An Excel Add-in for Accreditation Data Collection and Auto Grading Sheets (AGS): A Canadian Experience
Bibliographic record
Abstract
In this paper, an Excel Add-in or Xl-App for automating grade recording and graduate attributes assessment at the course level is presented. The Xl-App is one of the three major constituents of the OBACIS System. At the course level, the purpose of the Xl-App is twofold: 1) cutting down the grade compilation and accreditation reporting time and effort by an order of magnitude using the built-in OBACIS accreditation and grading sheets (AGS) module 2) introducing an advanced tool for data-driven continuous improvement (DCI) for enhancing the teaching and learning experience at both course and program levels. The app has a third module used to collect additional information related to accreditation reporting. This information is required by the course information sheets (CIS) mandated by CEAB accreditation questionnaire. The Win-app has a dedicated module that serves that purpose called OBACIS Catalogs. The Xl-App is capable of emitting the data collected in both XLSX and XML formats. The data collected can be easily exported to learning management systems (LMS), grade books, and web marking systems. The OBACIS Win-App can easily parse the data collected from different faculty members using the Xl-App in their raw excel format and integrate them together to generate unified program and faculty-level assessment reports that can be utilized in generating top-down continuous improvement action plans. The Xl-App has been in implementation since early 2015. It had remarkable impact on enhancing the teaching and learning experience of a handful of courses taught by the author. The App improved the robustness of course grading and saved a tremendous amount of time needed for grade and accreditation reporting.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".