Automating Articulation: Applying Natural Language Processing to Post-Secondary Credit Transfer

Abstract

Within the field of post-secondary student mobility, the assessment, and evaluation of transfer credit is a labor-intensive human intelligence task that is subject to time limits and human bias. This paper introduces a semi-automated approach to assessing transfer credit and generating articulation agreements between post-secondary institutions using natural language processing (NLP). The output from the NLP system is tested using a content expert generated an assessment of transfer credit between computer science programs at two separate post-secondary institutions. Initial testing with an unsupervised NLP algorithm, despite good results against standardized measures, assessed the percentage of course overlap as 71% similar to the percentages selected by human content experts. The application of an algorithm based on the Word2Vec model using domain-specific Wikipedia corpus and dependency parsing was applied to compensate for domain specific language and improved the relationship between content experts ratings and NLP output to 86% related overlap.

Keywords

• Articulation agreement
• natural language processing
• semantic similarity
• student mobility
• transfer credit
• word embeddings