A Graphically-Based Machine Learning Approach for Remote Learning Services

Abstract

Interactive learning is becoming increasingly important in the modern educational system. Ideally students should be able to expand on their knowledge, assess their progress and receive feedback from a remote location, outside the classroom. This research presents a graphically-based methodology to model the semantic structure of textual exchanges in the form of question and answer (Q/A). A machine learning approach is then presented which classifies questions and answers based on the similarities of their semantic structures. Because the methodology is graphically-based, similarities between graphs can be identified to establish context-free relationships/ associations between answers, or between questions and possible answers. By these means the relevant textual exchanges can be systematically analyzed and classified