Intelligent Decision Support in Automating ABET Accreditation Processes: A Conceptual Framework

In the era of knowledge-based decision-making, there is an increasing need for administrators and instructors of engineering degree programs to make informed decisions on the currency, relevancy, and efficacy of instructional efforts. Academic accreditation through ABET provides the best practices and the means to establish the requisite quality improvement processes. Various regulatory and funding agencies for these engineering degree programs also actively call for accreditations. Nevertheless, the tedious, time-consuming, resource-intensive, and knowledge-based nature of these processes and decisions pronounces the need for a knowledge-based tool to intelligently support and automate various pertinent activities. Such activities span from information collection, aggregation, and analysis to decision analysis, support, and monitoring of outcomes. We propose a conceptual framework for researching, designing, and developing such a knowledge-based system. The proposed conceptual framework seeks to automate not only various tedious and complex activities but also facilitate knowledge-based decision analysis/support in continuous quality improvement processes. The focus is on the decisions, processes, and activities related to Course Assessments, Course Learning Outcomes, and Student Outcomes. This framework is expected to deliver systems that promise significant improvement in the efficiency and efficacy of instructors, administrators, and accreditors.