Since the goal of the work is to improve the process of remote learning of foreign languages, it was chosen to create an application for translation based on the choice of the native language and the language being studied. An educational process using a set of telecommunication technologies aimed at enabling students to learn the basic amount of information they need without direct contact between students and teachers during the learning process (which can take place both synchronously and asynchronously), and can be both an independent form of education, as well as a supplement to another more traditional form of education (full-time, part-time, extramural or externship), if necessary, giving a person the opportunity to study a foreign language training course. So, on the basis of this concept, a translation application was developed, which accurately translates both ordinary language and phraseological units, slang expressions, etc. The model is used as the basis of training, so let's analyze the model according to the main indicators. The model was pre-trained on BookCorpus, a dataset consisting of 11,038 unpublished books and the English Wikipedia (excluding lists, tables and titles). The texts are written in lowercase and tokenized using WordPiece and a dictionary size of 30,000. With probability 0.5, sentence A and sentence B match two consecutive sentences in the original corpus, and in other cases it is another random sentence in the corpus. Note that a sentence here is a continuous stretch of text, usually longer than one sentence. The only limitation is that the result with two "sentences" has a total length of less than 512 tokens. The masking procedure details for each sentence are as follows: 15% of tokens are masked; in 80% of cases masked tokens are replaced by [MASK]; 10% of the time, masked tokens are replaced by a random token from the one they replace; in the remaining 10% of cases, masked markers remain unchanged. The model was trained on 4 Cloud TPUs in a Pod configuration (16 TPUs in total) for one million steps with a batch size of 256. The sequence length was limited to 128 markers for 90% of the steps and 512 for the remaining 10%. Adam optimizer is used with learning rate: β1=0.9, and β2=0.999, weight decay 0.01, learning rate warm-up for 10,000 steps and learning rate linear decrease after. After training the network, the mean squared error decreased from 34.2 to 3.3. Also, training the network made it possible to reduce overtraining and improve its ability to generalize to new data. In the trained network, the number of layers and neurons was increased, which allowed it to reproduce more complex dependencies in the input data. Training the network made it possible to improve its results on test data, increase its ability to generalize, optimize its structure and parameters, choose a more effective activation function, and reduce the risk of overtraining.
Foreign language training, machine learning, neuron network, neuron network training
2023 Copyright for this paper by its authors.
Nowadays, there is a certain class of topical tasks, the solution of which is impossible or difficult to
implement without the use of artificial neural networks (ANNs) [
To achieve the goal, the following tasks were set: 1. Conduct an analysis of the literature on the use of neural network technology. 2. Analyze and describe the necessary tools for creating classification software based on neural networks. 3. Build a classification model based on neural networks. 4. Analyze materials for working with programming languages. 5. Build the software architecture. 6. Develop software. 7. Test software. 8. Analyse work results.
The object of research concerns processes for remote learning of foreign languages with adaptation to the user based on machine learning. The subject of research comprises methods and tools for developing a software product for teaching foreign languages based on neural networks. The methodological basis of the research is general scientific and special methods, which made it possible to study the subject and the object of research, to explore directions and ways of optimizing the process of remote learning of foreign languages with adaptation to the user based on machine learning. The practical significance of the obtained results is that the use of the developed software will allow to improve translation from any selected pairs of languages, which will serve as a basis for remote training of users.
Intelligent adaptive learning systems are emerging rapidly, but are still in the experimental stage
[
Adaptive platforms based on machine learning are the most advanced scientific method for
establishing a truly adaptive state. Machine learning (ML) [
What is unique about adaptive systems based on machine learning is their ability to determine how
an individual learns and approaches a learning task within these intelligent systems, as well as provide
accurate and timely feedback and improve student performance. Since MN-based systems are
computationally intensive and analyze billions of bits of data in real time, system scalability can be an
issue from two perspectives: how these systems are efficiently coded [
In order to be able to guide the user through the learning and assessment process [
Security information relates to the user's authority to use the system and takes the form of User
name; Password. The OAuth 2.0 protocol (OAuth 2.0, 2012) provides the necessary security for secure
user login. Role information provides an understanding of the relationship between system users and
can be described as: Administrator; Tutor and Student/Apprentice. Thus, the system can easily provide
different services to different types of users. Personal information is mostly static data that records some
basic information about the user: Name; Address; Telephone; School; Email; Gender; Telephone. It
may also contain useful information such as postcode, which may help identify other users living in the
area that an active user may connect to, as this may be useful for identification/placement within a group
or beyond [
We may wish to record the time it takes a user to answer a question because: a) this may vary from
user to user; b) it can be used to distinguish between difficult and easier questions (and even use this
information later to adjust the difficulty level of the question). In addition, the level of difficulty of
individual questions in the test can give an idea of the overall level of difficulty of the test [
When a user interacts with an e-learning system, he/she does so by performing a set of actions [
In the educational platform, we can distinguish between self-directed and directed learning. The
actions we might want to record differ slightly between them, although they share many elements. In
both areas, the concept of "engagement" is very important. Engagement can be measured using a
combination of the following [
Evaluating the effectiveness of the use of distance education systems is impossible without studying
statistical data on the organization of educational content, its quality and compliance with the
educational and calendar plans of the organization of the educational process in general educational
institutions [
The integration of the systems presented above in most cases ensures the organization of information exchange between them [63-69].
Nowadays, there are a large number of different programming languages and each of them has its own scope of application, but still, in order to conduct an analysis on the choice of the best language, it is necessary to select several of the most popular languages to conduct an analysis between them, so in this section we will turn to statistics by popularity as. The following technology stack is selected for work: JavaScript, HTML, CSS, TAILWIND, Node Js.
The following main dependencies were used for development: "@types/node": "18.11.3", "@types/react": "18.0.21", "@types/react-dom": "18.0.6", "autoprefixer": "^10.4.12", "eslint": "^8.30.0", "postcss": "^8.4.18", "prisma": "^4.8.0", "tailwindcss": "^3.2.4", "typescript": "4.9.4"
Visual Studio Code was used as the code editor. Visual Studio Code is a full-featured text editor for editing local files or the code base. It includes various features for editing the code base, which helps developers track changes. Various features supported in VSC: Syntax highlighting, Auto indent, Recognition of file types, Sidebar with files of the specified directory, macro, Plugin and packages. Visual Studio Code is used as an integrated development editor (IDE) like Sublime text and NetBeans. The current version of the VS code editor is compatible with various operating systems such as Windows, Linux and MacOS.
When searching the Internet, users often feel overwhelmed by the amount of data that comes back
to them. Methods and systems are needed to help users navigate the Internet and filter information. This
is especially important for distance learning sites, as it is important that every user stays on the platform.
Current work has combined user profiling and a responsive user interface to help satisfy all users. User
profiling [
The first two approaches rely on traditional marketing techniques using known information or information gathered in person or over the phone to create relevant profiles. A third approach that uses "learned models" is our area of interest. The approach involves creating a system that initially does not know its users, but over time develops a profile model based on user interactions. Profile models can be created individually, for each user, or collectively, gathering all user data together to form an overall profile of interests and behavior.
The developed product has a simple interface consisting of intuitive blocks. The program, the main direction of which is to help with the translation of text from different languages into a language understandable to the user, works according to the following principle: To begin with, the user is presented with a reserved set of phrases for translation, based on this neuron, the network collects data to form a user profile. Data-sets and models for training were used from the resource that collects all data-sets and models for training neural networks using languages https://huggingface.co/.
After training the network, it becomes possible to use the selected languages based on the translation made during initialization of the application. In order for the neural network to be able to perform the task, it must be trained. Training a neural network is a process in which the parameters of a neural network are adjusted by simulating the environment in which the network is embedded. The type of training is determined by the method of adjusting the parameters. The learning process of the neural network is shown in Fig. 4.
In the process of functioning, the neural network forms an output signal Y, implementing some function Y = G(X). If the architecture of the network is given, then the form of the function G is determined by the values of the synaptic weights and the biased network. Let the solution of some problem be the function Y = F(X), given by the input-output data parameters (X1, Y1), (X2, Y2), ..., (XN, YN), for which Yk = F(Xk) (k = 1, 2, ..., N). Learning consists in finding (synthesis) a function G close to F in the sense of some error function E. If a set of training examples is selected - pairs (XN, YN) (where k = 1, 2, ..., N) and the method of calculating the error function E is selected, then the training of the neural network turns into a multidimensional optimization task that has a very large dimension, and since the function E can have an arbitrary form, learning in the general case is a multi-extremal nonconvex optimization problem.
According to different aspects of application and use of e-learning systems, adaptability can be defined in different ways. It is proposed to define adaptability as the property of a learning system that adapts and changes itself according to the requirements and characteristics of users before and during its use. On this basis, the following adaptive levels are distinguished: elementary adaptive level; static adaptive level; dynamic adaptive level.
Goal tree is a logical thinking tool that starts with the goal which the organization is trying to achieve and breaks down all the necessary conditions to achieve it. The goal tree is depicted in the Fig. 5.
Fig. 6 demonstrates the Activity diagram that has such main control points: Entry point (initialization); Data; Prism (orm db); Interaction with the user; Operation of the program; Termination
At the first stage - program execution, the application is initialized. The application receives initial assets, interface, data. At the second stage, after loading the main components, database data and software Json files are checked. At the third stage, a module with a prism is executed, which allows you to process requests and data sent/extracted from the database. We receive json, after processing requests and transfer the user to the main application in the translation tab.
The fourth stage is the stage of interaction with the user. If translation is selected - user data is accepted -> request processing -> providing results -> record in json -> record in db. Another option is model training, which works as follows: Training is selected -> We download json assets -> We expect a translation from the user -> We send data to the database -> We randomly generate the next translation. If the training is finished or another tab is selected, proceed to the fifth stage.
The fifth stage - The operation of the application is indicated on the activity diagram (Fig. 6). The application, at this stage, interacts with data and user requests and interacts programmatically between components and user requests. If the transition to models is selected or the application is closed, we proceed to stage six. The sixth stage – the transition to the review of models and training or closing the application is selected – we save the received data, initialize the completion of the program.
The class diagram is clear and simple, when the user interacts with the application, he chooses one of the available buttons. If you choose to translate, the data is sent to prisma using download-data, which accepts the main parameters: language, request, status. If training is selected, we receive data by request in the post_data file, we have several request\status parameters, after which we interact with random_data, which, using chat.json, randomly generates and sends sentences to the user for translation from the selected asset.
The state diagram is as follows: Interaction with the user starts the initialization of the program, after which the initial configuration is performed.
System analysis relies on a number of applied logical-mathematical disciplines, technical procedures and methods that are widely used in management activities, including formalized and informal means of research, as well as on a set of principles, that is, basic rules accepted as truth, which are used as a basis for building methods of analysis. Flowchart analysis is usually applied to fairly simple systems, while fault tree synthesis is applied to more complex systems.
Today, the Internet offers various ways to learn foreign languages. Thespecial variation is the study of English as the main language of communication of the global community. We will try to analyze the most effective methods of learning English using some online resources. Today there are 4 main methods of teaching English: Grammar Translation – a classic method of learning English (translation from the native language into a foreign language and vice versa). Direct Method - direct method (The main attention is paid to good pronunciation, spontaneous use of language without the use of translation, little attention is paid to grammar analysis). Audio-lingualism is one of the first modern methods (the method of repeating and memorizing standard phrases). Communicative Language Teaching – modern standard method (communicative learning method is based on the idea that successful learning of a foreign language occurs with the help of its study in real situations, which, in turn, leads to natural mastery and ability to use a foreign language).
So, for our developed application, we will use grammar translation and direct method, for use by one user, and also to increase the functionality of the application, we will use phraseological units, dialect words and slang elements for it. That will improve understanding and learning of the chosen language. To start the application, it is necessary to connect the necessary libraries and modules. First, let's install the modules and dependencies correctly:
The main logic of the application is written using the Java Script language, Tailwind Css is used for styling. Prisma is used to place data obtained in the process of interaction with the user. The Prisma schema is intuitive and allows you to declare database tables in an understandable way, simplifying the data modeling process. Models are defined manually or analyzed from an existing database. The Prisma schema file is the main configuration file for setting up Prisma. It is usually called schema.prisma and consists of the following parts: Data Sources: Specify the details of the data sources to which Prisma should connect (for example, a PostgreSQL database); Generators: Defines which users should be generated based on the data model (eg Prisma Client); Data model definition: defines application models (data form per data source) and their relationships.
So, let's make the necessary settings:
After creating and configuring Prisma, it is necessary to create interface elements for interaction with the user:
To begin with, we define what the client will use, then we import the necessary: Fonts (pictures for the menu), fonts, styles. After that, we connect the objects defined in other modules. After receiving the objects, we "lay out" them in containers and fill them with content. According to this logic, the necessary interface elements were created: Sections, buttons, electronic links, etc. After connecting the styles and the styling process, our app looks like this:
Next, we need to create some data, send some to the server, and take some from the server, the following modules perform these actions:
When the user interacts with the application, data is sent to the prism. POST [
To train the network, we use a ready-made collection of data (Assets), in the training application we have the following interface:
Assets are extracted from our json file and provided to the user. After the user offers his version of the translation, the data will go to the server, after which the neural network will improve the translation based on what was sent. The main function of the developed application is translation. For the user, it is possible to configure a selected pair of languages for training. For example, you can use the pair: English-Ukrainian, after which the user will receive a correct translation. There are several important aspects here, the translator uses neural networks for training, and therefore each accurate translation contributes to the improvement of the app's translation. The neural network is trained to perform a more accurate translation by studying: phraseological units, a separate dialect, slang. The second aspect is when using models. Each model has its own initial settings, for example, one of the models we use:
General information: T5-Base is a control point with 220 million parameters.
Developers: Colin Raffel, Noam Shazir, Adam Roberts, Catherine Lee, Sharan Narang, Michael Mathena, Yanqi Zhou, Wei Li, Peter J. Liu. Check out the related article and the GitHub repository Model type: Language model. Language(s) (NLP): English, French, Romanian, German. License: Apache 2.0. Related Models: All T5 control points So, this is a big plus, we can choose a specific learning model for each user or group of users.
Data for translation is obtained with the help of the hugginface API using a token:
So, since the application is deployed locally, first it is necessary to unpack its styles with the following command: npx create-next-app --example with-tailwindcss with-tailwindcss-app.
When we see that all the necessary modules are assembled, it is necessary to run the application locally, for this we use Versel. Go to https://vercel.com/dashboard, log in and add the project:
Translate - perform a translation. About\Help improve - used for network training. Learn More – an electronic link to a web application that contains models and datasets, so we will work with each button.
In the current section, the application needs to translate the given phrase, to improve the translation and train the neural network, after we send the translation, we receive an alert that the data has been sent:
When you click the Learn More button, you are redirected to a web resource with models and datasets:
Since the goal of the work is to improve the process of remote learning of foreign languages, it was chosen to create an application for translation based on the choice of the native language and the language being studied. An educational process using a set of telecommunication technologies aimed at enabling students to learn the basic amount of information they need without direct contact between students and teachers during the learning process (which can take place both synchronously and asynchronously), and can be both an independent form of education, as well as a supplement to another more traditional form of education (full-time, part-time, extramural or externship), if necessary, giving a person the opportunity to study a foreign language training course. So, on the basis of this concept, a translation application was developed, which accurately translates both ordinary language and phraseological units, slang expressions, etc. The model is used as the basis of training, so let's analyze the model according to the main indicators. The model was pre-trained on BookCorpus, a dataset consisting of 11,038 unpublished books and the English Wikipedia (excluding lists, tables and titles).
The texts are written in lowercase and tokenized using WordPiece and a dictionary size of 30,000. Then the model input looks like this: [CLS] Sentence A [SEP] Sentence B [SEP].
With probability 0.5, sentence A and sentence B match two consecutive sentences in the original corpus, and in other cases it is another random sentence in the corpus. Note that a sentence here is a continuous stretch of text, usually longer than one sentence. The only limitation is that the result with two "sentences" has a total length of less than 512 tokens.
The masking procedure details for each sentence are as follows: 15% of tokens are masked. In 80% of cases masked tokens are replaced by [MASK]. 10% of the time, masked tokens are replaced by a random token (different) from the one they replace. In the remaining 10% of cases, masked markers remain unchanged.
The model was trained on 4 Cloud TPUs in a Pod configuration (16 TPUs in total) for one million steps with a batch size of 256. The sequence length was limited to 128 markers for 90% of the steps and 512 for the remaining 10%. Adam optimizer is used with learning rate : 1e-4, β 1=0.9β1=0.9 and β 2=0.999β2=0.999 weight decay 0.01, learning rate warm-up for 10,000 steps and learning rate linear decrease after.
With fine-tuning for further tasks, this model achieves the following results:
Regarding the advantages and disadvantages of the application, the following can be highlighted: Advantages: 1. Simple and clear interface 2. Possibility of specific selection of models and data sets 3. Used and supported by free resources 4. Easy access to data with Prisma 5. High degree of protection Cons: 1. It is necessary to train the network a lot for quality learning 2. Since the application is hosted locally, it is impossible to train in large groups until there is compatible access.
So, the developed application has great advantages and few disadvantages that can be easily fixed, the application is ready to be made and tested.
Timely detection of cyber-threats is important, but in our case we have a high degree of protection of modules, files and the application, and therefore the probability of a cyber-threat is almost 0%. For each module and library, in our case, the acquisition proceeds as follows:
Wesee: name, version of theclosedfile(set whenstartingtheproject andNodeassembly), requires - a parameter that determines whether this module is needed, version, resolved - where we get it from, and integrity - integrity\protection of the file. All modules in the project are protected by a SHA-512 key. SHA-512 is a hashing algorithm that performs the function of hashing some data given to it.
Hashing algorithms are used in many things, such as Internet security, digital certificates, and even blockchains. It is part of a group of hashing algorithms called SHA-2, which also includes SHA-256, which is used on the Bitcoin blockchain for hashing.
The project is protected by SHA-512 keys and Versel authentication mechanisms, and in order to start working with Versel, you need to have a Git Hub account that also protects your repositories, based on this we conclude that the probability of cyber-attacks is minimal.
Training a network is the process of optimizing its weights and biases to reduce the prediction error on the training data. At the same time, the network "learns" to recognize regularities in the data and form its own representations of objects. When comparing a trained and untrained network, the following advantages of training can be noted: Better prediction accuracy: Training the network allows for better prediction accuracy on test data compared to an untrained network. For example, if we consider the problem of image classification, training the network can reduce the error of image classification from 30% to 5%. Generalization ability: Training a network allows you to increase its generalization ability. This means that the network can correctly classify new data that it has not seen before. For example, if a network has been trained to classify images from two classes (cat and dog), it can correctly classify images from three classes (cat, dog, and rabbit) if it has been trained on a large enough number of images. Optimization of the structure and parameters: Training the network allows you to optimize its structure and parameters to obtain better prediction accuracy. For example, you can change the number of layers and neurons in each layer, choose different activation functions and loss functions, change the batch size for training, use different optimization methods such as Adam, SGD, RMSProp, etc. Various regularization techniques can also be applied, such as Dropout, L1 and L2 regularization, as well as data augmentation to avoid overtraining.
Now let's compare our trained network and untrained network:
A trained network is better than an untrained network for several reasons. First, training the network allowed it to improve its performance on test data. For example, after training the network, the mean squared error decreased from 34.2 to 3.3. Also, training the network made it possible to reduce overtraining and improve its ability to generalize to new data. The second reason is that training the network allowed it to optimize its parameters and structure to enable more efficient pattern recognition in the input data. For example, the number of layers and neurons was increased in the trained network, which allowed it to reproduce more complex dependencies in the input data. The third reason is that training the network allowed us to choose a more efficient activation function (in this case, the relay), which allowed us to improve the convergence speed of the network and reduce the training time. So, training the network made it possible to improve its results on test data, increase its ability to generalize, optimize its structure and parameters, choose a more effective activation function, and also reduce the risk of overtraining.
The information system for remote learning of foreign languages with adaptation to the user based on machine learning has been created. Moreover, features of use, adaptive learning systems, existing methods, profile modelling standards and architecture have been considered and analysed. Also, the research task has been formulated. The systematic analysis of the research object has been performed, namely, the main provisions of fuzzy logic, its architecture, advantages and disadvantages have been considered. Thus, the model of the process of forming user parameters in adaptive learning systems, the tree of goals, have been analysed. The main languages and software tools used for development have been defined. So, Java Script has been chosen as the main language for development and the main software for work such as VS code (as a code editor), Versel (as a web application for testing) have been analysed. The formation of the user profile and the choice of architecture have been reviewed.
Practical implementation of the designed software product has been carried out. Also, the initialization process of the data set for neural network training has been described, the architecture and models have been explored and visualized, data preparation has been carried out, model construction has been implemented. So, the developed application has several minor disadvantages that can be easily corrected, and many advantages that are not found in analogues. A trained network is better than an untrained network for several reasons. First, training the network allowed it to improve its performance on test data. For example, after training the network, the mean squared error decreased from 34.2 to 3.3. So, training the network made it possible to improve its results on test data, increase its ability to generalize, optimize its structure and parameters, choose a more effective activation function, and also reduce the risk of overtraining. 7. References [40] D. Malikin, I. Kyrychenko, Research of Methods for Practical Educational Tasks Generation
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