Bibliographic record
Abstract
OBACIS is a tri-platform outcome-based assessment and continues improvement system. The system is composed of three integrated platforms/applications: a Win app, an Excel app, and a Web-app. In this paper, a Closed-Loop Teaching and Learning Framework (CTLF) is presented. The framework is composed of two layers: A program layer and a course layer. At the heart of the program layer lies the program-level closed-loop teaching and learning process (P-CTLP) with an overarching process concerned with the analytics and the continuous improvement activities located in the feedback loop. P-CTLP has several inputs or constituents that affect its quality outcomes, namely the successful engineering graduates and the continuously improving program outcomes, a.k.a. Graduate Attributes. At the course layer, there is a simplified course-level closed-loop teaching and learning process (C-CTLP) with its simplified feedback loop, inputs and outputs. P-CTLP and C-CTLP are a sort of master-detail closed-loop continuous improvement system. C-CTLP is managed and controlled by individual faculty members, while higher administrative and executive staff manages P-CTLP and the overall CTLF. Since the outcome-based accreditation is still locked in the data collection phase, the framework presented will be the foundation for creating a data-driven analytics engine and a continued improvement system at the program and faculty levels. The proposed framework should be instrumental in justifying the effort and the investment spent on the new outcome-based accreditation process.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".