Are Simulated Coding Interviews a Fair and Practical Examination Format for Non-professional Programmers Enrolled in a Master’s Degree Program in Biostatistics?
Bibliographic record
Abstract
This report describes an innovative and evidence-based approach to implementing coding interviews as an examination format for non-professional programmers: namely, students in a Master of Biostatistics program taking a course in the language of SAS. In addition to its academic purpose, the coding interview examination also serves as practice for what our students will likely encounter when interviewing for jobs after graduation. We discuss our experience with coding interviews as an examination format in light of two questions: "Is it fair?" and "Is it practical?". We propose that the answer to both questions is "yes". A detailed description of the exam goals and structure is provided, along with sample questions, model answers, and a brief discussion of the rationale for each question. We also review student feedback from the course evaluation and summarize our conclusions related to fairness and practicality.
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How this classification was reachedexpand
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".