  The recent pandemic has changed the way we see education. It is not
surprising that children and college students are not the only ones using
online education. Millions of adults have signed up for online classes and
courses during last years, and MOOC providers, such as Coursera or edX, are
reporting millions of new users signing up in their platforms. However,
students do face some challenges when choosing courses. Though online review
systems are standard among many verticals, no standardized or fully
decentralized review systems exist in the MOOC ecosystem. In this vein, we
believe that there is an opportunity to leverage available open MOOC reviews in
order to build simpler and more transparent reviewing systems, allowing users
to really identify the best courses out there. Specifically, in our research we
analyze 2.4 million reviews (which is the largest MOOC reviews dataset used
until now) from five different platforms in order to determine the following:
(1) if the numeric ratings provide discriminant information to learners, (2) if
NLP-driven sentiment analysis on textual reviews could provide valuable
information to learners, (3) if we can leverage NLP-driven topic finding
techniques to infer themes that could be important for learners, and (4) if we
can use these models to effectively characterize MOOCs based on the open
reviews. Results show that numeric ratings are clearly biased (63\% of them are
5-star ratings), and the topic modeling reveals some interesting topics related
with course advertisements, the real applicability, or the difficulty of the
different courses. We expect our study to shed some light on the area and
promote a more transparent approach in online education reviews, which are
becoming more and more popular as we enter the post-pandemic era.

Computers and Society;Computation and Language