What Are the Top 5 Benefits of NLP in Education?
By quickly recognizing the potential of the role of NLP in education, educators can reduce their expenses and improve how they function, augmenting the whole educational process in doing so. The top 5 benefits natural language processing can lend us in education are listed below.
1. Resource Optimization
In traditional learning systems, both students and teachers have to wait often. Students must dedicate time and effort to finding good tutors, while the teachers have to wait until they get enough students to conduct a class. NLP helps bridge this gap between the resources by facilitating online learning sessions that can be carried out remotely.
NLP applications in the classroom help teachers quickly scale their digital learning platforms, increasing the number of activities. This is because the configuration does not require a demanding infrastructure.
Resources such as chatbots are available for students, who can easily access and experience enjoyable learning. In addition, the function of dividing long lessons into short ones makes education convenient for them, preventing them from losing focus during the process.
2. Intelligent Teaching Systems
The use of artificial intelligence in teaching transforms static education systems into dynamic, adaptable, and intelligent teaching and learning environments that are skillfully adapted to the students’ changing needs. A good example is NLP-based voice assistants.
This concept is growing more and more as people seek digital learning to improve their skills. Chatbots adapt programs to students’ skill levels based on the idea of providing personal assistance.
3. Adaptability of Learning Environments
Natural language transformation can serve great for learning-based activities involving repetitive interactions, such as questions about assignments, courses, exams, and so forth. These repetitive administrative tasks, which often end up as daily tasks for employees, can be tackled effectively with constantly evolving NLP-driven technology. It allows educators to focus entirely on key educational activities.
Conversational AI is handy in various ways, helping students better interact with education systems. They can evaluate students through a variety of media, such as images, videos, and texts. Similarly, the chatbot will improve the feedback mechanism to help students quickly follow the development plan, hence undergoing significant improvements systematically.
4. Broader-level Educational Assistance
Real-world scenarios sometimes make it difficult for teachers to respond to the specific needs of students. NLP can be the savior in such situations. For example, NLP can help design learning solutions that enable teachers to respond effectively to the needs of students with disabilities. Similarly, it can act as a feeding system that lends data to deep learning algorithms working on students’ psychometry.
5. Cost and Labor Efficiency
One of the best things about NLP-based training solutions is that they do not require heavy infrastructure and can be implemented in no time. In fact, a teacher can successfully use chatbots for only 10% of the cost of building a complete e-learning platform.
NLP Use Cases in Education
Most of the communication and activities in an educational context are done through speech and text. Thanks to this, the treatment of natural language and its use in teaching is a logical coincidence.
NLP has brought a host of benefits to the academic environment, and new NLP use cases in education are being developed and proposed. The most significant of them include:
1. Text Summarization
Too large pieces of unstructured information, such as dissertations and academic articles, can be automatically compiled under NLP. It allows you to reduce the text to essential elements and create a new concise version, conveying only the most relevant data.
The industry refers to this process in NLP as extraction-based summarization and is used to produce a summary of a particular document.
In abstraction-based abstractions, the natural language process uses deep learning techniques to transcribe unstructured information into existing text and generate passages or phrases that were not present in the original document.
You can use automated summaries at the search or documentation level to extract the most relevant information from data sources and automatically insert the results into your search article or database.
2. Machine Translation (MT)
As the Internet continually breaks down barriers between countries, the significance of natural language processing tools for machine translation (MT) becomes evident. They resort to deep learning neural systems to translate speech and text into different languages. Some commonly known machine translation platforms include Google Translate, Facebook Translation App, and Microsoft Translator.
For more personalized educational applications, there are private machine translation systems that use NLP. These systems are adapted to particular language areas. You can even train them to understand the industry jargon and terminologies specific to different professions, such as medicine, finance, law, etc.
Natural language platforms with machine translation can be particularly valuable for English language learners (ELLs) because they allow students to practice English as a second language outside the classroom.
In addition, comparative NLP systems can help assess English-speaking students’ knowledge levels over time and track their progress. And online language teachers can give these students feedback on any grammatical, syntactic, or sentence construction errors that occur while learning.
3. Semantic and Sentiment Analysis in Learning Environments
In NLP, semantic analysis helps determine the meaning conveyed by a language. It is a complex area of natural language transformation that requires systems to analyze sentence structure, the interaction between different words, and related concepts.
This analysis reveals the meaning of the word in part of a text along with the general sense and subject of the paragraph or document.
In sentiment analysis, natural language transformation systems use machine learning algorithms that can classify a piece of text based on the polarity of the meaning it expresses. The analysis classifies steps on a scale from positive, negative, or neutral to nuances between these extremes.
In business and data analysis, sentiment analysis is used to measure public opinion on social media and other platforms, also assessing the feedback from customers or users of various channels. It also covers business and support channels consumers use to communicate with brands.
Education administrators and staff can use NLP semantic and sentiment analysis to study students’ behavior in response to their instruction and changes in their academic and social environment.
This can play an essential role in assessing whether a particular curriculum or teaching approach is well accepted and in identifying students who may have difficulty.
Teachers can also use natural language processing techniques to study the level of collaboration between students in the classroom. Researchers have begun to use social media analysis methods for language data to discover models of student collaboration in online discussion forums and Massive Open Online Courses (MOOCs).
4. Using NLP to Improve Academic Writing
Historically, the natural language process has had significant success in teaching, identifying problems with the grammar and mechanics of students’ words, and providing holistic results for five-paragraph essays.
In addition to these relatively limited applications, natural language learning systems can help students provide action-oriented feedback on specific pieces of written text or formative feedback.
Students can use this supplementary feedback to improve their writing while revising their work. For example, NLP analysis can reveal whether the essential elements of an essay, such as key topics, arguments, or evidence, are present in a student’s work. NLP systems can also advise on how to organize written work.
Combined with Automated Writing Evaluation or AWE assessment systems, natural language processing solutions can provide students with low-level feedback, such as vocabulary advice or higher-level recommendations that affect the structure and flow of a document or essay.
The range of written assignments for which NLP can be used constantly expands, including resource-based essays. Authors should integrate data from multiple sources, external documents, stories, and abstracts.
5. Establishing Readability Formulas
With NLP analysis, educators can now create readability formulas that help adapt reading material to individual students to make the text challenging and rewarding. The formulas use measures that provide information on the complexity of language in the form of vocabulary, textual context, and syntactic density.
The reading model analysis allows teachers to better predict the extent to which students can read and understand each passage. And it applies to a wide range of readers and genres. Some NLP examples also have simplification algorithms that can automatically adjust the text to better suit students who need to read the text.
6. Using NLP for Behavior Motivation
There is no doubt that motivating students to work and learn is one of the most challenging tasks for teachers. Students often get bored after a while before ultimately dropping out of the course.
That’s why emphasizing student participation and motivation is so imperative. And this is what makes natural language processing in education even more relevant and beneficial.
Moreover, NLP machine learning technology is capable enough to analyze the language used by teachers and students in the classroom to define their mental states during learning. With this analysis, teachers can see in real-time how students react to teaching methods and what can be done to get them more involved.
NLP for teachers can also alert students who are having difficulty understanding a lesson and identify students who need more attention to improve. It opens up a newer and exciting avenue in the market for NLP tracking tools to record, track, and analyze student behavior and alertness during a class.
Of course, more accurate results may require further testing, but you need to establish and test examples of natural language therapy for education to see how effective it can be.
7. Improving Reading Skills with NLP
If students have difficulty reading in class, there are NLP applications in education that can make life easier for them. NLP algorithms can quickly detect errors in students’ reading skills and provide automatic real-time feedback on how they can improve.
Of course, a teacher would never have the time or resources in a classroom full of children to provide this type of feedback to every student, which is why NLP solutions are becoming increasingly popular.
Another great feature of NLP in teaching is that it can help students match with the reading content that suits them best in terms of complexity and productivity.
Again, teachers will never have the time to figure this out for themselves, which is why NLP solutions that can do so are so appreciated.
NLP technology has also been shown to evaluate students’ reading outcomes significantly more accurately than traditional methods, such as the Flesch-Kincaid readability test.
8. Question Answering
Students and teachers love essays, quizzes, and cards, but their manual preparation can be complex for already over-occupied professionals.
NLP can help you in this area by creating questions and answers for your learning materials. It may also determine the extent to which a student’s written response corresponds to the facts in the teaching material.
It can adapt questions and grades to students’ unique educational needs, such as reading level and pace of learning. The teachers will be able to evaluate essays on grammar, structure, semantics, and reasoning, allowing them to save significant time.
Chatbots are another feature to consider since they can act as robot trainers using conversational artificial intelligence to explain things and answer questions.
The way a question-answering model is built is just the same as a voice recognition sequence-to-sequence model is created. It initiates by transcribing the student’s spoken questions or phrases into text.
Then the question-answer model generates answers in order to evaluate the student’s responses. You can use text-to-speech synthesis to convey results to the students.
Intangible qualities like kindness, warmth, friendliness, enthusiasm and accent can be rooted in a synthesized voice to make the student feel comfortable.
10. NLP for Assistance in Language Learning
Another of the most common use cases of NLP is a specialized and popular field of language learning.
At a basic level, language learning involves learning to read, write and speak the words of another language by associating them with words in your native language or pictures of objects. Advanced language skills require you to immerse yourself in a new language’s script, literature, and culture.
By integrating NLP’s translation skills and making it fun through gamification, you can help students and faculty in many other regions with minimal effort.
NLP techniques can also mimic some of the advanced language learning activities:
A real-time smartphone camera stream combining NLP with object detection can generate scene descriptions in another language.
Conversational NLP, text-to-speech synthesis, and speech recognition can be combined so that students can communicate with foreigners and learn each other’s languages.
NLP’s amalgam with optical character recognition works amazingly to help people learn to read literature in a second language. Annotations like meaning, synonyms, and parts of speech can be displayed on e-learning devices in real-time.
What’s even more interesting is that natural language processing is only one of many ways how we can make the best of AI in education sector.