Abstract:
Programming online judges (POJs) are an emerging application scenario in e-learning
recommendation areas. Specifically, they are e-learning tools usually used in
programming practices. Usually, they contain a large collection of such problems, to be
solved by students at their own personalized pace. These online judges play an
outstanding role for any student who wants to practice programming. Basically, these
online judge platforms provide various types of programming problems made by
many problem setters. Sometimes these problems are categorized by problem tag. The
more problems in the online judges the harder the selection of the right problem to solve
according to previous users' performance, causing information overload and widespread discouragement.
Many times, students try problems that are difficult than their skill level. Also, it is very
troublesome for them to find-out suitable problems that match with his/her skill levels.
These issues have not been addressed in any previous research works so we end up with
a solution in that we use machine learning and recommendation algorithms.
These research works present a non-personalized and content-based collaborative
filtering technique to mitigate this issue by suggesting a programming problem.
