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.
