Beyond Assessment: Assuring Student Learning in Higher Education.
Bibliographic record
Abstract
Setting up an 'Assurance of Learning' (AoL) system in line with requirements for accreditation is generally perceived to be a challenging task in both theory and practice. This paper provides an overview of the AoL system developed by the Faculty of Commerce and Administration to meet the requirements for accreditation by the Association to Advance Collegiate Schools of Business (AACSB), and describes its rationale, results achieved to date, and current challenges. The Faculty's system draws on the use of graduate attributes (Barrie, 2004), constructive alignment (Biggs, 1999), quality systems (Deming, 1982) and Theory of Constraints (Goldratt, 1994). In particular, individual student assessment is used to provide programme-level assurance of learning of graduate attributes. AoL's focus on 'closing the loop' – using student cohort performance data to inform system level change so that more students achieve the overall programme-level learning goals – is illustrated through a number of examples. While AoL developments have been led largely by business schools, we argue that wider adoption would allow universities to back up their claims about their students' achievement of graduate attributes, moving towards assuring, not just assessing, student learning.
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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 | 0.035 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".