7 Applications of NLP in the Education Sector

Natural language processing, or NLP, is being heralded as one of the most revolutionary and significant ways that the learning and education world will be enhanced both now and over the future years. It has previously been established that NLP is successful when used in teaching. This technology is already in use and has had a lot of beneficial effects on helping kids improve their reading and writing skills.

 

The advantages of NLP are starting to truly stand out, whether it be for parsing and summarizing ideas in writing or for encouraging essay revisions and enhancing a general writer's prose, but one fundamental concern still lingers for many companies, developers, and service managers.

 

First of all, advances in NLP can assist students in writing better essays by offering formative input (i.e., actionable feedback on certain essay components) that can be used in the rewriting process to enhance more than simply grammar and mechanics.

 

For instance, NLP may be used to determine if crucial discourse components like assertions, arguments, and evidence are present or absent. NLP may also provide students with comments on how well an essay is organized. Automatic writing evaluation (AWE) systems may be created by combining several NLP techniques. These systems can offer both low-level input (such as suggestions for vocabulary) and higher-level feedback (e.g., advice about the cohesion of discourse).

 

What is Natural Language Processing?

 

Words and writing are the primary forms of communication among humans. Natural Language describes how people communicate with one another. Humans communicate with one another often and in a variety of languages, whether orally or through written language.

 

This data is in normal language, but since computers only speak in 1s and 0s, they are unable to understand it. The generated data is useful and can provide insightful information. To comprehend, mimic, and intelligently react to human speech, computers are required.

 

In the field of artificial intelligence known as "natural language processing," or NLP, robots are equipped with the capacity to read, comprehend, and extrapolate meaning from human languages.

 

To understand language structure and rules and create models that can interpret, decode, and identify important elements from text and voice, natural language processing (NLP) integrates the fields of linguistics with computer science.

 

Applications of NLP

 

NLP is one method by which people have given robots a soul and lessened the demand for labor. Tasks involving voice and human contact have been automated as a result. Among the uses for NLP are:

 

• Tools for translation use natural language processing (NLP) to translate statements between languages. Examples include Google Translate, Amazon Translate, and others.

 

• Chatbots: Commonly seen on websites, chatbots are an efficient method for businesses to respond to frequent inquiries.

 

• Virtual assistants: You can chat with virtual assistants like Siri, Cortana, Google Home, Alexa, and others, and they can also interpret commands you give them.

 

• Targeted Advertising: Have you ever discussed a good or service, or merely googled something, and then started getting advertising for it? This practice of reaching specific audiences at precisely the correct moment is known as targeted advertising, and it helps businesses produce huge amounts of income.

 

• Grammar checkers also enter the picture to assist you to write flawlessly, in addition to autocorrect, which will instantly correct any spelling errors you make.

 

Applications of NLP in Education

 

NLP systems are capable of doing extensive analysis on unstructured data sets, which include text-based data from internet content, news articles, documents, social media discussion, and customer service interactions with companies. Machines can go through this data and learn from it with little human intervention thanks to the methods employed in natural language processing. Additionally, systems can be specially designed to meet the requirements of any sector or application.

 

The automation of customer care, speech recognition, voice assistants, translation technologies, email filtering, and text analysis and rewriting are only a few examples of typical NLP applications. 

 

Natural Language Processing is also the key technology underpinning common applications like chatbots, internet search engines, product suggestion portals, and the digital voice assistants that drive smartphones, smart homes, and autonomous cars.

 

Speech and written text account for a large portion of interaction and communication in educational settings. This provides a logical connection between the use of natural language processing in education. Now that NLP has proven to be beneficial in an academic environment, new use cases are being created and put forth.

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Applications of NLP in Education

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1. Text Summarization:

 

Automatic summarizing under NLP enables the reduction of the text to its key components and the creation of a short new version that transmits just the most pertinent information for big pieces of unstructured material, such as dissertations and academic papers. A summary of a certain body of work is produced using this NLP technique, known as extraction-based summarizing.

 

In abstraction-based summarization, deep learning algorithms are applied to unstructured data to produce new phrases or sections that are not present in the original text while also paraphrasing the current content.

 

To automatically extract the most pertinent facts from a data source and add the findings to your research paper or database, utilize automated summarization at the research or documentation level.

 

2. Machine Translation:

 

Deep learning neural networks are used by natural language processing (NLP) technologies for machine translation (MT) to translate speech and text into many languages, as the internet continues to break down boundaries between nations. The Facebook Translation App, Microsoft Translator, and Google Translate are examples of generic machine translation services.

 

NLP-based customizable machine translation systems are available for more specialized or focused educational applications. These systems are designed for a single linguistic domain or may be educated to comprehend the jargon and language used in a particular field, like law, finance, or medicine.

 

For English Language Learners (ELLs), natural language platforms with machine translation capabilities can be very helpful since they allow students learning English as a second language to practice outside of the classroom. 

 

Additionally, NLP systems with benchmarking capabilities may be used to evaluate the competency levels of English language learners over time and monitor their development. Additionally, these students can receive feedback from online language instructors on any grammar, syntactic, or sentence-building mistakes they make.

 

3. Improve Academic Writing:

 

Natural Language Processing has historically been quite effective in the educational setting, recognizing student grammar and word use issues and offering comprehensive scores for five-paragraph essays.

 

Beyond these very narrow uses, natural language learning systems can help students by offering formative feedback or actionable input on certain passages of a written work. When rewriting their work, students may utilize this new information to expand the scope of their writing beyond grammar and mechanics. 

 

For instance, NLP analysis may show if crucial components of an essay, such as important themes, compelling arguments, or supporting data, are present or missing in a student's writing. NLP tools may also make suggestions on how to structure a piece of writing.

 

Natural Language Processing tools may provide students with low-level feedback, such as vocabulary suggestions, or higher-level recommendations that impact the structure and flow of a document or narrative when used in conjunction with Automatic Writing Evaluation (AWE) systems.

 

Essays with sources are now part of the growing list of writing assignments to which NLP may be applied. Data from various sources, outside papers, narratives, and summaries all need to be integrated by writers.

 

4. Semantic and Sentiment Analysis:

 

The goal of semantic analysis in NLP is to determine what a language means. It is a challenging topic of natural language processing that calls for the analysis of sentence structure, word interactions, and numerous related concepts by computer systems. This analysis exposes the general meaning and subject of the passage or document as well as the significance of the individual words in the text.

 

Machine learning models are used in sentiment analysis by natural language processing systems to categorize texts based on the degree of disagreement they represent. The analysis assigns passages to one of three categories: positive, negative, or neutral, as well as shades of opinion in the middle.

 

Sentiment analysis is used in the business and data analytics sectors to monitor public opinion on social media and other platforms and analyze user or customer feedback from the many channels for buying and getting the help that customers use to connect with firms.

 

NLP semantic and sentiment analysis may be used by administrators and faculty at educational institutions to examine how students behave in reaction to the present teaching they are getting as well as modifications to their academic and social settings. This can help determine if a specific curriculum or teaching strategy is being well-received and in locating pupils who may be experiencing problems of one type or another.

 

Natural language processing tools may also be used by teachers to examine the degree of collaboration among their pupils. To identify patterns of student collaboration in online discussion boards and Massive Open Online Courses (MOOCs), researchers have started to apply social network analysis techniques to linguistic data.

 

5. Formulating Readability Measures:

 

Teachers may now create readability formulae using NLP analysis to help them match reading materials to specific pupils in a way that makes the text appropriately difficult and rewarding. The formulae make use of metrics that provide information about the vocabulary, text cohesiveness, and syntactic density of a language.

 

Instructors may more accurately forecast the rates at which their students will be able to read and grasp certain sections using readability formula analysis, which is relevant to a wide range of readers and genres. Some NLP systems also include text-simplification algorithms that can change material automatically to better suit the audience of students who will read it.

 

6. Chatbots:

 

A sequence-to-sequence model for speech recognition is similar to how a question-answering model is built. The student's spoken inquiries or replies are first typed up. Chatbots, which may function as robotic instructors and use conversational AI to answer queries, is an additional factor to take into account.

 

The process of producing responses or assessing student responses using a question-answering paradigm comes next. With the use of text-to-speech synthesis, the outcomes are read back to the pupil. They could provide the synthetic voice with intangible characteristics like fervor, generosity, warmth, friendliness, and accent to reassure the student.

 

7. Improved Reading:

 

Applications of natural language processing in education can help students who have problems reading aloud in class. The NLP algorithms can quickly identify pupils who have reading comprehension problems and can provide instant, automated feedback on how to improve. Of course, in a classroom full of students, a teacher would never have the time or resources to go around and give each kid this type of feedback, which is why NLP solutions are becoming more and more in demand.

 

Another noteworthy feature of NLP in education is its capacity to match students with reading material that is both challenging and fruitful for them. Once more, instructors would never have the time to independently study this. NLP solutions that can accomplish this are therefore much valued.

 

It has also been shown that NLP technology can grade student reading scores more accurately than traditional methods, such as the Flesch-Kincaid Grade Level exam. This is another element influencing how businesses are using applications programming interface (API) solutions for natural language processing.

 

Conclusion:

 

NLP is a boon to the educational sector. NLP's amazing qualities have fundamentally altered the educational environment. NLP in education has the potential to be innovative and advantageous to both instructors and students alike. Additionally, it can assist teachers in improving the atmosphere for learning and the caliber of instruction within certain tasks. Even though the study is still in its infancy, it seems that NLP can have a profoundly positive impact on learning.