Subjective Question Generation and Answer Evaluation using NLP
This addresses the problem of automating subjective educational assessments for teachers and students, but it appears incremental as it builds on existing objective question generation work.
The research tackled automated subjective question generation and answer evaluation from text input, aiming to improve or create NLP models for this purpose, with potential applications in teacher assessment and student self-assessment.
Natural Language Processing (NLP) is one of the most revolutionary technologies today. It uses artificial intelligence to understand human text and spoken words. It is used for text summarization, grammar checking, sentiment analysis, and advanced chatbots and has many more potential use cases. Furthermore, it has also made its mark on the education sector. Much research and advancements have already been conducted on objective question generation; however, automated subjective question generation and answer evaluation are still in progress. An automated system to generate subjective questions and evaluate the answers can help teachers assess student work and enhance the student's learning experience by allowing them to self-assess their understanding after reading an article or a chapter of a book. This research aims to improve current NLP models or make a novel one for automated subjective question generation and answer evaluation from text input.