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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

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Conference

2016 ASEE Annual Conference & Exposition

Location

New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016

ISBN

978-0-692-68565-5

ISSN

2153-5965

Conference Session

Assessment I: Developing Assessment Tools

Tagged Division

Educational Research and Methods

Page Count

40

DOI

10.18260/p.27299

Permanent URL

https://peer.asee.org/27299

Download Count

284

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Paper Authors

biography

Wajid Hussain The Islamic University in Madinah Orcid 16x16 orcid.org/0000-0002-9337-4815

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Wajid Hussain is Director of the Office of Quality & Accreditation at the Faculty of Engineering, Islamic University, KSA. An enthusiastic, productive Electrical/Computer Engineer with more than 15 years Engineering experience and Mass Production expertise of Billion Dollar Microprocessor Manufacture Life Cycle.

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 Methodology

He 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® 6 for faculty, E learning with EvalTools® 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.

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biography

Fong K. Mak P.E. Gannon University

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FONG MAK, P.E. received his B.S.E.E. degree from West Virginia University in 1983, M.S.E.E. and Ph.D. in Electrical Engineering from the University of Illinois in 1986 and 1990. He joined Gannon in 1990. He was the Chair of Electrical and Computer Engineering at Gannon University from 2001 till 2014 and the Program Director for the professional-track Gannon/GE Transportation Embedded System Graduate Program for 2001-2014. He is now the professor of the department.

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biography

Mohammad Faroug Addas The Islamic University in Madinah

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Dean, Faculty of Engineering, The Islamic University in Madinah, Al-Madinah Al-Munawwarha, Saudi Arabia, e-mail: mfaddas@gmail.com

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Abstract

Abstract: This research references past work that 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, the learning outcomes data measured by most institutions are rarely classified into all three learning 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 the 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 accurate and comprehensive root-cause analysis with early remediation of observed performance deficiencies because necessary outcomes information related to deficient teaching and learning mechanisms are measured at only mastery level courses. A holistic approach for continuous quality improvement in academic learning would require a systematic, quantified measurement of performance indicators in all three domains of learning and their corresponding categories of learning levels for all course levels in a given program’s curriculum.

This research presents an innovative methodology for engineering program evaluation utilizing significant customization implemented in a web-based software EvalTools® 6 for the Faculty of Engineering at _______ University. The customization includes unique curricular assessments implementing scientific constructive alignment for 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 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 classified as introductory, reinforced and mastery. The computed values of ABET student outcomes are then used as a performance index in program term reviews.

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. Prioritized action items are generated and electronically transmitted to various academic committees. The committees have specific functions to continuously improve the overall quality of academic learning by aligning the design, and implementation of learning outcomes, curriculum, teaching, learning activities and assessments to acquire holistic standards as outlined in an ideal outcomes-based educational system.

Hussain, W., & Mak, F. K., & Addas, M. F. (2016, June), 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 Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.27299

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