The field of Artificial Intelligence (AI) known as Natural Language Processing (NLP) focuses on facilitating communication between humans and machines using human language as the interactive medium.
Methods based on NLP can analyze the structure and meaning of human language by examining its syntax, semantics, and morphology. By combining computer science and linguistics, NLP systems transform language data into rule-based machine learning algorithms that can solve specific problems and perform various tasks.
NLP systems can analyze large amounts of unstructured data, including text-based information from online content, news reports, documents, social media comments, and customer interactions. These tools enable machines to sift through this information and learn from it with minimal human intervention. Moreover, these systems can be customized to suit the needs of any industry or application.
Use of NLP in Education
In the realm of education, a significant portion of communication and activities occur through speech and text. This makes NLP and its applications in education a natural fit. NLP has already provided benefits in educational settings, and new use cases are constantly being developed.
Text Summarization
NLP enables automated summarization of large pieces of unstructured information, such as dissertations and academic papers. This process, known as extraction-based summarization, reduces the text to its essential components and creates a concise version that conveys the most relevant information. NLP also employs deep learning techniques in abstraction-based summarization to generate passages or sentences that were not present in the source document.
Automated summarization is particularly useful during the research or documentation stage, as it can extract the most relevant information from a data resource and automatically incorporate the results into research documents or databases.
Machine Translation (MT)
NLP tools for machine translation (MT) utilize deep learning neural networks to translate speech and text into different languages. Popular platforms for general machine translation include Google Translate, Microsoft Translator, and the Facebook Translation App.
For specialized educational purposes, customizable machine translation systems using NLP can be tailored to a specific language domain or trained to understand the terminology and language associated with a particular profession, such as law, finance, or medicine.
These NLP-powered language platforms with machine translation capabilities are particularly beneficial for English Language Learners (ELLs), offering them opportunities for practice outside the classroom. Additionally, NLP systems with benchmarking facilities can assess the proficiency levels of English language learners over time and track their progress. Online language tutors can also provide feedback to these students on any grammatical, syntax, or sentence construction errors they make.
Utilizing NLP to Improve Academic Writing
NLP has been successful in identifying grammar and word mechanics issues in the academic sphere. It can provide holistic scores for five-paragraph essays and offer actionable feedback on specific elements of a written body of text or formative feedback. For example, NLP analysis can reveal whether key topics, arguments, or evidence are present or absent in an essay, and it can evaluate the organization of a written work.
Combined with Automated Writing Analysis (AWE) systems, NLP solutions can provide students with low-level feedback, such as vocabulary suggestions, as well as higher-level feedback regarding the structure and flow of a document or narrative.
The scope of writing tasks to which NLP can be applied continues to expand, including source-based essays that require integrating information from multiple sources, external documents, narratives, and summaries.
Semantic and Sentiment Analysis in Learning Environments
Semantic analysis in NLP aims to establish the meaning of language. It involves analyzing sentence structure, word interactions, and related concepts to reveal the meaning of words in a given text and the overall meaning and subject of the passage or document.
Sentiment analysis, on the other hand, uses machine learning models to classify a piece of text based on the expressed opinion polarity. The analysis categorizes passages on a scale ranging from positive, negative, or neutral to various shades of opinion in between.
In the business and news analytics fields, sentiment analysis helps gauge public opinion on social media and other platforms and assess customer or user feedback from various trade and support channels.
Educational administrators and staff can utilize NLP’s semantic and sentiment analysis to study students’ behavior in response to their current instruction and changes in their academic and social environments. This information can be instrumental in evaluating the reception of a specific curriculum or teaching approach and identifying students who may be facing particular challenges.
Educators can also leverage NLP techniques to examine the level of collaboration among students in a classroom setting. Social network analysis approaches have been applied to language data to reveal patterns of collaboration between students in online discussion forums and Massive Open Online Courses (MOOCs).
Establishing Readability Formulas
NLP analysis enables educators to establish readability formulas that help match learning materials to individual students based on the appropriate level of challenge and reward. These formulas employ metrics that provide information about language complexity in terms of vocabulary, text cohesion, and syntactic density.
Readability formula analysis allows instructors to better predict the reading and comprehension rates of specific passages. It is applicable to various readers and genres. Some NLP systems even feature simplification algorithms that can automatically adjust the text to make it more suitable for the students who will be reading it.
Future Hopes for Natural Language Generation (NLG)
Natural Language Generation (NLG), a sub-branch of NLP, focuses on developing computer systems and applications capable of generating various types of text in natural language based on semantic representations. NLG is currently utilized for text summarization and generating answers to user questions.
While NLG technology is still in its early stages, notable progress has been made. In 2019, Open AI released GPT-2, a robust NLG system trained on eight million web pages. GPT-2 can generate high-quality and coherent text passages, including poems, stories, and news articles, with minimal prompting. Although it may face challenges with highly technical or niche content, there are high hopes for the future evolution and use of NLG technology in educational settings.
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