International Trends in Engineering Education Accreditation ( III ): Ams Big Data Empirical Research, and Benchmarking Analysis Within Washington Accord Framework

Project Details


While the Washington Accord (WA) is the largest and most important international accreditation framework, and also arguably the most influential, in Taiwan WA accreditation is still a relatively new issue. Implementation of this international collaborative research project began in 2013; this particular plan will be implemented in the third year of the three-year project. The main focus of last year5s research was on two key areas. As regards international collaboration and exchange, the applicant undertook a comparison of the core capabilities of students who have graduated from institutions holding the European EAFS FC and EAFS SC engineering education accreditation with the core capabilities of students who have graduated from institutions holding the corresponding WA and Sydney Accord (SA) accreditation, with the aim of exploring how the WA and EUR-ACE accreditation frameworks can be harmonized. The work undertaken in the third year of the project will build on the results obtained in the first two years, seeking to establish an international benchmarking analysis framework and methodology with respect to graduates5 core capabilities. The planned implementation period for this international collaborative research project is from August 2015 to July 2018; the anticipated results of the international collaboration include: (1) The establishment of cross-framework benchmarking analysis principles and frameworks for comparison of the WA accreditation framework and the accreditation framework currently used in Taiwan; (2) The completion of trans-national benchmarking analysis with respect to the WA signatory nations; (3) The holding of a big data data-mining methodology workshop and the establishment of a mechanism for ongoing improvement of department and graduate institute accreditation quality.One of the research results achieved in the first two years of implementation of this project was the completion of a set of rubrics for the assessment of graduates5 core competencies, based on the latest international standards for graduate attributes and professional competency. While the WA provides clear, precise guidelines, the applicant found that, in order to realize the continuingimprovement in engineering accreditation quality assurance that was the main focus of the first two years’ research, there is an urgent need for systematic evaluation in order to gain a better understanding of current trends in engineering and technical education accreditation. The key to effective quality assurance lies in the systematic collection of large quantities of time-series data, and in the judging the value regarding the quality of engineering education; there is an urgent need for more effort to be expended in regard to engineering education accreditation today.The methodology used in this project will build on the theoretical foundations provided by the use of engineering education big data in Lattuca (2006). Data mining techniques will be used to analyze data from the AMS accreditation database platform, with the aim of performing benchmarking analysis and establishing best practice. The main data source used will be the IEET’s Accreditation Management System (AMS) accreditation database; the research targets a panel of accredited engineering programs at universities and colleges in Taiwan, with sample data divided into two broad categories: (a) Factors relating to the school’s resources; (b) Factors relating to teaching. The project clearly outlines the big data analysis techniques to be used, the sources of data for analysis, and the intended data items, with the aim of clarifying the steps to be implemented in the process of data mining and analysis. There is also a detailed explanation of the three-year timetable for project implementation and of the key auditing items for each year of implementation, to serve as a basis for mid-term and end-of-period review and for continuous improvement over the entire three-year implementation
Effective start/end date1/08/1631/10/17

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 4 - Quality Education
  • SDG 8 - Decent Work and Economic Growth
  • SDG 17 - Partnerships for the Goals


  • Washington Accord
  • engineering education accreditation
  • big data
  • benchmarking analysis
  • international collaboration


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