Approaching the Loop: A Brief Review of Effective Practises in Continuous Program Improvement
Bibliographic record
Abstract
Using the results of outcomes basedassessment for the purposes of continuous improvement,or closing the loop, is a frequent topic of discussion inhigher education, and is becoming more commonplaceamongst Canadian engineering programs. There havebeen several organizations and institutions in the UnitedStates that have been investigating outcomes assessmentand how institutions use the data for improvementpurposes. Most notable of these are the National Institutefor Learning Outcomes Assessment and the schoolsparticipating the in the Wabash Study. Despite theseinvestigations and discussions, there is no clearconsensus of what a functioning closed loop resembles,due to the diversity that exists between one institution andthe next. Ultimately it will be the decision of an individualinstitution as to what the final process will resemble, butthere are some key or effective practises for continuousimprovement that can help institutions guide and shapetheir approach to closing the loop.This paper will briefly review the current landscape incontinuous improvement in higher education, and presenteffective practises, common themes and techniques forclosing the loop. The intent of this paper is to provide aresource collection of effective practises to help develop ameaningful, sustainable and practical data-informedcontinuous improvement process with a focus onengineering.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".