Abstract
Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.
Abstract (translated)
人工智能(AI)是一个快速发展的领域,已经渗透到许多商业和研究领域。机器学习、深度学习和自然语言处理(NLP)是AI的子集,用于处理数据 processing 和建模的不同方面。本综述文章概述了AI对教育的影响,并结合当前机会进行了描述。在教育领域,学生反馈数据至关重要,以揭示为学生提供现有服务的优缺点。AI可以帮助确定教育基础设施、学习管理系统、教学和实践以及学习环境方面的进步领域。NLP技术在文本格式中分析学生反馈发挥着关键作用。本研究专注于可适应教育领域的现有NLP方法和应用程序,如情感标注、实体标注、文本摘要和主题建模。研究趋势和在教育中采用NLP的挑战进行了审查和探索。用现有方法解释NLP中的语境based挑战,如幽默、领域特定语言、歧义和 aspect-based情感分析,以克服这些挑战。研究社区的方法探索了提取反馈中的表情符号和特殊字符的语义意义,以及在教育中采用NLP的挑战。
URL
https://arxiv.org/abs/2301.08826
