  Student mobility or academic mobility involves students moving between
institutions during their post-secondary education, and one of the challenging
tasks in this process is to assess the transfer credits to be offered to the
incoming student. In general, this process involves domain experts comparing
the learning outcomes of the courses, to decide on offering transfer credits to
the incoming students. This manual implementation is not only labor-intensive
but also influenced by undue bias and administrative complexity. The proposed
research article focuses on identifying a model that exploits the advancements
in the field of Natural Language Processing (NLP) to effectively automate this
process. Given the unique structure, domain specificity, and complexity of
learning outcomes (LOs), a need for designing a tailor-made model arises. The
proposed model uses a clustering-inspired methodology based on knowledge-based
semantic similarity measures to assess the taxonomic similarity of LOs and a
transformer-based semantic similarity model to assess the semantic similarity
of the LOs. The similarity between LOs is further aggregated to form course to
course similarity. Due to the lack of quality benchmark datasets, a new
benchmark dataset containing seven course-to-course similarity measures is
proposed. Understanding the inherent need for flexibility in the
decision-making process the aggregation part of the model offers tunable
parameters to accommodate different scenarios. While providing an efficient
model to assess the similarity between courses with existing resources, this
research work steers future research attempts to apply NLP in the field of
articulation in an ideal direction by highlighting the persisting research
gaps.
