What are We Asking our Students? A Literature Map of Student Surveys in Computer Science Education
Bibliographic record
Abstract
Many research papers pull data from student surveys. But are those surveys well designed? Are the questions used validated? Are the results comparable across studies? What exactly are we asking our students? In this work, we performed a systematic literature map of the past 15 years of papers in the three main conferences sponsored by the ACM Special Interest Group on Computer Science Education: International Computing Education Research (ICER), Innovation and Technology in Computer Science Education (ITiCSE), and the Special Interest Group on Computer Science Education Technical Symposium (SIGCSE). We search for all papers referring to student surveys or questionnaires. Out of 1313 papers analyzed, 42 papers referred to surveys containing general questions applicable to many or all computer science students. Our analysis showed that many papers were using surveys to extract similar types of information, such as demographics, prior experience or motivation to study computer science. However, the questions were being asked in different ways, using different scales, thus making it difficult or impossible to compare survey results between studies. We further found that while some studies based their questions on well-validated surveys, or at least shared their questions for possible later validation, approximately half of the papers found neither validated their questions, nor shared them to allow for post-hoc validation.
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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 | 0.069 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.107 | 0.088 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".