  Manually determining concepts present in a group of questions is a
challenging and time-consuming process. However, the process is an essential
step while modeling a virtual learning environment since a mapping between
concepts and questions using mastery level assessment and recommendation
engines are required. We investigated unsupervised semantic models (known as
topic modeling techniques) to assist computer science teachers in this task and
propose a method to transform Computer Science 1 teacher-provided code
solutions into representative text documents, including the code structure
information. By applying non-negative matrix factorization and latent Dirichlet
allocation techniques, we extract the underlying relationship between questions
and validate the results using an external dataset. We consider the
interpretability of the learned concepts using 14 university professors' data,
and the results confirm six semantically coherent clusters using the current
dataset. Moreover, the six topics comprise the main concepts present in the
test dataset, achieving 0.75 in the normalized pointwise mutual information
metric. The metric correlates with human ratings, making the proposed method
useful and providing semantics for large amounts of unannotated code.
