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ABET Accreditation During and After COVID19 - Navigating the Digital Age
Engineering accreditation agencies and governmental educational bodies worldwide require programs to evaluate specific learning outcomes information for attainment of student learning and establish accountability. Ranking and accreditation have resulted in programs adopting shortcut approaches to co...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
Publicado: |
IEEE
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675559/ https://www.ncbi.nlm.nih.gov/pubmed/34976567 http://dx.doi.org/10.1109/ACCESS.2020.3041736 |
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collection | PubMed |
description | Engineering accreditation agencies and governmental educational bodies worldwide require programs to evaluate specific learning outcomes information for attainment of student learning and establish accountability. Ranking and accreditation have resulted in programs adopting shortcut approaches to collate cohort information with minimally acceptable rigor for Continuous Quality Improvement (CQI). With tens of thousands of engineering programs seeking accreditation, qualifying program evaluations that are based on reliable and accurate cohort outcomes is becoming increasingly complex and is high stakes. Manual data collection processes and vague performance criteria assimilate inaccurate or insufficient learning outcomes information that cannot be used for effective CQI. Additionally, due to the COVID19 global pandemic, many accreditation bodies have cancelled onsite visits and either deferred or announced virtual audit visits for upcoming accreditation cycles. In this study, we examine a novel meta-framework to qualify state of the art digital Integrated Quality Management Systems for three engineering programs seeking accreditation. The digital quality systems utilize authentic OBE frameworks and assessment methodology to automate collection, evaluation and reporting of precision CQI data. A novel Remote Evaluator Module that enables successful virtual ABET accreditation audits is presented. A theory based mixed methods approach is applied for evaluations. Detailed results and discussions show how various phases of the meta-framework help to qualify the context, construct, causal links, processes, technology, data collection and outcomes of comprehensive CQI efforts. Key stakeholders such as accreditation agencies and universities can adopt this multi-dimensional approach for employing a holistic meta-framework to achieve accurate and credible remote accreditation of engineering programs. |
format | Online Article Text |
id | pubmed-8675559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
spelling | pubmed-86755592021-12-29 ABET Accreditation During and After COVID19 - Navigating the Digital Age IEEE Access Education Engineering accreditation agencies and governmental educational bodies worldwide require programs to evaluate specific learning outcomes information for attainment of student learning and establish accountability. Ranking and accreditation have resulted in programs adopting shortcut approaches to collate cohort information with minimally acceptable rigor for Continuous Quality Improvement (CQI). With tens of thousands of engineering programs seeking accreditation, qualifying program evaluations that are based on reliable and accurate cohort outcomes is becoming increasingly complex and is high stakes. Manual data collection processes and vague performance criteria assimilate inaccurate or insufficient learning outcomes information that cannot be used for effective CQI. Additionally, due to the COVID19 global pandemic, many accreditation bodies have cancelled onsite visits and either deferred or announced virtual audit visits for upcoming accreditation cycles. In this study, we examine a novel meta-framework to qualify state of the art digital Integrated Quality Management Systems for three engineering programs seeking accreditation. The digital quality systems utilize authentic OBE frameworks and assessment methodology to automate collection, evaluation and reporting of precision CQI data. A novel Remote Evaluator Module that enables successful virtual ABET accreditation audits is presented. A theory based mixed methods approach is applied for evaluations. Detailed results and discussions show how various phases of the meta-framework help to qualify the context, construct, causal links, processes, technology, data collection and outcomes of comprehensive CQI efforts. Key stakeholders such as accreditation agencies and universities can adopt this multi-dimensional approach for employing a holistic meta-framework to achieve accurate and credible remote accreditation of engineering programs. IEEE 2020-12-01 /pmc/articles/PMC8675559/ /pubmed/34976567 http://dx.doi.org/10.1109/ACCESS.2020.3041736 Text en This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ |
spellingShingle | Education ABET Accreditation During and After COVID19 - Navigating the Digital Age |
title | ABET Accreditation During and After COVID19 - Navigating the Digital Age |
title_full | ABET Accreditation During and After COVID19 - Navigating the Digital Age |
title_fullStr | ABET Accreditation During and After COVID19 - Navigating the Digital Age |
title_full_unstemmed | ABET Accreditation During and After COVID19 - Navigating the Digital Age |
title_short | ABET Accreditation During and After COVID19 - Navigating the Digital Age |
title_sort | abet accreditation during and after covid19 - navigating the digital age |
topic | Education |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675559/ https://www.ncbi.nlm.nih.gov/pubmed/34976567 http://dx.doi.org/10.1109/ACCESS.2020.3041736 |
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