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This research references past work which indicates that the major driving force of outcomes assessment initiatives in engineering institutions has been regional and specialized accreditation standards. Continuous quality improvement and accreditation-based activity at various engineering institutions remain as relatively isolated processes, with realistic continuous quality improvement efforts maintaining minimal reference to learning outcomes assessment data measured for accreditation. The lack of utilization of digital technology and appropriate methodologies supporting the automation of outcomes assessment further exacerbate this situation. Furthermore, learning outcomes data measured by most institutions is rarely classified into all three domains of the revised Bloom’s taxonomy and their corresponding categories of the levels of learning. Generally institutions classify courses of a program curriculum into three levels: introductory, reinforced and mastery. The outcomes assessment data is measured for mastery level courses in order to streamline the documentation and effort needed for an effective program evaluation. A major disadvantage of this approach is that it does not facilitate early remediation of performance failures because necessary outcomes information related to deficient teaching and learning mechanisms is measured only for mastery level courses. A holistic approach for continuous quality improvement in academic learning would require a systematic measurement of performance indicators in all three domains and their corresponding categories of learning levels for all course levels in a given program’s curriculum. In this research, we present an innovative methodology for engineering program evaluation utilizing significant customization implemented in a web-based software, EvalTools® 6. Unique curricular assessments implementing scientific constructive alignment are utilized for the measurement of specific performance indicators related to ABET student outcomes. Performance indicators are classified according to the three domains of the revised Bloom’s taxonomy and their corresponding categories of learning levels. Final values of ABET student outcomes used as a performance index in program term reviews are obtained based on calculations applying an intelligent weighted averaging algorithm to associated performance indicators. The weights are related to the numerical counts of performance indicators measured for the different levels of learning for each of the three domains in multiple course levels. Analytical information related to the performance indicators measured for multiple course levels, their distribution in each of the learning domains, and corresponding categories of learning levels provide valuable information that helps identify specific areas for improvement in the education process.

Engineering Program Evaluations Based on Automated Measurement of Performance Indicators Data Classified into Cognitive, Affective, and Psychomotor Learning Domains of the Revised Bloom's Taxonomy