Developing Competency Statements for Computer Science Curricula
Bibliographic record
Abstract
This Working Group aims to take the current approved Computer Science curricula document, CS2013, and redevelop it into competency statements. The CC2020 project has designed and built a prototype of a visualization tool to compare and contrast current computing curricula. Three basic approaches were taken to portray the base data that will be used for the tool: the first being expert-defined competencies, the second based on mining, and the third based on expert-defined knowledge areas. The visualization tool takes competency statements from each of the current approved computing curricula and visually represents them. Using competency to frame curricula and describe educational outcomes in computing is not new. Since the CC2005 report was published several additional curricula have appeared and the information technology, information systems, and software engineering communities have developed three approaches to defining computing competency in the context of developing their curricula reports. In future the CC2020 report advocates that all new curricula will be written as competency statements. Currently the CS2013 curricula is expressed in learning outcomes rather than the competency statements, so it is essential to be able to demonstrate Computer Science curricula in these new terms to accommodate the new direction and demonstrate Computer Science in the new visualization tool.
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 | 0.024 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".