Using Coding Interviews as an Organizational and Evaluative Framework for a Graduate Course in Programming
Bibliographic record
Abstract
Objective: In a Statistical Analysis System (SAS) coding interview, job applicants are typically presented with data management and data analysis problems and asked to solve them using the programming language of SAS. Interviewers not only assess technical competence, but also algorithm design and more generally how applicants approach computer programming. In the language of constructivism, the problems are designed to assess the depth and soundness of the applicant’s mental model of SAS programming. We asked whether a SAS course, embedded within a Master of Biostatistics program, could reasonably be structured using a coding interview for the final examination as its organizing framework. Methods: This is a case study, where we describe how our content delivery was structured in order to prepare students for their coding interviews. It additionally relies on the metaphor of learning a second language through immersion. Results: Using a constructivist approach enhanced with active learning exercises, a course could in fact be designed around a coding interview. Course content can be mapped to the metaphor of foreign language immersion. Student response has been positive, and the formative evaluation has been encouraging to date. Conclusions: Coding interviews are a novel and potentially promising way to design a course in SAS programming.
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 | 0.140 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".