Eight years of computing education papers at NACCQ
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
Abstract: The 157 computing education papers from the past eight NACCQ conferences are categorised and summarised by a group of researchers from multiple institutions, with steps taken to measure and improve the consistency of classification. The papers are set predominantly in programming subjects, hardware/architecture/systems/ network subjects, and capstone projects. The bulk of the papers are about teaching/learning techniques, assessment techniques, teaching/learning tools, curriculum, and educational technology. Most of the papers are set within single subjects, a few in multiple subjects within a single program or department, and fewer still in a range of subjects across the whole institution or multiple institutions. Nearly a quarter of the papers either expound a position or outline a proposal; a large but diminishing proportion report on something such as a change of curriculum or approach; and a large and increasing proportion are clearly research papers, focusing on the analysis of data to answer an explicit research question.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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