Administrative Considerations Pertaining to the Use of Creative Methods of Student Assessment: A Theoretically Grounded Reflection from a Master of Biostatistics Program
Bibliographic record
Abstract
Student evaluation is a key consideration for educational program administrators because program success depends on students’ ability to demonstrate successful development of core competencies. Student evaluations must therefore be aligned with learning objectives and overall program goals. Graduate level educational programs typically incorporate course-level and program-level evaluations, e.g., a final examination in a single course vs. a qualifying examination that assesses knowledge gained from several courses. While there is often considerable attention given to the structure of these evaluations at the program level, the format is typically left to the instructor’s discretion. We argue in this article that there are administrative advantages to encouraging instructors to adopt creative forms of assessment that extend beyond the typical concerns related to program structure. Specifically, we argue that advantages can be gained in terms of increasing student engagement, adding real world context to student evaluations, maintaining positive program culture, and reducing the opportunity for cheating. We present two examples of creative assessments implemented in a 2-year Master of Biostatistics program, along with a discussion of three key questions administrators should consider as they work with instructors to develop innovative assessment methods: (1) what changes to make; (2) in what order to make those changes; and (3) how to consult with instructors about making those changes.
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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 | 0.148 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.011 | 0.038 |
| Insufficient payload (model declined to judge) | 0.002 | 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".