Early Computer Science Students' Perspectives Towards The Importance Of Writing
Bibliographic record
Abstract
Faculty and industry practitioners recognize written communication to be important in computer science, but it can be challenging to convince students of the same. As student perceptions are molded early in a program of study, we focus on early-year CS students to understand their perceptions towards the importance of writing in CS, with the goal of framing discipline-specific writing pedagogy. We qualitatively analyze responses from first and second-year CS students in a survey about the role of writing in their field. The responses reveal that a majority view writing as an indispensable skill. Specifically, students recognize it as a fundamental skill, applicable across diverse contexts, and uniquely relevant in CS compared to other fields. We identified 4 perceptions that they hold which are helpful to their development as writers: that writing is a useful fundamental skill, which is useful for achieving various goals in a variety of contexts, and that writing in CS is different than in other fields. However, 20% of responses include reasons why writing is not important in CS, and we identify 4 perceptions harmful to students' development as writers: that writing skills can be avoided, are defined narrowly, do not need to be developed beyond a baseline, and come at the cost of computing skills. We believe that there is an opportunity to align discipline-specific writing instruction with these useful and harmful perceptions.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".