Computer teaching aids are divided into:
computer textbooks;
domain-specific environments;
laboratory workshops;
simulators;
knowledge control systems;
directories and databases for educational purposes;
tool systems;
expert learning systems.
Automated learning systems (ATS) - complexes of software and hardware and educational and methodological tools that provide active learning activities. AES provides not only teaching specific knowledge, but also checking the answers of students, the possibility of hints, the amusement of the material being studied, etc.
AES are complex human-machine systems that combine into one whole series of disciplines: didactics (goals, content, patterns and principles of education are scientifically substantiated); psychology (taking into account the characteristics of the character and mental warehouse of the student); modeling, computer graphics, etc.
The main means of interaction between the student and the ATS is dialog. The dialogue with the learning system can be controlled by both the learner and the system. In the first case, the student himself determines the mode of his work with AOS, choosing a method of studying the material that corresponds to his individual abilities. In the second case, the method and method of studying the material is chosen by the system, presenting to the student, in accordance with the scenario, frames of educational material and questions to them. The student enters his answers into the system, which interprets their meaning for himself and displays a message about the nature of the answer. Depending on the degree of correctness of the answer, or on the questions of the student, the system organizes the launch of certain paths of the learning scenario, choosing a learning strategy and adapting to the level of knowledge of the student.
Expert learning systems (ETS). They implement learning functions and contain knowledge from a certain rather narrow subject area. ETS have the ability to explain the strategy and tactics of solving the problem of the studied subject area and provide control of the level of knowledge, skills and abilities with the diagnosis of errors based on the results of training.
Training databases (UBD) and training knowledge bases (UBZ), focused on a certain subject area. UBD allow you to form data sets for a given educational task and to select, sort, analyze and process the information contained in these sets. UBZ, as a rule, contains a description of the basic concepts of the subject area, strategy and tactics for solving problems; a set of proposed exercises, examples and tasks of the subject area, as well as a list of possible student errors and information for correcting them; a database containing a list of teaching methods and organizational forms of education.
Multimedia systems. They allow to implement intensive methods and forms of training, increase the motivation of learning through the use of modern means of processing audiovisual information, increase the level of emotional perception of information, form the ability to implement various forms of independent information processing activities.
Multimedia systems are widely used to study processes of various nature on the basis of their simulation. Here you can visualize the life of elementary particles of the microworld, invisible to the ordinary eye, when studying physics, figuratively and clearly talk about abstract and n-dimensional worlds, clearly explain how this or that algorithm works, etc. The ability to simulate the real process in color and with sound accompaniment raises learning to a qualitatively new level.
Systems<Виртуальная реальность>. They are used in solving constructive-graphic, artistic and other tasks, where it is necessary to develop the ability to create a mental spatial design of an object according to its graphical representation; in the study of stereometry and drawing; in computerized simulators of technological processes, nuclear installations, aviation, sea and land transport, where without such devices it is fundamentally impossible to work out the skills of human interaction with modern super-complex and dangerous mechanisms and phenomena.
Educational computer telecommunication networks. They allow to provide distance learning (DL) - learning at a distance, when the teacher and the student are separated spatially and (or) in time, and the educational process is carried out using telecommunications, mainly on the basis of the Internet. At the same time, many people get the opportunity to improve their education at home (for example, adults burdened with business and family concerns, young people living in rural areas or small towns). A person at any period of his life gains the opportunity to remotely acquire a new profession, improve his skills and broaden his horizons, and practically in any scientific or educational center in the world.
In educational practice, all the main types of computer telecommunications are used: e-mail, electronic bulletin boards, teleconferences and other Internet features. DL also provides for the autonomous use of courses recorded on video discs, CDs, etc. Computer telecommunications provide:
the ability to access various sources of information through the Internet and work with this information;
the possibility of prompt feedback during the dialogue with the teacher or with other participants in the training course;
the possibility of organizing joint telecommunications projects, including international ones, teleconferences, the possibility of exchanging views with any participant in this course, a teacher, consultants, the possibility of requesting information on any issue of interest through teleconferences.
the possibility of implementing methods of remote creativity, such as participation in remote conferences, remote<мозговой штурм>network creative work, comparative analysis of information in the WWW, remote research work, collective educational projects, business games, workshops, virtual tours, etc.
Joint work encourages students to get acquainted with different points of view on the problem being studied, to search for additional information, to evaluate their own results.
1The penetration of information technologies into the field of education leads to the expansion of the conceptual base, both through the formation of new concepts and through the use of old concepts in a new semantic meaning. The need to change the semantic content of some concepts of cybernetics is primarily due to the fact that the tasks of learning management cannot be considered in isolation from the state of the student. From this point of view, the information field built in training systems and the many participants in the educational process form a single whole - a "self-consistent system". This concept was borrowed by us from physics, like many other concepts that have already found application in the description of automated learning systems, not by chance. In our opinion, there is much in common between the tasks of learning automation and methods of describing, for example, a quantum system. At the same time, the content of the concept of “quantum of information” has much more in common with the concept of an energy quantum than is commonly believed.
From the point of view of information technology, the task of learning can be considered as transferring the system to a new qualitative state through a finite number of quantitative transformations.
When developing automated learning systems, the information processed by the computer and offered to the user should be evaluated, first of all, from the point of view of the perception of this information by consciousness as information useful for the formation of a personality. In other words, any learning system (not necessarily automated) is a semantic information system (SIS) . In this regard, it is expedient, in our opinion, to single out such cybernetic elements, which are commonly called information flows , specifying, however, these concepts as applied to SIS.
Under semantic information flow in learning (SIPO) we will understand such a sequence of changes in our knowledge, which only in its entirety is perceived by consciousness as a certain step in the development of the personality, i.e., it ensures the transition of the personality to a new quality.
The input of the learning system receives information organized according to the principle of "elementary diversity": a set of bits of information is uniformly processed over time. Bits of information given on the numeric axis x and the interrupt processing cycles set by the generator can be considered as the coordinates of some "spatio-temporal" manifold (x, t) - a homogeneous space of screen events.
The processing of information for the purpose of learning is a violation of the homogeneity of the manifold, its transformation into a certain, possibly metric, space. To understand exactly what changes occur in the continuous flow of information in the process of preparing it for perception from the screen of a computer monitor, let us consider the basic operations on the information space dictated by the tasks of learning.
1. Markup of the information space - the division of the information space into SIPO.
2. Formatting SIPO - setting a single element, a unit of information flow in relation to the learning process.
3. SIPO quantization. By quantization of SPS we mean its decomposition into some basic components that correspond to predetermined properties that depend on the characteristics of the computer representation of information, learning tasks, and perception features. In this case, the quantization procedure itself can be decomposed into two components:
- sequential quantization - splitting into parts of the "length" of the information flow (long-quantization);
- parallel quantization - stratification of individual long quanta into layers - flaky quanta along the way of deepening the idea of an information flow element.
4. Distribution of SIPO. In the learning process, the need for different quanta is different, and this circumstance makes it necessary to solve the problem of distributing the information flow over the area of computer representation of knowledge (lines, frames, windows).
5. Concatenation (connection) of SIPO. The content of the term is similar to its meaning in programming. We are talking about both the connection of individual layers of long quanta of the same SIPO, and the connection of some quanta (both long and flaky) of different SIPO. As a rule, concatenation within the same SIPO is due to the use of the same flaky quanta by different long quanta.
6. Gateway information flow - suspension of the flow of new information to adjust the basic knowledge needed to understand further reasoning.
7. Merging of information flows - the formation of a new information flow based on the results obtained in several independent SIPO.
It is useful to clarify the problem of quantization of SIPO based on the understanding of the energy quantum accepted in physics. In physics, a quantum of energy (quantum of an electromagnetic field) is understood as an energy portion that is emitted, moves in space and is absorbed only as a whole, as a whole - a corpuscle. In this case, the absorption property of a quantum depends on the relationship between the energy of the quantum and the capabilities of the absorbing system, i.e. The energy of a quantum absorbed by a system is a property not only of the quantum, but also of the absorbing system. In the existing interpretation of the quantum of information, this basic property of the energy quantum is absent altogether. But it is precisely this property that makes it possible to speak of a quantum system. The trainees placed in the information space represent a multi-level system that requires the assimilation of a different amount of information for its qualitative change, i.e. quanta of various informational energy. From this point of view, a screen page of a text, a formula, a drawing cannot be considered as invariant concepts of information flow quanta. In accordance with the concept of semantic information, only such a set of data that necessarily changes the state of our knowledge should be considered a quantum of information, and from the point of view of learning, only an assimilated portion of information can change the state of knowledge. A piece of information can be assimilated only when all the data from this portion is clear to the student. Thus, even with the same learning background, one may understand the formula without additional explanations, another with additional explanations, and the third needs an explanation of the terminology used in the explanation. Such an understanding of the quantum of information brings it much closer to the concept of the quantum of energy. It is obvious that for certain sizes of the information quantum it makes no sense to talk about the possibility of its absorption at all, i.e. assimilation.
However, it should be noted that a person, as an element of the educational process, tends to break information into quanta himself in order to fully assimilate it. At the same time, he has to solve additional problems of sorting the available information and searching for the missing information. The solution of these problems should be assigned to automated training systems. The refinement of semantic operations on semantic information considered above, based on the tasks of learning, allows, in our opinion, to better organize the process of preparing the source material for its use in automated learning systems.
Literature
- Gorovenko L.A. Building an information and educational environment with elements of artificial intelligence: Dis.... cand. tech. Sciences. Krasnodar, 2002. - 167 p.
- Solomatin N.M. Information semantic systems. - M.: Higher School, 1989. - 127p.
Bibliographic link
Rykova E.V., Rykov V.T. COMPUTER TEACHING SYSTEMS AND INFORMATION STREAMS // Successes of modern natural sciences. - 2004. - No. 3. - P. 87-88;URL: http://natural-sciences.ru/ru/article/view?id=12424 (date of access: 09/19/2019). We bring to your attention the journals published by the publishing house "Academy of Natural History"
As a rule, elements of programmed learning are part of automated learning systems (AOS). These systems are complexes of scientific, methodological, educational and organizational support for the learning process conducted on the basis of computer or, as they are also called, information technologies. From the point of view of modern didactics, the introduction of the information environment and software has introduced a huge number of new opportunities in all areas of the learning process. Computer technologies represent fundamentally new means of education. Due to their speed and large memory reserves, they make it possible to implement various environments for programmed and problem-based learning, to build various options for interactive learning modes, when in one way or another the student's answer really affects the course of further learning.
As a result, a modern teacher must inevitably master new educational approaches based on the means and methods of individual computer training. In the general case, the teacher gets access to computer tools, information environment and software products designed to support teaching activities. All these tools form complexes of automated learning systems.
As part of automated learning systems, a number of learning problems are currently being solved. The first group includes the tasks of checking the level of knowledge, skills and abilities of students before and after training, their individual abilities, inclinations and motivations. For such checks, appropriate systems (batteries) of psychological tests and examination questions are usually used. This group also includes the tasks of checking the performance indicators of students, which is carried out by registering such psychophysiological indicators as reaction speed, attention level, etc.
Second a group of tasks is associated with the registration and statistical analysis of the indicators of mastering educational material: the establishment of individual sections for each student, determining the time for solving problems, determining the total number of errors, classifying the types of individual errors, etc. It is logical to include the solution of tasks of managing educational activities in this group . For example, tasks to change the pace of presentation of educational material or the order of presenting new blocks of educational information to the student, depending on the solution time, type and number of errors. Thus, this group of tasks is aimed at supporting and implementing the main elements of programmed learning.
Third a group of AES tasks is associated with solving the problems of preparing and presenting educational material, adapting the material according to difficulty levels, preparing dynamic illustrations, control tasks, laboratory work, and independent work of students. As an example of the level of such classes, one can point to the possibility of using various information technology tools. In other words, the use of software products that enable the formation of various complex laboratory or other practical work. For example, such as assembling a "virtual" oscilloscope with a subsequent demonstration of its capabilities for registering, amplifying or synchronizing various signals. Similar examples from the field of chemistry may concern the modeling of the interaction of complex molecules, the behavior of solutions or gases when the experimental conditions change.
The technical support of automated learning systems is based on local computer networks, including automated workstations (AWS) of students, a teacher and a communication line between them (Fig. 10.1). The student's workplace, in addition to the monitor (display) and keyboard, may contain a printer, multimedia elements such as speakers, sound synthesizers, text and graphic editors. The purpose of all these technical and software tools is to provide students with solutions, reference material and means of registering answers. Equipping the teacher's central workplace includes significant additional technical and software elements that allow you to register
Rice. 10.1. The general scheme of a closed control loop in the "teacher - student" system. The software of automated workstations of a teacher and a student (ARMP and ARMU) makes it possible to implement various options for automated training systems, including programmed training systems based on taking into account individual learning difficulties and issuing personal tasks
individual responses of students, keep statistics of types of errors, issue individual tasks and provide corrective assistance. Extended versions of automated learning systems can have access to the Internet, access to databases in various subject areas, and e-mail.
1The analysis of computer training systems is carried out, the main problems in their construction are revealed. The main problem is the construction of a model of the student, there are a large number of these models, but they poorly take into account the psychophysiological characteristics and characteristics of the student and, as a rule, are not used in the formation of the structure of educational resources and their content, which reduces the effectiveness of the use of computer training systems. The construction of models is proposed to be built in the form of a semantic network, which differs from other models in the visibility and simplicity of knowledge representation, the presence of mechanisms for their structuring and compliance with modern ideas about the organization of human memory. The creation and improvement of computers has led and continues to lead to the creation of new technologies in various fields of scientific and practical activity. Despite the current rapid development of computer training systems, there are many problems associated with both their development and the implementation and efficiency of using these training systems. The main problem in creating adaptive learning systems is the difficulty in building such a software environment that could “understand” a person.
a computer
education
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5. Petrushin V.A. Learning systems: architecture and implementation methods (review) // Izvestiya RAN. Technical cybernetics. - 1993. - No. 2. - S. 164-190.
6. Petrushin V.A. Expert-training systems. - Kyiv: Naukova Dumka, 1992. - S. 196.
7. Pimenov V. I. Algorithmic support of the instrumental complex for the formation of knowledge about technological processes. Izvestiya vuzov. Instrumentation. - 2009. - No. 1. - P. 3–9.
8. Rybina G.V. Teaching integrated expert systems: some results and perspectives / Artificial intelligence and decision making. - 2008. - No. 1. - P. 22–46.
9. Frolov Yu.V., Makhotin D.A. Competency Model as a Basis for Assessing the Quality of Specialist Training // Higher Education Today. - 2004. - No. 8. - P. 34-41.
The creation and improvement of computers has led and continues to lead to the creation of new technologies in various fields of scientific and practical activity. One of these areas was education - the process of transferring systematized knowledge, skills and abilities from one generation to another. Being a powerful information sphere in itself, which has experience in using various classical (non-computer) information systems, education quickly responded to the possibilities of modern technology.
Before our eyes, non-traditional information systems associated with learning are emerging; it is natural to call such systems information-training.
Automated learning systems are systems that help to master new material, control knowledge, and help teachers prepare educational material.
The purpose of the study: to analyze computer training systems, identify the main problems in their construction, develop submodels of a computer training system for advanced training.
Modern research in the field of using computers in education is developing mainly within the framework of several main areas, which can be described as follows: intelligent learning systems; educational multimedia and hypermedia; learning environments, microworlds and modeling; use of computer networks in education; new technologies for teaching specific disciplines.
Despite the current rapid development of computer training systems, there are many problems associated with both their development and the implementation and efficiency of using these training systems.
Considering the problem of developing computer learning systems in general, one cannot fail to mention the following important feature noted by V.L. Stefanyuk, is the allocation of two main processes: learning as learning and learning as tutoring (figure).
Classification of intelligent computer learning systems
The direction of learning (learning systems) is self-learning, learning with a teacher, adaptation, self-organization, etc., therefore, when developing learning systems, models are studied that demonstrate the ability to adapt to the environment by accumulating information. The direction of tutoring (learning systems) is closely related to the questions of “whom to teach” (learner model), as well as “what to teach” (learning model) and even “why to teach”, i.e. here models of transfer of information and knowledge from the teacher with the help of the computer are investigated.
Since there are no generally accepted theories and learning algorithms in the field of pedagogy, there are no formal models of the student, learning, educational influences, explanations, etc., hopes are placed mainly on logical-linguistic models. The interpenetration of the integration processes of artificial intelligence and pedagogy was expressed in intelligent learning systems, as well as in teaching integrated expert systems, in the need to introduce additional tools to support the student's model, according to which the teacher determines the current subgoal of learning at a strategic level, as well as tools that implement a specific model of learning in the form of a set of educational influences at the tactical level and providing the teacher with the opportunity to observe the actions of the student and provide him with the necessary assistance.
G.A. Atanov in the book "Activity Approach to Teaching" writes that the modeling of knowledge about the student has three main goals - establishing "what he is", "what we want to see him" and "what he can become". Sometimes subject knowledge and skills in a particular discipline/course are included in the student's normative model, or a five-component subject model is considered as part of the normative model, etc.
The main problem in creating adaptive learning systems is the difficulty in building such a software environment that could “understand” a person. Therefore, most developments in this area are based on creating models of trainees with subsequent description and construction of various hypotheses (works by A.G. Hein, B.S. Gershunsky, V.P. Zinchenko, A.V. Osin, S.V. Panyukova, I. V. Robert, and others). Models are assigned a certain set of characteristics, which subsequently directly affect the construction of the training system itself. There are a fairly large number of student models, but they poorly take into account the psychophysiological characteristics and characteristics of the student and, as a rule, are not used in the formation of the structure of educational resources and their content, which reduces the effectiveness of the use of computer training systems.
From this point of view, the model of the student and, accordingly, the structure of these systems implemented on the basis of the use of adaptation technologies, must take into account the modality of the student; type of his temperament; the current psycho-emotional state of the student. Of particular interest is the determination of the current psycho-emotional state of the student. As real tools that determine the psycho-emotional state, two large groups can be distinguished:
1. Tests and testing programs.
2. Special devices or systems.
In modern works on computer training systems, there are practically no studies related to the formation of a student's competency model, reflecting his ability to apply knowledge and personal qualities for successful activity in a specific professional area, which is a new process in the framework of the creation and use of these systems. This model can be considered as a new dynamic component of the student's model, closely related, on the one hand, to the psychological portrait of the personality, and, on the other hand, reflecting the results of using specific teaching influences.
There are various approaches to modeling the content of education as a complex system, ways to represent semantic information, problems that arise in the development of knowledge-based systems, and the most common models for their presentation. To represent knowledge in intelligent systems, there are various ways, the presence of which is caused, first of all, by the desire to represent knowledge related to various subject areas with the greatest efficiency.
The method of knowledge representation in most cases is implemented using the corresponding model. The main types of knowledge representation models are divided into logical (formal), heuristic (formalized) and mixed.
Based on a system analysis of intelligent models of knowledge representation, a model in the form of a semantic network was chosen as the main means of solving these didactic problems in the field of informatics.
Having done a system analysis of intellectual models, we can conclude that the model of a computer training system for advanced training should include the construction of the following three submodels: a student model (M1), a model of the learning process (M2), an explanation model (M3) .
Model M1 includes the following components: in the simplest case - accounting information about the student, and in more complex ones - the psychological portrait of the student's personality (Ph); the initial level of knowledge and skills of the student (); the final level of knowledge and skills of the student (); algorithms for identifying the levels of knowledge and skills of the student (A); psychological testing algorithms to identify personal characteristics, on the basis of which a psychological portrait of the student's personality (АPh) is formed. Under the term "knowledge", in accordance with the point of view of O.I. Larichev, is understood as the theoretical preparedness of the student (declarative knowledge), and the term "skills" - the ability to apply theory in solving practical problems (procedural knowledge).
To implement the A and APh algorithms in the formation of the M1 model, the following set of student testing procedures was used: the procedure for entering initial information (control questions, the vector of correct answers and weight coefficients for each question); the procedure for deriving questions and answer options in the process of conducting knowledge control; evaluation procedure; procedure for calculating the final grade. Model M1 contains information about the state of knowledge of the student (models , ) ─ both general, integrated characteristics, and those that reflect the assimilation of the current educational material by him.
In general, the learner model is a finite directed graph, which can be described as Mlearner =
Currently, a library of evaluation algorithms has been developed, which are flexibly used when testing trainees, depending on the specifics of the course/discipline and the contingent of trainees. For example, a method based on the balanced assessment of T. Roberts for closed-type questions and supplemented by the possibility of arbitrary setting of the degree of severity of the assessment, as well as weighting the questions by difficulty coefficients obtained on the basis of an expert assessment, is effectively used. In this case, balance means the independence of the mathematical expectation of the assessment from the number of correct and incorrect answers received randomly to this question.
To form the model of the student M1, the reference model Me is used, corresponding to the level of knowledge of the teacher about a particular section of the course being studied, with which the results obtained at the stage of constructing M1 will be compared. Formally, the reference model Me, as well as the student model, is a directed graph, i.e. a set of the form Me =
The dynamic construction of the student's model M1 is carried out by comparing the current M1 with the reference model Me previously built by the teacher. It is important to note that at this stage, along with the identification of the level of knowledge and skills, the construction of a psychological portrait of the individual is carried out.
The learning process model contains knowledge about the planning and organization (design) of the learning process, general and particular teaching methods, so the proposed M2 model includes the following components: a set of M1 models; a set of learning strategies and learning influences; the function of choosing learning strategies or generating learning strategies depending on the input model M1.
It should be noted that the learning management is carried out on the basis of some plan, which is either selected from the library of learning strategies or automatically generated based on the parameters M1, and each learning strategy consists of a certain sequence of learning actions.
The set-theoretic description of the adaptive model M2 is a set of the form M2 =
The explanation model (M3) is developed based on the fact that the existing methods of implementing explanation methods in traditional computer systems do not fully satisfy the learning objectives, in particular, the Ml and M2 models, so the M3 model, focused on production models of knowledge representation, includes the following components :
M3G - target procedures that provide an explanation of the progress of solving the problem by generating explanation texts on the display screen containing descriptions of the rules used in the output (recorded explanations), as well as localizing student errors when solving the current problem;
M3D - procedures for detailed explanation, allowing, depending on the level of knowledge of the student, to visually illustrate the progress of solving the problem with varying degrees of detail;
M3A - algorithms for interpreting the results of the processes of identifying the student's ability to implement direct / reverse inference mechanisms, including the possibility of providing additional information about the objects of the problem area and their relationships.
Models M1, M2, M3 fully specify a typical learning task with the help of specific procedures and functions, and also indicate the presence of certain relationships. In other words, we can say that for the successful implementation and operation of a computer system for advanced training of specialists, it is necessary that its model includes the following functionality:
Building a model of a student (taking into account the psychological portrait of a person, his educational request and the level of initial knowledge) and the reference model of the course;
Building a model of the learning process, the essence of which is the dynamic modification of the learning strategy in accordance with the current model of the student and the subsequent generation of a set of learning influences that are most effective at this stage of learning, taking into account the psychological characteristics of the students;
Control of the student's activity and generation of control decisions for the appropriate adjustment of the student's actions in order to achieve the set learning goals;
Building an explanation model for evaluating the logic of decision making, calculation results, explaining an incorrect alternative or a stage in solving a problem.
Reviewers:
Karelin V.P., Doctor of Technical Sciences, Professor, Head of the Department of Mathematics and Informatics, Taganrog Institute of Management and Economics (TIU and E), Taganrog;
Kiryanov B.F., Doctor of Technical Sciences, Professor of the Department of Applied Mathematics and Informatics, Novgorod State University. Yaroslav the Wise, Veliky Novgorod;
Antonov A.V., Doctor of Technical Sciences, Professor, Dean of the Faculty of Cybernetics, Obninsk Institute of Atomic Energy, National Research Nuclear University MEPhI, Ministry of Education and Science of the Russian Federation, Obninsk.
The work was received by the editors on October 30, 2013.
Bibliographic link
Lyashchenko N.I. ANALYSIS OF MODELS OF COMPUTER TRAINING SYSTEMS. CONSTRUCTION OF SUBMODELS IN A COMPUTER SYSTEM FOR PROFESSIONAL DEVELOPMENT OF SPECIALISTS // Fundamental research. - 2013. - No. 10-10. – S. 2153-2157;URL: http://fundamental-research.ru/ru/article/view?id=32726 (date of access: 09/19/2019). We bring to your attention the journals published by the publishing house "Academy of Natural History"
Computer tutorials (KOPR) are electronic hypertext textbooks with interactive functions and multimedia elements, which are designed for independent work of students with educational material; effective in distance learning technology.
COPR complement traditional educational materials, using the capabilities of modern computer technology.
They include:
theoretical material
analysis of the solution of typical problems and explanatory examples
graphics and animation materials
tests for self-control and knowledge control
necessary additional and service means.
It is possible to identify the most common types of computer facilities:
Presentations- the most common type of presentation of demonstration materials (blah blah)
Electronic encyclopedias combine the functions of demonstration and reference materials and are an electronic analogue of conventional reference and information publications, such as encyclopedias, dictionaries, reference books. Hypertext systems and hypertext markup languages such as HTML are commonly used to create such encyclopedias.
They have a number of additional features:
Usually they support a convenient search system by keywords and concepts;
Have a convenient navigation system based on hyperlinks;
May include audio and video clips.
Didactic materials(collections of tasks, dictations, exercises, examples, essays and essays), presented in electronic form. Also, didactic materials include simulator programs, for example, for solving mathematical problems or for memorizing foreign words.
Knowledge control system programs such as questionnaires and tests. They allow you to quickly, conveniently, impartially and automatically process the results.
Electronic textbooks and e-learning courses combine all or several of the above types of training programs into a single software package. For example, the trainee is first invited to view the training course (presentation); at the next stage, he can set up a virtual experiment based on the knowledge gained while watching the training course; and finally, he must answer a set of questions.
Educational games and educational programs mainly aimed at preschoolers and younger students. This type includes interactive programs with a game scenario. By performing a variety of tasks during the game, children develop fine motor skills, spatial imagination, memory and other skills.
As a result of working with software of various types, we single out the following principles for choosing a software product for use in the lesson:
1) The program should be clear from the first acquaintance to both teachers and students. Program management should be as simple as possible.
2) The teacher should be able to compose the material at his own discretion and engage in creativity in preparation for the lesson.
3) The program must allow the use of information in any form of presentation (text, tables, diagrams, slides, video and audio fragments, etc.).
Training and metodology complex - a system of normative and educational and methodological documentation, training and control tools necessary and sufficient for the qualitative organization of basic and additional educational programs, according to the curriculum. The CMC of an academic discipline is one of the elements of the organization of educational activities in full-time, part-time and part-time forms of education. The teaching materials should be developed for students in all academic disciplines, taking into account the need to improve the quality of assimilation of the content of educational material at the level of the requirements of the SES VPO.
The main goal of creating the WMC- provide the student with a complete set of educational and methodological materials for independent study of the discipline. At the same time, in addition to the direct teaching of students, the tasks of the teacher are: the provision of consulting services, the current and final assessment of knowledge, motivation for independent work.
