business process, a set of attributes and metrics for assessing the effectiveness of execution, as well as software modules created for quickly building a prototype of a business process for its subsequent adaptation to the specifics of a particular company.

Reference Models help define and set requirements and enable business processes, they are based on industry standards and include industry expertise. For typical processes, reference models can help you select and model key workflows, define key performance indicators (KPIs) and metrics that measure performance in key areas, as well as manage activities and problem solving, analyze root causes, and handle exceptional cases.

The structure of a typical reference model includes: recommendations and description of the subject area; elements of composite user interfaces (screen forms and portlets logically connected into chains); service wrappers for quick access to business data; examples of typical business rules; key performance indicators and elements for their analysis; executable process models; data models and process attributes; adaptation to the legal framework and the specifics of business in a particular country; recommendations for the stages of deployment and implementation of processes. Such a set of resources will allow you to quickly adapt to the implementation of the process approach within the framework of a specific business process management system, reduce the iteration time of the development cycle, test execution and process analysis. At the same time, the maximum correspondence between the technical implementation and the existing business task is achieved.

However, as analysts at AMR Research point out, "technologies and methods by themselves are not capable of providing any benefits -" more "does not always mean" better. " Some companies use many different solutions, but this only decreases the efficiency. Literacy in the use of such technologies is important ”. The Reference Models are based on industry-accepted standards and Software AG's experience in creating a reference model for defining customer requirements. In practice, this model becomes a starting point from which customers can create the desired model.

The Process Framework, for example, for the order processing business process, includes a basic process model with diagrams of actions for various users and roles, selected KPIs from the SCOR (The Supply-Chain Operations Reference-model) model for the whole process and individual stages. rules to support different processing sequences, for example based on customer segment, targets for different customer segments, product types and regions, and dashboards to help manage exceptional situations.

The Process Framework allows you to focus on the need and possibility of adjusting KPIs for specific customer groups and configuring them taking into account the emergence of new products, entry into new regions or market segments. Such information will enable supply chain, trade, logistics and manufacturing managers to better control specific activities, and IT managers to quickly assess the real health of the IT systems that support order processing.

Vladimir Alentsev ([email protected]) - consultant for BPM and SOA, representation Software AG in Russia CIS (Moscow).

The ideal point model assumes the comparison of a specific product or other object with a certain standard as a difference. In accordance with the model, each feature is normalized as a distance from the ideal or reference value of the feature. For the application of the model, first of all, an idea of \u200b\u200ban ideal product from the point of view of consumers is formed - an "ideal" point X0 is introduced.

The model characterizes the degree of closeness of a specific product to the "ideal" in accordance with the dependence

where TO i – weighting factors; X 0i – coordinates of the ideal point. Exponent t is chosen by the researcher and, as a rule, takes values \u200b\u200bat level 1 or 2. The summation is carried out over p product properties. Low values \u200b\u200bare best W, since if the ideal point is the best, then it is obvious that the minimum distance from it is desirable.

The choice of the ideal point is quite difficult and ambiguous. The reader should pay attention to the following possible approaches to choosing the ideal point.

• 1. The best scores in terms of severity: "all fives". If we consider such a consumer feature as the convenience of controlling complex equipment, for example, a car or a music center, then the coordinates of the ideal point will correspond to the border of the selected scale. However, the corresponding hypothetical "best in all respects" product will be far from reality, since the best product in all respects does not always exist. In particular, it is difficult to combine the properties of a limousine and an SUV in one car. If a better product does exist, the price will be prohibitively high.
• 2. Application of parameters of the real most competitive or "best on the market" product according to the principle: "girl of my dreams" or "real man". The peculiarity of this approach is that deviations from the ideal point in any direction, even in the direction of formal improvement, are considered undesirable.
• 3. Application of such objective properties when there is an optimal level of property. In this case, the ideal levels will not necessarily be either the highest or the lowest. In such a situation, the use of a model with an ideal exact is most justified. Examples of parameters with optimum: TV screen size for a car or kitchen, TV picture brightness. A good example of having an optimal level is room illumination when “too bright” and “too dark” are equally undesirable. A comment should be made about the need to specify the purpose of the product. So, if you do not indicate that the TV is intended for the kitchen, then you may want to consider the largest TV on sale as ideal.
• 4. Best properties for a given price. The following approach is proposed. In order not to put "all fives", which in principle is not required, and it is unrealistic for the price, it is necessary to have a regression model of the dependence of the price on the levels of properties, which corresponds to parametric pricing. Then the expert can choose a set of properties at each price level available to him. And this is real, since the "mobile shouldn't cost more than ten thousand" approach is used by many.

Obviously, to apply a model with an ideal point, the dimensions of all coordinates must be the same in order to be able to sum the corresponding values \u200b\u200bin the formula. One way out of the problem is the use of dimensionless point estimates. Another method, which is also considered below, consists in normalization, when the actual levels are divided into reference or normative ones, which can also be the coordinates of the ideal point.

Model with normalized factor levels

The use of models with relative factors makes it possible to combine factors with different dimensions in one model. The corresponding model is as follows:

(16.2)

All designations correspond to those entered in the formula (16.1); Zi - parametric indices.

The model is widely used in calculating product quality indices and, especially, in assessing competitiveness. When calculating quality indices X i0 - normative levels of expressiveness of the properties of the goods set by the standards and technical conditions. As a rule, model (16.2) is applied while considering the objective (production and operational) properties of the product, such as speed, power, size, reliability, etc., although it is possible to consider objective properties.

When assessing competitiveness X i0 – parameters of the compared product, which may be the product of the strongest competitor. In the literature on competitive analysis, there are various names for the indicator - composite parametric index of consumer properties, group indicator of competitiveness.

Methodological materials on organizational, methodological, psychological and pedagogical support for professional growth, self-realization of teachers and the formation of key competencies, a profile of the competencies of a teacher were developed by the regional scientific and methodological center for expert evaluation of pedagogical activities of the State Budgetary Educational Institution of Higher Professional Education MO "Academy of Social Management"

The text is provided for your reference.
The reference model of the competence of a pedagogical worker, developed in the regional scientific and methodological center for expert assessment of pedagogical activity, due to its characteristics, is a normative, predictive model aimed at the result, therefore it underlies the control and measuring materials used in certification, defining their goals, objectives and content ...

We present a reference model of the competence of a teacher in a graphical and descriptive form.

Picture 1- Reference model of key competencies of a teacher

A reference model for the competence of a teacher (Figure 1) is an ideal, verbalized, that is, encoded with signs of a natural language, a teacher's model, which is an ideal image, a standard of a specialist that meets all the requirements for teaching staff during certification for the first and highest qualification categories, pp. 30, 31 of the Procedure for attestation of pedagogical workers of state and municipal educational institutions, the requirements set out in the unified qualification reference book of positions of managers, specialists and employees (annex to the order of the Ministry of Health and Social Development of the Russian Federation of August 26, 2010 No. 761 n), and professional standards.

When designing a reference model of a teacher's competence, we relied on the author's developments, various scientific schools, in particular, we used the domestic research of I.A. Winter, N.V. Kuzmina, A.K. Markova, and foreign studies of the Council of Europe.

Key competence we consider it as an integral characteristic of a pedagogical worker, which allows him to freely navigate in the social and professional space, to perform professional activities efficiently and effectively, to solve standard and non-standard professional and pedagogical tasks, to be a socially adapted person, capable of constant personal and professional self-development.

The scope of competence is competency profiles as components of its knowledge, skills and attitudes, meaningfully determining competence.

Picture 2 - Special and professional competence

Special and professional competence (Figure 2), that is, possession of their own professional activities at a sufficiently high level, the ability to project their further professional development.

understanding the purpose, mission of the profession;

possession of the norms of professional activity, high efficiency;

achieving high results and their stability; professional skill;

professional consciousness (awareness of the maximum number of signs of professional activity: content, means, labor results);

professional thinking, professional intuition, independence in solving professional problems;

optimal psychological cost of the result, no fatigue and overload.

Within the special and professional competence highlights the following competency profiles :

1. Subject competence , that is, the depth, consistency of knowledge on the subject and their application in teaching practice; the ability to implement curricula of basic and elective courses in various educational institutions.

2. Organizational and methodological competence , i.e. readiness to apply modern educational methods and technologies, including informational ones, to ensure the quality of the educational process; activities, actions, techniques, skills, methods of work, techniques used in this profession to successfully achieve a result; ability to organize educational activities of students (pupils).

3. Diagnostic competence , i.e. possession of psychological and pedagogical knowledge, psychological and pedagogical actions, methods, techniques, skills, techniques, technologies; the ability to apply modern methods of diagnosing the achievements of students and pupils; carry out pedagogical support of the processes of socialization and professional self-determination of students, preparing them for a conscious choice of profession.

4. Analytical and evaluative competence , i.e. the ability to analyze and evaluate the formation of universal educational actions, mental operations of students, taking into account their individual characteristics and capabilities, both in qualitative and quantitative indicators (points in the rating, category, etc.); apply methods of mathematical and statistical processing of information; participate in professional tests, the result of which is a differentiated (qualitative and quantitative) assessment of professionalism.

5. Predictive competence , i.e., the ability to determine growth prospects, zones of proximal development of their students and their professional development; to be aware of the potential opportunities of schoolchildren and their own; awareness of development prospects and opportunities for their implementation (prognostic criteria); self-design, self-experimentation; building your own strategy for professional growth, building and implementing a scenario for your professional life; consistency between the motivational and operational side of the activity.

6. Research competence , i.e., the ability to apply the methods of theoretical and experimental research; plan, organize, conduct and analyze a pedagogical experiment on the implementation of innovations; ability to analyze and synthesize; research skills; the ability to generate new ideas (creativity); demonstrate an understanding of the quality of research relevant to the discipline; demonstrate an understanding of experimental verification of scientific theories.

Figure 3 -

Communicative competence (Figure 3) - competence of social interaction as the ability to adequately establish mutual understanding, avoid conflicts, create a climate of trust; referring oneself to a professional community; possession of the norms of professional communication, ethical norms of the profession; orientation of professional results for the benefit of other people, their spiritual enrichment with the means of their profession; the ability to cooperate, make contacts, easy compatibility; competitiveness, the ability to arouse interest in society in the results of their professional activities.

Communicative competence manifests itself in the followingcompetency profiles :

1. Social and communicative competence , that is, the ability to find verbal and non-verbal means and ways of forming and formulating a thought during its generation and perception, adequate to situations of interaction; the ability to use public speaking skills, including in the field of broadcasting their own experience (the ability to broadcast their own positive experience to the pedagogical community: articles, speeches, participation in competitions; ability to conduct discussions, polemics; willingness to interact with colleagues).

2. Organizational and communicative competence , that is, the ability to organize productive communication and cooperation of schoolchildren; the ability to conduct educational activities in the form of dialogues, polemics, disputes, discussions, exchange of views, scientific disputes, etc.

Figure 4 - Information competence

Information competence (Figure 4) is related to information technology ownership:

• receiving, processing, issuing information; transformation of information (reading, note-taking);
• mass media, multimedia technologies, computer literacy;
• possession of electronic, Internet technology.

Information competence is manifested in the following profiles:

1. Information retrieval competence , that is, the ability to find the necessary information from various sources.

2. Information and analytical competence , that is, the skills to analyze information and manage it; readiness to use the basic methods, methods and means of obtaining, storing, processing information; willingness to work with a computer as a means of information management; ability to work with information in global computer networks.

3. Information technology competence , that is, the ability to use, reproduce, improve the means and methods of obtaining and reproducing information in printed and electronic form; knowledge of basic applied programs and the ability to use them; Computer skills.

Figure 5 - Personal competence

Personal competence, i.e., stable professional motivation, the presence of a positive self-concept, creative attitude, conscious professional creativity, changing oneself by means of the profession; individuality in professional work; openness to continuous professional training, accumulation of experience, change; possession of the techniques of self-realization and development of individuality within the framework of the profession, readiness for professional growth, the ability for individual self-preservation; self-development of professional abilities; strong goal-setting; professional learning; reliance on past professional experience, continuity; an increase in individualization and relative autonomy with professional growth.

Profiles personal competence:

1. Self-development and self-expression competence - sustainable motivation, ability to set goals, professional abilities, professional learning, self-presentation, positive emotions; ability and readiness for education throughout life, mastery of methods of personal self-expression and self-development, means of resisting professional deformations of the individual.

2. Reflexive competence - a system-forming component of professional pedagogical activity and the quality of a person, which allows reflection to be carried out most effectively and adequately, which ensures development and self-development, promotes a creative approach in educational and professional activities, achieving their maximum efficiency and effectiveness; acmeological phenomenon contributing to the achievement of the highest results in activities; professional and personal qualities of a teacher, his readiness and ability for reflective activity using knowledge, skills, skills, professional and life experience; the ability for introspection and self-esteem.