Universal language module. Using modular foreign language teaching to develop student autonomy. Interface for UNL language

STAGE-3 is a multifunctional natural language text processing system that has been developed since the 1980s. a group of Russian linguists, mathematicians and programmers at the Institute of Information Transmission Problems of the Russian Academy of Sciences. The STAGE-3 system is based on the theory “Meaning Û Text”, developed by I.A. Melchuk, and the integral theory of language developed by Yu.D. Apresyan.

STAGE-3 is not a commercial development aimed at achieving a specific application goal. Our main task is linguistic modeling of natural language and computer implementation of such models. This explains our desire to build models that are as adequate as possible from a linguistic point of view. Often, extensive linguistic information is introduced into the system, whether it is needed to improve the efficiency of computer word processing or not. In particular, we strive to obtain linguistically correct syntactic structures for each sentence, not because otherwise the sentence cannot, for example, be correctly translated into another language, but simply because the task of modeling natural language syntax requires it. However, we are convinced that ultimately the theoretical adequacy and completeness of linguistic information pays off from a purely practical point of view.

All STAGE-3 applications use the original three-valued logic system and the elaborate formal linguistic description language FORET (see Apresjan et al. 1992a, Apresjan et al. 1992b).

2. Stage-3: modules, properties, architecture, implementation

2.1.Modules

The ETAP-3 system contains the following main modules:

• High quality machine translation system
• Module for generating Russian texts based on the Universal Network Language (UNL)
• Natural language interface for databases
• System of synonymous paraphrase of sentences
• Syntax error corrector
• Computer-assisted language teaching system
• Workplace for syntactic markup of a text corpus.

Below we will briefly describe all these modules, and we will dwell on one of them - the UNL module - in more detail.

2.1.1. Machine translation system ETAP-3

The main module of STAGE-3 is a machine translation (MT) system serving five pairs of languages. There are systems for translation: (1) from English to Russian, (2) from Russian to English, (3) from Russian to Korean, (4) from Russian to French and (5) from Russian to German.

To date, the first two systems have been developed in most detail. The translation system from English into Russian and from Russian into English, which can be considered as a single bidirectional module, is intended for translating real texts, mainly scientific and technical topics. Best results received for texts by computer technology, electrical engineering, economics and politics, since the combinatorial dictionaries of the working languages ​​of the system (each containing about 50,000 dictionary entries) are predominantly focused on the vocabulary of these subject areas. However, STAGE-3 also copes with texts on everyday topics, since in lately dictionaries were significantly replenished with everyday vocabulary. For each lexeme, the combinatorial dictionary contains its syntactic, word-formation, semantic and word-formation features, its control model, as well as information about stable phrases with this lexeme.

In addition, there is a Russian morphological dictionary (100,000 dictionary entries), which, in addition to purely morphological information, contains basic syntactic information about the lexeme and its approximate translation equivalent. The English morphological dictionary has the same structure (60,000 dictionary entries). The system is based on comprehensive grammatical descriptions of the English and Russian languages, compiled by the developers of STAGE-3.

For other pairs of languages, translation systems exist at the prototype level.

If a homonymous sentence is received at the input of STAGE-3 and the system cannot resolve this homonymy, then several translation options are offered at the output. In all other cases, the system produces one, most plausible syntactic structure and one, most likely translation. If the user of the system wants to get all possible translations, he can select the appropriate option, and the system will “remember” all cases of unresolved homonymy and produce all possible syntactic sentence structures with lexical contents allowed for them. Let's consider one real example. Offer They made a general remark that... with the “all translation options” option selected, it was translated into Russian in two ways, which differ both in syntactic structures and in the choice of vocabulary: (a) They made a general comment that... and (b) They forced the general to note that…

2.1.2. Natural language interface for databases

This module of the ETAP-3 system translates queries specified in free form in natural language (English or Russian) into expressions in the SQL query language. The module also provides translation from SQL to natural language. The module is based on a semantic component developed specifically for this purpose, which translates the deep syntactic structure into a formal semantic representation, from which you can easily move to a representation in the SQL language.

2.1.3. System of synonymous paraphrasing

This module is designed to conduct linguistic experiments to obtain a variety of synonymous and quasi-synonymous periphrases of Russian and English sentences. The system is based on the apparatus of lexical functions, one of the most important innovations of the “Meaning Û Text” theory. The result of the synonymous paraphrase module can be illustrated with the following example:

(1) The director ordered John to write a report – The director gave John an order to write a report – John was ordered by the director to write a report – John received an order from the director to write a report.

This area of ​​linguistic research seems very promising, as it may have the most various applications, for example, in teaching native and foreign languages, in authoring systems and text planning systems.

2.1.4. Syntax error corrector

This module is designed for processing texts in Russian. Its goal is to search for and correct various kinds of errors in grammatical agreement, as well as in case management.

2.1.5. Computer-assisted language teaching system

This module is a stand-alone software application, namely, computer game in the form of a dialogue. This program can be used when teaching Russian, English and German as a foreign language. The game is intended for those who have already mastered the language well, but would like to expand their vocabulary, first of all, due to stable phrases and means of paraphrasing. The system is based on the apparatus of lexical functions. The program can also be successfully used by speakers of the above languages ​​who want to enrich their vocabulary (for example, journalists, teachers and even politicians).

2.1.6. Workplace for syntactic markup of a text corpus.

This newly developed module uses ETAP-3 dictionaries, as well as the system's morphological and syntactic analyzers, to build the first syntactically marked corpus of Russian texts. This application is of a mixed type: the tree structure obtained as a result of automatic analysis is then edited by a person using convenient graphical tools.

2.2. Basic properties of the system

Among the main features of the ETAP-3 system as a whole and its individual modules, the following can be noted:

• Using rules as the basic unit of an algorithm
• Level approach
• Transfer through the transfer stage
• Using Dependency Trees
• Lexicalist approach
• Possibility of receiving translation options
• Possibility of diverse use of linguistic resources

In this version of STAGE-3, all modules use only rule-based algorithms. However, in a number of recent experiments, the MP module was supplemented with a component based on accessing the translation memory. , and a statistical component that semi-automatically extracts translation equivalents from bilingual text corpora (see Iomdin & Streiter 1999).

Like many other natural language text processing systems, ETAP-3 is characterized by a layered approach. During processing, each sentence goes through several stages and at each stage is presented in the form of a certain structure: 1) morphological, 2) syntactic and 3) normalized (or deep syntactic). The actual translation (transfer) is carried out at the level of a normalized syntactic structure, i.e. English normalized structures are transformed into the corresponding Russian normalized structures and vice versa.

What distinguishes ETAP-3 from most similar systems is the use of syntactic dependency trees to represent the structure of a sentence (all over the world, most natural language text processing systems use the structures of the immediate constituents).

STAGE-3 is characterized by a lexicalist approach in that the information recorded in the dictionary is considered as important as the information recorded in the grammar. Accordingly, STAGE-3 dictionaries contain significantly more information than dictionaries used in other similar systems. The STAGE-3 dictionary entry contains, in addition to the name of the lexeme, information about the syntactic and semantic features of the lexeme, its control model, translation equivalent, various rules, as well as the meanings of lexical functions, keyword which this lexeme is. Syntactic features words characterize his ability or inability to perform in certain syntactic constructions. A word can be assigned several syntactic features from general list, containing more than 200 features. Semantic features are necessary to check the semantic agreement between words in a sentence. Management model words contain information about the surface expression of the actants of a given word (for example, a word can control one or another preposition or conjunction or one or another case form of a name). The most important components of a dictionary entry are rules. All rules in STAGE-3 are distributed between the grammar and the dictionary. Grammatical rules are more general and apply to broad classes of words, while rules mentioned in dictionary entries (either directly or by reference) apply to small groups of words or even individual words. This organization of rules ensures that the system is automatically configured to process each individual proposal. During the translation process, only those rules are activated that are explicitly referenced in the dictionary entries of the words contained in the sentence.

As an illustration, we present a fragment of a dictionary entry for the English word chance:

SYNT:COUNT,PREDTO,PREDTHAT

DES:"FACT","ABSTRACT"

D1.1: OF"PERSON"

D2.1: OF"FACT"

D2.2: TO2

D2.3: THAT1

SYN1: OPPORTUNITY

MAGN: GOOD1/FAIR1/EXCELLENT

ANTI-MAGN: SLIGHT/SLIM/POOR/LITTLE1/SMALL

OPER1: HAVE/STAND1

REAL1-M: TAKE

ANTIREAL1-M: MISS1

INCEPOPER1: GET

FINOPER1: LOSE

CAUSFUNC1: GIVE /GIVE

TRANS: CHANCE/ HAPPENING

R:COMPOS/MODIF/POSSES

1.1 DEP-LEXA(X,Z,PREPOS,BY1)

1 ZAMRUZ:Z(PO1)

2 ZAMRUZ:X(RANDOM)

1 ZAMRUZ:Z(RANDOM)

TRAF:RA-EXPANS.16

TRAF:RA-EXPANS.22

When developing the ETAP-3 system, we sought to build its components in such a way that they could be used for a variety of purposes. In particular, the main grammatical and vocabulary resources of the system are used in all its modules. For example, Russian dictionaries are used at the analysis stage when translating from Russian into English and at the synthesis stage when translating from English into Russian; the same dictionaries are used in the MP module, in the paraphrase system, in the syntactically marked corpus, etc. Moreover, some of the system’s resources can be “alienated” from it and, after being refined depending on customer requirements, can be naturally used in various processing systems -linguistic texts.

2.3.General architecture of the ETAP-3 system

To give general idea about the functioning of the ETAP-3 system, we provide general algorithm MP module (Scheme 1). All other modules can, with a certain reservation, be considered as derivatives of this one.

MACHINE TRANSLATION MODULE OF THE STAGE-3 SYSTEM

(ARCHITECTURE)

2.4. Implementation

The ETAP-3 system was implemented on a MicroVax computer ( operating system VMS). Recently a new one was created software for working with STAGE-3 on personal computers under Windows NT 4.0, which allows the lexicographer to use a number of additional tools and more efficiently maintain and edit dictionaries.

3. Interface for UNL language

3.1 Background and objectives

UNL module is being developed as part of an extensive international project with a very ambitious goal: to overcome, at least partially, the language barrier that separates Internet users. Despite the fact that with the advent of the Internet, time and space barriers between people have practically disappeared, Internet users continue to be separated by a language barrier. This appears to be the main obstacle to successful international and interpersonal communication in the information society. The diversity of languages ​​spoken by Internet users has been recognized as one of humanity's pressing problems. In any case, this is evidenced by the fact that the project aimed at solving this problem is being carried out under the auspices of the UN and coordinated by the Institute for Advanced Study at the UN University.

The project was founded in 1996. Currently, 15 universities and research institutes from Brazil, Germany, India, Indonesia, Jordan, Spain, Italy, China, Latvia, Mongolia, Russia, Thailand, France and Japan are participating in the project.

It is expected that in the coming years teams from other countries will join the project, so that ultimately it is planned to cover official languages all UN member countries

The idea of ​​the project is as follows. A universal intermediary language is proposed that is powerful enough to express all the important information conveyed by texts in natural languages. This language - Universal Network Language (Universal Networking Language, or UNL) was proposed by H. Uchida (United Nations University). For each natural language, it is proposed to develop two systems: a “deconverter” that would translate texts from the UNL language into a given language, and an “enconverter” that would convert texts in a given language into UNL language expressions. It should be emphasized that generating text in UNL will not be completely automatic. This procedure is planned as a dialogue between a computer and a human (editor).

Thus, this project is fundamentally different from traditional machine translation. First of all, the input for generating texts in different natural languages ​​is the UNL structure, the quality of which does not depend on the imperfection of text analysis procedures. During the interactive construction of UNL structure editor will review the results of the automatic enconverter, correct errors and resolve remaining ambiguity. The editor can then run the deconverter and translate the edited text UNL expression into your native language to check the results of your work and, if necessary, make additional changes to this expression.

Other important difference UNL systems from machine translation is that expressions in the language UNL can be generated and stored regardless of the natural languages ​​into which these texts will be translated. UNL can be thought of as universal method representation of meaning. To process UNL text automatically—for example, to index it, search it, or extract information from it—it is not necessary to translate the text into natural language. The latter is necessary only if a person will work with the text.

An enconverter and a deconverter for each natural language form a language server, which is planned to be hosted on the Internet. All language servers will be connected into a single UNL network, which will allow an Internet user to translate any document from UNL into his own language, as well as translate into UNL those texts that he wants to make publicly available.

3.2 UNL language

In this article we will not be able to describe the UNL language in detail, since this topic deserves a separate article, which will likely be written by the creator of the language, Dr. Hiroshi Uchida. We will only dwell on those features of the UNL language that will be important for further presentation. The complete UNL language specification is located at http://www.unl.ias.unu.edu/.

UNL is a computer language designed to represent information in a way that can produce text containing that information in a wide variety of languages. A UNL expression is a directed hypergraph corresponding to a natural language sentence. The arcs of the graph represent semantic relationships, e.g. agent(activist),object(object),time(time),place(place),instrument(tool),mode(modus operandi) etc. At the nodes of the graph there are so-called Universal Words (US) denoting concepts, or groups of US. Nodes can be provided with attributes. Attributes contain additional information about the use of the node in a given clause, e.g. @imperative, @generic, @future, @obligation.

Each US corresponds to some English word. Some words have semantic delimiters that clarify the meaning of these words. In most cases, delimiters indicate the place of a concept in the knowledge base. This is done as follows. Universal Word of Kind A(icl>B) is interpreted as ‘A belongs to category B’. For example, US coach without any restrictions has the same meanings as the English word coach generally. To clarify the meaning of a word, delimiters are used. Yes, the expression coach (icl>transport) should be understood as 'coach as a vehicle’, that is, bus; expression coach (icl>human) has an interpretation ‘ coach as a person’, that is, trainer, and the expression coach (icl>do)– interpretation ‘ coach as a type of action’, that is, a verb train. In other words, the apparatus of limiters allows us to represent the US as an English word taken in exactly one meaning. In addition, limiters allow you to introduce concepts for which English There are no single word symbols. For example, in the Russian language there is an extensive group of verbs of movement, the meaning of which includes an indication of the method or means of movement: fly in, swim, crawl, run etc. There are no one-word English equivalents for the verbs of this group. However, based on English words, it is possible to construct ES that are close to them in meaning, for example, come (met>ship) means ‘to arrive, with the vehicle being a ship’.

Here is an example of an expression in UNL corresponding to an English sentence

(2) However, language differences are a barrier to the smooth flow of information in our society.

Each UNL line structure is an expression of the form relation (US1, US2). For simplicity, semantic delimiters for universal words are omitted.

aoj(barrier.@entry.@present.@indef.@however, difference.@pl)

mod(barrier.@entry.@present.@indef.@however, flow.@def)

mod(difference.@pl, language)

aoj(smooth, flow.@def)

mod(flow.@def, information)

scn(flow.@def, society)

pos(society, we)

3.3. Translation from UNL into Russian in the ETAP-3 system

As already noted in section 1, STAGE-3 is a transfer system, and the translation itself is carried out at the stage of normalized syntactic structure (NormSS). At this level, it is most convenient to establish a correspondence between the Russian language and UNL, since expressions of the UNL language and normalized syntactic structures reveal a lot common features. Here are the most significant of them:

1. Both expressions of the UNL language and NormSS occupy an intermediate position between the surface and semantic representations of a sentence and approximately correspond to the so-called deep syntactic level. At this level, the meaning of lexical units is not decomposed into primitives, and the relationships between lexical units are the same for all languages;
2. In both UNL and NormSS expressions, nodes represent terminal elements (lexical units) rather than syntactic categories;
3. Nodes contain additional characteristics (attributes);
4. In both UNL and NormSS language expressions, arcs represent directed dependencies.

At the same time, there are significant differences between UNL and NormSS language expressions:

1. In NormSS, all nodes represent lexical units, and in the UNL language, a node can represent a subgraph.
2. In NormSS, a node always corresponds to one meaning of a word, and the meaning of the US can be wider or narrower than the meaning of the corresponding English word:

2.1. The meaning of the US can correspond to several meanings of one word at once (see above).

2.2. They can correspond to a free phrase (for example, computer-based or high-quality).

2.3. They may correspond to some form of the word (for example, the word best is a form of the word good or well).

2.4. They may denote a concept for which there is no direct equivalent in English.

1. NormCC is the simplest of all connected graphs, namely a tree, while a UNL expression is a hypergraph.
2. In UNL, arcs can form loops and connect individual subgraphs.
3. Nodes in NormSS are connected by purely syntactic relations that do not carry any meaning, while relations in the UNL language denote semantic roles.
4. Attributes in NormSS correspond to grammatical characteristics, while the meaning of many UNL attributes is conveyed by lexical means, both in English and Russian (for example, modal verbs).
5. NormSS contains information about the order of words in a sentence, but in the UNL language expression there is no such information.

NormSS of sentence (2) looks like this:

1. 
   Transition from UNL expression to intermediate representation (IR)
2. Transition from PP to Russian NormSS (NormSSR).
3. Synthesis of the Russian proposal on the Norm SSR.

The first of these steps is the interface between the UNL language and the ETAP-3 system, and the rest are carried out standard means English-Russian module of the ETAP-3 system.

The translation algorithm from UNL into Russian is shown in Diagram 3.

As follows from the above, the transition from an expression in the UNL language to NormSS should solve the following five problems:

1. Replace all TS with English words wherever possible. Russian lexemes will appear at the stage of English-Russian translation when referring to English dictionary. If no one was found for the CS English equivalent, the meaning of this US should be expressed by other means.
2. Translate the syntactic relations of the UNL language into the syntactic relations of STAGE-3, either directly or using lexical means.
3. Translate UNL language attributes into STAGE-3 grammatical characteristics, either directly or using lexical means.
4. Convert the UNL graph to a dependency tree.
5. Determine the order of words in a sentence.

The first and (partly) the second problem are solved using UNL dictionaries - English and English combinatorial. Rules written in the formal logical language FORET are responsible for all other tasks.

Thus, all these problems are solved either with the help of dictionaries or with the help of rules. The rules are divided into three classes depending on the degree of universality: there are GENERAL, STYLE and DICTIONARY rules. General rules can be activated when processing any offer. The other two types of rules apply only if the sentence being processed contains a word that contains a reference to some rule (in the case of a stencil rule) or the rule itself (in the case of a dictionary rule). This organization of rules ensures automatic configuration of the system: only those rules that are required to process a particular offer are activated.

3.4. Current state of affairs and plans for the future

An experimental version of the translation module from UNL into Russian is available at http://proling.iitp.ru/Deco. By the summer of 2000 we plan to make the module suitable for public use. Our next task will be to create an interactive enconverter.

As is clear from Diagram 3, the interface between UNL and the structures with which the ETAP-3 machine translation module works is carried out at the level of the English NormSS. From the same diagram it is clear that English translation original UNL expressions is a natural by-product of such an architecture. To do this, it is enough to direct the English NormSS to synthesis. A number of successful experiments in this direction have already been carried out.

Literature

Yu.D. Apresyan, I.M. Boguslavsky, L.L. Iomdin etc. (1992 a ). Linguistic processor for complex information systems. Science, 256 p. M.

Ju. D. Apresjan, I. M. Boguslavsky, L. L. Iomdin etal. (1992b).ETAP-2: The Linguistics of a Machine Translation System. // META, Vol. 37, No. 1, pp. 97-112.

Igor Boguslavsky (1995). A bi-directional Russian-to-English machine translation system (ETAP-3). // Proceedings of the Machine Translation Summit V. Luxembourg.

Leonid Iomdin & Oliver Streiter. (1999). Learning from Parallel Corpora: Experiments in Machine Translation. // Dialogue"99: Computational Linguistics and its Applications International Workshop. Tarusa, Russia, June 1999. Vol.2, pp. 79-88.

The research to which this article is devoted was carried out with partial financial support from the Russian Foundation for Basic Research (grant No. 99-06-80277).

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An average garrison structure that allows you to exchange crafting reagents for Garrison Resources and vice versa. In addition, it opens access to factions (Alliance) and (Horde). Allows you to use the auction.

We recommend using the addon Master Plan- this is the best addon for garrison management on at the moment. The addon can be found and downloaded. For convenient mission management, we recommend using the addon Garrison Mission Manager. You can find and download it.

1. Review

2. How to open access to the trading post?

In this section we will tell you how to access the level 1, 2 and 3 shop.

2.1. Trading shop, level 1

The first level shop will be available immediately after the construction of the town hall.

2.2. Garrison Blueprints: Trading Post Level 2

Garrison Blueprints: Trading Post, Level 2 can be purchased at level 98 or after building an outpost in Spiers of Arak. It costs 1000g or can be exchanged for Notes on creating an outpost. Notes can be obtained twice: the first time - while completing quests in Gorgrond, the second time - while completing quests in the Spiers of Arak, thereby saving some gold. Below are the locations of merchants for the Horde and Alliance.

2.3. Garrison Blueprints: Trading Post Level 3

Garrison Blueprints: Trading Post, Level 3 can be obtained as a reward for the achievement Wild Friends (Alliance) or Wild Friends (Horde), Exalted with three Draenor factions out of five possible: (Alliance), (Horde), (Alliance), (Horde) , (Alliance), (Horde). The achievement is common to all characters on the account.

Once you complete the achievement, don't expect the blueprint to magically appear in your bag. Go to Spartz Boltspin (Alliance) or Rezlak (Horde) and buy it for 1000g.

3. Exchange

When you visit the shop for the first time, you will receive a quest (Alliance) or Little Tricks (Horde). After completing the quest, you will gain access to the merchant. He sells Draenor crafting reagents: ore, grass, meat, fish fillets, fur, leather and dust for enchanting. Price for 5 units. reagent ranges from 20 to 50x Garrison Resources (prices for Horde and Alliance are always different).

4. Orders

Daily orders at the Trading Post allow you to exchange reagents for Garrison Resources. Exchange rate - 5 reagents for 20 resources.

5. Auction

After visiting the second level shop for the first time, you can take a quest from the ancient trading mechanism: (Alliance) or (Horde). You will receive 5 parts, each of which must be combined with different items:

• The enchantment crystal module requires 4 reagents, which can be obtained from mobs and bosses in raids or attacks on the garrison:
• The auction management module requires 3 reagents, which can be obtained from mobs and bosses in dungeons:

One of modern technologies, which allows us to solve the problem of changing the educational paradigm is modular learning, because it is based on the positions of an active, active, flexible approach to building the pedagogical process.

In the late 80s - early 90s of the 20th century, a new term from the field of technical sciences “burst” into pedagogical science, namely “module”. Module (from lat. modulus -"small measure") - component, separable or at least mentally distinguished from the general. Modular usually refers to a thing consisting of clearly defined parts, which can often be removed or added without destroying the thing as a whole.

Much has been written and spoken about the benefits of modular learning in the education system. Modular training - a method of organizing the educational process based on a block-modular presentation of educational information.

The essence of modular training is that the content of training is structured into autonomous organizational and methodological blocks - modules, the content and volume of which can vary depending on the didactic goals, profile and level differentiation of students, and the desires of students to choose an individual trajectory of movement along the training course. Modules can be compulsory or elective. The modules themselves are formed: as a structural unit curriculum by specialty; as an organizational and methodological interdisciplinary structure, in the form of a set of sections from different disciplines, united by thematic basis; or as an organizational and methodological structural unit within academic discipline. A necessary element of modular training is usually a rating system for assessing knowledge, which involves scoring students' performance based on the results of studying each module.

In pedagogical science, a module is considered as an important part of the entire system, without knowledge of which the didactic system does not work. In terms of content, this is a complete, logically completed block.

Modular learning, partially used in schools in England and Sweden, is built according to the rules of modularity, when the design of educational material ensures that each student achieves the set didactic objectives, has completeness of the material in the module and is integrated different types and forms of training. The positive effect achieved as a result of such training is associated with its dynamism, which consists in the variability of the content of elements and modules. The goals of this training are formulated in terms of methods of activity and methods of action, and are divided into cycles of cognition and cycles of other types of activities. Modular learning is distinguished by a problem-based approach and the student’s creative attitude to learning. Its flexibility is associated with the differentiation and individualization of training based on repeated diagnostics in order to determine the level of knowledge, needs, and individual pace of the student’s educational activity.

The leading principles of modular training include:

• 1) principles of modularity;
• 2) structuring the content of training into separate elements;
• 3) dynamism;
• 4) activities;
• 5) flexibility;
• 6) conscious perspective;
• 7) versatility of methodological consulting and parity.

The principle of modularity presupposes the integrity and completeness, completeness and logic of constructing units of educational material in the form of blocks-modules, within which the educational material is structured in the form of a system of educational elements. It is constructed from blocks-modules as from elements training course by subject. The elements inside the block-module are interchangeable and movable. Mastering educational material occurs in the process of a completed cycle of educational activities.

It is the module that can act as a training program, individualized in content, teaching methods, level of independence, and the pace of the student’s educational and cognitive activity. The essential characteristics of modular training include its difference from other training systems.

Firstly, the content of training is presented in complete independent complexes (information blocks), the assimilation of which is carried out in accordance with the goal. The didactic goal is formed for the student and contains not only an indication of the volume of content being studied, but also the level of its assimilation. In addition, each student receives written advice from the teacher on how to act more rationally and where to find the necessary educational material.

Secondly, the form of communication between teacher and student is changing. It is carried out through modules and plus personal individual communication. It is the modules that make it possible to transfer learning to a subject-based basis. Relationships become parity, equal between teacher and student.

Thirdly, the student works independently as much as possible, learns goal setting, self-planning, self-organization, self-control and self-esteem.

Yu.B Kuzmenkova defines a module as an autonomous mini-course and assumes that this course (like any other) is designed in accordance with specified goals, methods for their implementation and verification of achieved results. According to Yu.B. Kuzmenkova needs to set a limited number of specific tasks that are realistically feasible in a limited period of study time. To do this, it is convenient to compose several short-term and alternating modules in accordance with the marked core (for any program) components - thematic orientation and target orientation - and within general course arrange them sequentially and/or in parallel. One of the features of the proposed approach is the possibility of differentiation, which makes it possible to clearly distinguish between the selected target settings; At the same time, as practice shows, it is quite convenient to divide modules into language and speech. This differentiation is based on the principle of focusing attention, according to which mastering educational material, especially unfamiliar material, becomes more effective when differentiating difficulties and removing them one by one. (11, 21-28)

In accordance with this, when compiling language modules, it is assumed that attention will be focused on mastering a specific topic (or a certain sequence of topics) related to the study of an aspect of the language - for example, from the corresponding section of phonetics, grammar or vocabulary, while the compilation of speech modules involves The main task is to master any skills and abilities within one type of RD (or two related ones - receptive and reproductive).

Modern studies of the modular principle of organizing the educational process identify three main blocks in the structure of the module: a block containing the material to be studied, practical and control blocks.

L.N. and M.E. The Kuznetsovs propose the following module structure:

Target block (didactic goal of studying the module, personally oriented tasks that realize the goal).

Update block (list of basic knowledge and methods of action necessary to study the topic, exercises, tests and others independent work for updating

background knowledge).

Problem block (problem situations that are personally significant for students, emotionally rich material on the topic).

Fund block (equipment and didactic materials(constantly updated), methodological findings).

Theoretical block (list of subject knowledge and methods of action, systematizing ideas, principles, patterns, methods, generalizing methods of action, presentation of the content and structure of the topic in the form of support (orientation in the topic).

Application block (a system of multi-level tasks for variable repetition and consolidation).

Generalization block (didactic material for concise generalization, systematization of educational material, reflection).

Recess block (educational material of increased complexity).

Exit block (didactic material for tests, tests, reports at educational conferences, for homework).

Table 2. Possible construction of a language module

Training objectives may include the comprehensive development of skills and abilities, and when working on a selected topic, you can use various types reading/listening, role playing games, presentations, writing reports, writing essays, etc. - but certainly with an emphasis on the primary development of lexical skills. Vocabulary (to the extent regulated by a minimum dictionary) is well acquired during the development of communication skills in the chosen field - in conditional communicative situations of various types.

If students do not have enough time in the basic course to study specific sections of phonetics or grammar, then a module can be similarly constructed that focuses on mastering the necessary skills within a narrow topic. For example, “Correction of the pronunciation of German vowels” or “Correction of the use of tenses verb forms" (in the latter case, a good example is shifting the emphasis from training exercises to study grammar in context). At the same time, it is possible to comprehensively develop speech skills based on already known lexical material. The priority, however, will be targeted work with material on grammar or phonetics.

You can also compose various narrowly thematic modules by choosing targets based on Table 3 as follows.

Table 3. Development of necessary knowledge in aspects of language

It should be noted that the duration and frequency of mini-courses are variable values ​​and a module is not necessarily a whole lesson, it can be half a lesson, or some part of it, or a series of thematically organized lessons of longer duration. When compiling a program, they can be varied and used in different combinations: two different language modules within the framework of the elective course program can be extended over the entire first quarter, and built in parallel within one lesson (10 minutes - phonetic, the rest of the time grammatical), and the lexical module may start from the second quarter, or some other way. The choice and sequence of introduction of modules is determined by the specific needs of students. What is important is that, from a planning point of view, the main requirement is systematic approach to the organization of classes (their cyclicality, continuity, repetition and, accordingly, regular reporting), otherwise all these modular innovations will turn out to be chaotic.

It is most convenient to develop modules for teaching grammar, because... It is grammar that causes the greatest difficulties in learning; it is because of ignorance of grammar that students make numerous mistakes in oral and writing, so let’s dwell in more detail on this aspect of the language.

Grammar is a collection of rules about changing words and combining words in sentences. It makes it possible to put human thoughts into a material linguistic shell. Grammar cannot be separated from speech; without grammar it is not conceivable to master any form of speech, since it, along with the vocabulary and sound composition, represents the material basis of speech. A characteristic feature of grammar is that, abstracting from the particular and concrete, it takes the general thing that underlies the change in words and their combinations in sentences and builds grammatical rules from it.

From the above it follows that grammar has an important practical significance. Therefore, in the experiment being carried out, a module for teaching grammar will be considered.

Thus, using various modules, it is possible to successfully carry out intra-subject and inter-subject connections, integrate educational content, forming it in the logic of the content of the leading academic subject with the need to differentiate educational content.