  As the demand for programming skills grows across industries and academia,
students often turn to Programming Online Judge (POJ) platforms for coding
practice and competition. The difficulty level of each programming problem
serves as an essential reference for guiding students' adaptive learning.
However, current methods of determining difficulty levels either require
extensive expert annotations or take a long time to accumulate enough student
solutions for each problem. To address this issue, we formulate the problem of
automatic difficulty level estimation of each programming problem, given its
textual description and a solution example of code. For tackling this problem,
we propose to couple two pre-trained models, one for text modality and the
other for code modality, into a unified model. We built two POJ datasets for
the task and the results demonstrate the effectiveness of the proposed approach
and the contributions of both modalities.
