A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data

Abstract

There has been a significant increase in academic processes to ensure the quality of educational resources such as curricula, examinations, and educational content. This has drawn attention to studies exploring the use of text mining, learning machines, and auto-analytic tools like natural language processing (NLP) to interpret and evaluate the quality of these resources. The study utilized a methodical approach to survey the NLP techniques for extracting syntactic and semantic features for analyzing and understanding educational contents. The study identified the challenges and strengths of traditional and advanced approaches utilized in feature extraction. This review has benefits for stakeholders such as regulatory bodies, researchers, higher education institutes, and NLP researchers. The study provides NLP researchers with the current strengths and weaknesses of document analysis as well as the accumulated evidence skills for NLP-based application developers, which improves their ability to develop the appropriate algorithm and techniques for NLP tasks