Instructor-aided asynchronous question answering system for online education and distance learning

Abstract

Question answering systems have frequently been explored for educational use. However, their value was somewhat limited due to the quality of the answers returned to the student. Recent question answering (QA) research has started to incorporate deep natural language processing (NLP) in order to improve these answers. However, current NLP technology involves intensive computing and thus it is hard to meet the real-time demand of traditional search. This paper introduces a question answering (QA) system particularly suited for delayed-answered questions that are typical in certain asynchronous online and distance learning settings. We exploit the communication delay between student and instructor and propose a solution that integrates into an organization's existing learning management system. We present how our system fits into an online and distance learning situation and how it can better assist supporting students. The prototype system and its running results show the perspective and potential of this research.