Towards an Automatic Approach for Assessing Program Competencies
Bibliographic record
Abstract
Skills analysis is an interdisciplinary area that studies labor market trends and provides recommendations for developing educational standards and re-skilling efforts. We leverage techniques in this area to develop a scalable approach that identifies and evaluates educational competencies. In this work, we developed a skills extraction algorithm that uses natural language processing and machine learning techniques. We evaluated our algorithm on a labeled dataset and found its performance to be competitive with state-of-the-art methods. Using this algorithm, we analyzed student skills, university course syllabi, and online job postings. Our cross-sector analysis provides an initial landscape of skill needs for specific job titles. Additionally, we conducted a within-sector analysis based on programming jobs, computer science curriculum, and undergraduate students. Our findings suggest that students have a variety of hard skills and soft skills, but they are not necessarily the ones that employers want. The data also suggests these courses teach skills that are somewhat different from industry needs, and there is a lack of emphasis on soft skills. These results provide an initial assessment of the program competencies for a computer science program. Future work includes more data gathering, improving the algorithm, and applying our method to assess additional educational programs.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".