A proposed NCAAA-based approach to the self-evaluation of higher education programs for academic accreditation: A comparative study using TOPSIS

Quality standards must be fulfilled to satisfy a base level of quality. Despite using this idea as a foundation, evaluations of academic programs still rely on the evaluators' experiences and may differ from one evaluator to the next. As a result, more precise evaluation approaches must be created to ensure quality is accurately reflected. The main goal of this research paper is to propose and evaluate an approach to assessing higher educational programs using the Self-Evaluation Scale (SES) developed by the Saudi National Commission for Academic Accreditation and Evaluation (NCAAA). The proposed approach is a breakdown of the original performance criteria and standards into sub-criteria and elements to ensure the required data quality. The second goal is to compare the NCAAA's original performance criteria and the proposed evaluation sub-criteria. A comparison framework that uses the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is developed. Data from eight programs offered in a Middle Eastern University was used for the application and comparison between the two evaluation approaches. Results show that both approaches provide different quality performance rankings. The proposed approach demonstrated more conservative and accurate overall quality performance ratings, indicating that application decisions for accreditation are affected.(c) 2023 by the authors; licensee Growing Science, Canada.

Automated summary

The main goal of this research is to propose and evaluate an approach using the Self-Evaluation Scale (SES) to assess higher educational programs. The proposed approach breaks down the original performance criteria into sub-criteria and elements to ensure data quality. The second goal is to compare the original performance criteria with the proposed evaluation sub-criteria using the TOPSIS method. Results show that the proposed approach provides more conservative and accurate quality performance ratings.