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

Abstract

He has received specialized Quality Leadership Training at LSI Corporation and received an award LSI Corporation Worldwide Operations Review 1999 for his significant contributions to the Quality Improvement Systems. At LSI Wajid was the PE in charge of the world famous APPLE IPOD 2000-2001 processor WW qualification/production. Over the years Wajid has managed several projects related to streamlining operations with utilization of state of the art technology and digital systems.This has given him significant experience working with ISO standard quality systems.He is a specialist on ABET accreditation procedures and was appointed by the Dean of Engineering, KFUPM, Hafr Al Batin campus to lead the intensive effort of preparing the EEET program for the ABET Evaluators Team site visit in 2013. EEET received excellent comments for the display materials presented by Dr. Subal Sarkar ABET team chair which was managed to completion by Wajid. He is Digital Integrated Quality Management Systems Expert for Automated Academic Student Outcomes based Assessments MethodologyHe has taught several courses on electronics, microprocessors, electric circuits, digital electronics and instrumentation. He has conducted several workshops at the IU campus and eslewhere on Outcomes Assessment best practices, OBE, EvalTools R 6 for faculty, E learning with EvalTools R 6 for students, ABET accreditation process.He is a member of SAP Community, ISO 9001, Senior Member IEEE, IEEE Qatar, ASEE and REED MEP professionals International & Middle East.Dr. Fong K. Mak P.E., Gannon University Abstract: 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 requi...

La evaluación en educación superior con fines de acreditación de alta calidad a través de un modelo sistémico con teoría de redes

“…) Más allá del tratamiento de las relaciones al interior de las organizaciones, el uso de redes se ha expandido a sistemas macro en los que interactúan varias personas o instituciones, y desde ese enfoque se descubren propiedades del conjunto que antes no eran visibles en cada una de las partes (Borgatti & Foster, 2003). En el contexto específico de la calidad, el uso de redes es de gran utilidad para establecer una ponderación en un enfoque general que no necesita asumir, a priori, la medición del logro educativo como lo más importante, tal como sucede en los modelos de acreditación estadounidenses (Hussain, Mak, & Addas, 2016). Este problema reviste gran importancia en la gestión de la calidad en tanto ayuda a contener el problema de las fortalezas sobredimensionadas de las que se comentó al principio, porque puede suceder que se encuentren en aspectos de menor importancia.…”

An Expert Comparison of Accreditation Support Tools for the Undergraduate Computing Programs

“…At the heart of quality management in education is the continuous improvement [21]. A longitudinal study of an Irish university demonstrated the need for external reviews, internal expertise, as well as training and senior management commitment as key elements for the successful realization of continuous improvement in higher education institutions [7].…”

“…In relation to our research, [14] utilized CLOSO, which is developed in Visual Basic .NET, to demonstrate the assessment of course outcomes and student outcomes in an automatic fashion in a bid to reduce instructors' time and efforts. Finally, EvalTools is detailed in [21] as a web-based tool for implementing specific performance indicators at the course level rather than the program level to fulfil the requirements of ABET. The tool applies a weighted algorithm to measure different learning levels (e.g.…”

Specific, Generic Performance Indicators and Their Rubrics for the Comprehensive Measurement of ABET Student Outcomes

“…EvalTools® 6 [43] is chosen as the platform for outcomes assessment instead of Blackboard® [33] since it is the only tool that employs the Faculty Course Assessment Report (FCAR) and EAMU performance vector methodology [42,44,45,46,47,48,49,50,51] . This methodology facilitates the use of existing curricular assignments for outcomes assessment to achieve a high level of automation of the data collection process.…”

“…Additionally, the learning outcomes data measured by most engineering institutions are rarely classified into all three learning domains of the revised Bloom's taxonomy [52] and their corresponding categories of the levels of learning. Generally, institutions classify courses of a program curriculum into three levels: introductory, reinforced, and mastery, with outcomes assessment data measured for the mastery level courses in order to streamline the documentation and effort needed for an effective program evaluation [48,49] . This approach presents a major deficiency for CQI in a student-centered OBE model because performance information collected at just the mastery level is at the final phase of a typical quality cycle and is too late for implementation of remedial efforts.…”