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

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

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...

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Bibliographic Details
Main Authors: Andrew Heppner, Atish Pawar, Daniel Kivi, Vijay Mago
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Articulation agreement
natural language processing
semantic similarity
student mobility
transfer credit
word embeddings
Online Access:https://ieeexplore.ieee.org/document/8685082/

Internet

https://ieeexplore.ieee.org/document/8685082/

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