Changing Bimodal Grade Distributions – A Missed Opportunity?
Bibliographic record
Abstract
Bimodal grade distributions indicate a gap in learning, where the highest quartile of students is skilful in the subject matter, but the lowest quartile is not retaining course material that demonstrates a good level of understanding. Students in the lower quartile do not necessarily have the same challenges as remedial students (i.e., those that do not meet the minimum course requirement) and should therefore be directed differently. We suggest that it is important to consider which elements of the course can be modified to reduce or eliminate bimodality. We provide here an approach to detect bimodality, explore the causes, and provide potential solutions that could be applied to any course. Our case study is on a third-year biochemistry course where several semesters showed a bimodal grade distribution. While student composition and timing of the course may have contributed to this result, underlying causes that can be controlled by the instructor include the lack of student engagement and academic motivation. Increasing the opportunities to earn marks and receive feedback, adding online components using social media, implementing seminars, and training teaching assistants to lead seminars can help reduce this problem.
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 | 0.010 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".