A COURSE RECOMMENDATION SYSTEM BASED ON LEARNER CAPACITY USING RESTRICTED BOLTZMANN MACHINE

Abstract

The education field has undergone a rapid transformation with the advent of online courses, that aid in skill acquisition and life long learning. However, in the present scenario, the major limitation of these courses is the high dropout rate. This is due to the poor course recommendation and the low learner in teraction associated with online courses. In order to reduce the dropout rate, course recommendation must be given based on the learning capacity of stu dents. In this weak learner interaction scenario, cognitive diagnosis, which is a psychometric technique used to discover the proficiency level of students in specific knowledge components could not be used. Instead, a variant of this technique called Multi-dimensional Item Response Theory (MIRT) can be in corporated in course recommendation systems to suitably represent learner’s learning state by obtaining implicit response on the followed course. In this work, course recommendation based on learner capacity is implemented by integrating MIRT into collaborative filtering, which is implemented using Restricted Boltzmann Machine. The Open University Analytics Dataset is used as the base experimental data. Experimental results and analysis show that this course recommendation system has better performance in terms of different quantitative metrics like precision and mean absolute error.