  The identification and analysis of student satisfaction is a challenging
issue. This is becoming increasingly important since a measure of student
satisfaction is taken as an indication of how well a course has been taught.
However, it remains a challenging problem as student satisfaction has various
aspects. In this paper, we formulate the student satisfaction estimation as a
prediction problem where we predict different levels of student satisfaction
and infer the influential predictors related to course and instructor. We
present five different aspects of student satisfaction in terms of 1) course
content, 2) class participation, 3) achievement of initial expectations about
the course, 4) relevancy towards professional development, and 5) if the course
connects them and helps to explore the real-world situations. We employ
state-of-the-art machine learning techniques to predict each of these aspects
of student satisfaction levels. For our experiment, we utilize a large student
evaluation dataset which includes student perception using different attributes
related to courses and the instructors. Our experimental results and
comprehensive analysis reveal that student satisfaction is more influenced by
course attributes in comparison to instructor related attributes.
