A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

TL;DR

This paper reviews current NLP techniques used for analyzing student feedback in education, highlighting trends, challenges, and potential solutions for improving educational feedback systems.

Contribution

It provides a comprehensive overview of NLP methodologies applied to educational feedback analysis, identifying key challenges and proposing directions for future research.

Findings

NLP techniques like sentiment analysis and topic modeling are effective in educational feedback.

Challenges include handling sarcasm, domain-specific language, and ambiguity.

Research on semantic interpretation of emoticons enhances feedback understanding.

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.