Automating Articulation: Applying Natural Language Processing to Post-Secondary Credit Transfer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it