Empowering Students through Elective Grading in a University Setting
Bibliographic record
Abstract
In undergraduate university courses, the assessment methods often lack variety, which can lead to significant stress for both students and educators. It is becoming increasingly apparent that incorporating a range of assessment types could alleviate this stress and better accommodate diverse learning styles (Leite et al., 2010). Elective Grading (EG) is an approach to assessment that empowers students to determine their own grade weighting, based on their own learning goals and progress. EG can be implemented by using simple algebraic formulas to increase or decrease the original grade by the amount elected by the student. Using computer-based spreadsheet technology, EG can be included in a dynamic system that responds to the student's work, rather than relying solely on the instructor's evaluation. This article explains the rationale behind adopting an EG system, exploring a different option for students to re-weigh tests and assignments to reduce the perceived impact of each assessment, with no grade inflation. This flexible approach can mitigate student stress and anxiety, and practical strategies for its implementation across the curriculum. EG can enhance student learning and engagement from both the instructor's and the students’ perspectives. Students can use EG to adapt their own assessment preferences that may reduce stress and improve learning outcomes.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".