Graph-based approach to model the dependency information of graduate attributes for supporting the accreditation process
Bibliographic record
Abstract
In the accreditation of an engineering program, the criterion of graduate attributes is particularly challenging due to its outcome-based nature, which involves diverse instructors to collect and analyze data on students’ skills and competencies in course activities. Also, the amount of data can be vast, causing the issues of relevance and consistency of the collected data. In this context, the purpose of this paper is to facilitate the relevant process by modeling the information dependency concerning the measurements of graduate attributes and the responsibilities of stakeholders. The modeling approach is based on the graph representation that focuses on the nodes and their relations. In the graph-based model, information contents are treated as nodes, which are classified into five types: graduate attribute (GA), attribute indicator (AI), program course (PC), learning outcome (LO), and grading component (GC). Then, the contextual interpretation of the GA assessments is specified by the relations that connect these content types. In this work, three types of content relations are defined: refine, measure and associate. Further, three types of stakeholders are identified (i.e., accreditor, administrator, and instructor), along with their relations to specify their responsibilities to the content types. To demonstrate the application of the proposed graph-based model, this paper overviews the use of the Integrated Course Design Tool (ICDT) and the course outline template in the accreditation process. Based on the graph-based model, suggestions are provided toward the development of quality function deployment and software tools.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".