Outcomes-Based Assessment and Lessons Learned in ABET-CAC Accreditation: A Case Study of the American University in the Emirates
Bibliographic record
Abstract
ABET accreditation is sought globally for engineering and technology academic programs due to the quality, added value, and competitiveness it adds to students, program, and the university locally, regionally, and globally. Aligning with its mission to prepare students as global citizens for future career aspirations and lifelong learning through quality teaching, the American University in the Emirates (AUE) focuses on outcome-based education to ensure the employability of graduates and hence soon realized the significance of the Accreditation Board of Engineering and Technology-Computing Accreditation Commission (ABET-CAC) standard toward the Computer Science (CS) program. While pursuing ABET accreditation was challenging, the outcome was positive, and currently, the Computer Science Program, with its two specializations in Network Security and Digital Forensics is ABET-accredited. The process required support from all units within the institution and was a great learning experience for all stakeholders. ABET draws generic requirements to be fulfilled by a program seeking accreditation without a detailed procedure to achieve them. However, there is little information about achieving these requirements, especially criterion 4: continuous improvement, which most programs fail to comply with according to ABET. This study presented a comprehensive and reproducible methodology that addresses our successful efforts in aligning the CS program with ABET-CAC requirements by emphasizing criterion 4. This article reported the evaluation of Student Outcomes number one and two for the academic year 2020–2021 through a comprehensive framework. The framework showed data collection, data reporting and analysis, actions, and recommendations for the next academic cycle. The framework showed a mathematical model for calculating the Student Outcomes (SOs) attainment based on the mapped Course Learning Outcomes (CLOs). Finally, the recommendations were reported. We believe this article established a solid foundation that would be beneficial for insinuations pursuing ABET accreditation.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".