  In recent years, Massive Open Online Courses (MOOCs) have gained significant
traction as a rapidly growing phenomenon in online learning. Unlike traditional
classrooms, MOOCs offer a unique opportunity to cater to a diverse audience
from different backgrounds and geographical locations. Renowned universities
and MOOC-specific providers, such as Coursera, offer MOOC courses on various
subjects. Automated assessment tasks like grade and early dropout predictions
are necessary due to the high enrollment and limited direct interaction between
teachers and learners. However, current automated assessment approaches
overlook the structural links between different entities involved in the
downstream tasks, such as the students and courses. Our hypothesis suggests
that these structural relationships, manifested through an interaction graph,
contain valuable information that can enhance the performance of the task at
hand. To validate this, we construct a unique knowledge graph for a large MOOC
dataset, which will be publicly available to the research community.
Furthermore, we utilize graph embedding techniques to extract latent structural
information encoded in the interactions between entities in the dataset. These
techniques do not require ground truth labels and can be utilized for various
tasks. Finally, by combining entity-specific features, behavioral features, and
extracted structural features, we enhance the performance of predictive machine
learning models in student assignment grade prediction. Our experiments
demonstrate that structural features can significantly improve the predictive
performance of downstream assessment tasks. The code and data are available in
\url{https://github.com/DSAatUSU/MOOPer_grade_prediction}
