A Systematic Approach for Tool-Supported Performance Management of Engineering Education

Abstract

Performance management of engineering education emerges from the need to assure proper training of future engineers in order to meet the constantly evolving expectations and challenges for the engineering profession. The process of accreditation ensures that engineering graduates are adequately prepared for their professional careers and responsibilities by ensuring that they possess an expected set of mandatory graduate attributes. Engineering programs are required by accreditation bodies to have systematic performance management of their programs that informs a continuous improvement process. Unfortunately, the vast diversity of engineering disciplines, varieties of information systems, and the large number of actors involved in the process makes this task challenging and complex. We performed a systematic literature review of jurisdictions around the world who are doing accreditation and examined how universities across Canada, US and other countries, have addressed tool support for performance management of engineering education. Our initial systematic approach for tool supported performance management evolved from this, and then we refined it through an iterative process of combined action research and design science research. We developed a prototype, Graduate Attribute Information Analysis (GAIA) in collaboration with the School of Electrical Engineering and Computer Science at the University of Ottawa, to support a systematic approach for accreditation of three engineering programs. This thesis contributes to research on the problem by developing a systematic approach, a tool that supports it, a set of related data transformations, and a tool-assessment checklist. Our systematic approach for tool-supported performance management addresses system architecture, a common continuous improvement process, a common set of key performance indicators, and identifies the performance management forms and reports needed to analyze graduate attribute data. The data transformation and analysis techniques we demonstrate ensure the accurate analysis of statistical and historical trends.