Innovative assessment and grading practices in higher education: a critical exploration for management educators
Bibliographic record
Abstract
Assessment and grading are among the most influential factors shaping student learning in higher education. Current practices are increasingly criticized for their limited capacity to promote deep learning and for their misalignment with pressing challenges such as academic integrity in the age of generative artificial intelligence, massification, equity, and the assurance of learning. This paper examines five innovative assessment and grading practices—authentic assessment, self- and peer-assessment, reassessment, standards-based grading, and ungrading—identified through the higher education literature. It then explores their uptake and potential in management education through a critical review of 58 assessment-related articles published since 2005 in four leading management education journals. While self- and peer-assessment are widely discussed, the other practices remain largely absent from the mainstream management education discourse. We argue that management education scholars and practitioners should engage more actively with assessment innovation, both to benefit from insights developed in the broader higher education field and to contribute meaningfully to that ongoing conversation. • Review five innovative assessment and grading practices in higher education. • Evaluates their relevance and uptake in management education literature. • Provides practical recommendations for management educators to begin experimenting.
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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 | 0.149 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".