Perspectives on the Student Transition into CS1
Bibliographic record
Abstract
As students transition into higher education, their experience can be quite new and foreign to them. This experience, while an individual one, consists of many concerns that are shared amongst these transitioning students. Partly as a result of these concerns, retention rates of first year Computer Science students suffer. Members of this panel have been involved in multi-year studies across Scotland [2, 5] as well as an international study [3] looking at the student experience as they transition into undergraduate Computer Science. This panel hopes to discuss the transition into CS1 and implications for Computer Science retention rates from varied international perspectives as well as through the lens of online learning. It further hopes to discuss new ways that we might be able to better support first year CS student retention in light of collected transition data, as well as the current state of any implemented recommendations in previous work (you may want to rephrase this).
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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".