A Case Where It Is Better to have an Unstandardized measure of the Right Construct than a Standardized Measure of a Related One: Application to Coding Interviews Within a Course in SAS Programming
Bibliographic record
Abstract
Interview-based examinations provide richer data than formats such as multiple choice and short answer, albeit at the cost of being less standardized. This describes administering a coding interview as the final examination in a class on SAS programming, plus primarily qualitative reflections. We conclude that when the goal is to assess facility with programming an interview-based examination should come into especial consideration. We argue that a coding interview measures the right thing, namely how well the student designs and writes SAS programs – which in turn depends on factors such as general programming literacy, critical SAS-specific knowledge, ability to design SAS programs, and the ability to engage in problem solving as part of the process of program development -- rather than something that is merely correlated with this core construct, as would be the case for the objective questions that are included within typical certification tests. In doing so it is not completely standardized, but sufficiently so. This examination format more closely matches how students engage in SAS programming in actual practice: for example, by incorporating web searching. Moreover, it has the innovative and desirable property of embedding instruction in addition to evaluation. Coding interviews are a time-intensive form of evaluation, but much more is learned about student performance, there is an opportunity to teach as you go, and the time is well spent.
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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 | 0.169 | 0.294 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| 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".