Recommendation of Learning Path Using an Improved ACO Based on Novel Coordinate System

Abstract

Intelligent methods are needed to organize the large amount of teaching and learning resources, one important aspect is to plan the learning path. According to the existing research, ant colony algorithm showed great advantages in learning path planning. Different from the traditional ant colony algorithm, Mahalanobis distance was adopted to calculate the distance between the data in the improved ACO. This paper proposed a method to recommend learning path using an improved ant colony algorithm based on a novel coordinate system. Also, In order to transform the unmeasurable concept map and information in syllabus into measurable data, a novel coordinate system was built to draw points which represent the teaching or learning units in it. The experimental results showed that this method can recommend an efficient learning path.