Bibliographic record
Abstract
In this paper, an integrated system for outcome-based assessment and continuous improvement is presented. The system is designed and implemented as a suite of three integrated Apps: An Excel-App for creating Auto Grading Sheets (AGSs); a Web-App for building assessmenttrees, updating server database(s), uploading associated documents, and conducting surveys; and a Win-App for program-wide and faculty-wide OBA data compilation, performance analysis, and data-informed continuousimprovement. The proposed system adopts a bottom-up approach for building assessment trees that define the structure and the smart logic embedded in AGSs. Some course assessment activities, possibly all, are mapped to graduate attributes, more precisely indicators, and course learning outcomes. The proposed system analyzes the collected datafrom three different views: 1) Categorical Analysis view (CAs), 2) Learning Outcomes Analysis view (LOAs), and 3) Graduate Attributes Analysis (GAAs) view. The paper presents some principles related to the proposed system, demonstrates its multiple user interfaces, and digs more intoOBA analytics and its proposed closed-loop continues improvement process. The objective of the proposed system and its underlying framework is to set new grounds for the accreditation process by making it more appealing, more economical, and more fruitful for all involved stakeholders.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".