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Improving Engineering Learning Outcomes Assessment through Performance Indicators

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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

Multidisciplinary Effects on Student Learning

Tagged Division

Multidisciplinary Engineering

Tagged Topic

Diversity

Page Count

9

DOI

10.18260/p.25615

Permanent URL

https://peer.asee.org/25615

Download Count

175

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Paper Authors

biography

Doanh Van Union University

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Doanh Van, PhD., PE, CEM is founding Chair of Union University Engineering Department. Currently, he is serving as professor of Engineering. Among the courses he teaches are thermal-fluids, energy conversion and solid modeling. He worked for Commonwealth Edision, GPUNuclear, Honeywell International, Warner Lambert, and Pfizer prior to joining Union University.

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Abstract

Abstract

Although Outcomes a-k are being reviewed for a possible revamp by ABET (http://www.abet.org/accreditation/accreditation-criteria/accreditation-alerts/) in the near future, the philosophy is expected to remain the same—learning outcomes must be assessed and evaluated by the Program. Reviewing the extent to which a Program is in compliance with Criterion 3 is among the major tasks of the ABET team during an accreditation review. Performance Indicators (PIs) are the yardsticks leading to effective and consistent assessments of the various learning outcomes. We have undertaken major efforts to improve such assessment tool. ABET requires that “the program must have documented student outcomes that prepare graduates to attain the program educational objectives.” It’s important that such assessment and evaluation be done consistently. A discussion of our old rubrics is given together with the various issues we had with them. Our improved performance indicators for the complete set of a-k outcomes are described. Emphases are given to “soft” outcomes such as d, f, h, i and j. These outcomes are “soft” because, as agreed upon by many faculty members, they are important but hard to be measured. The implementations of our new set of PIs are also discussed especially in light of the remapping of the outcome-to-course matrix. Faculty ownership of these performance indicators is critical to assure diverse ideas, ownership and spirited implementation. Huge investment in faculty time must be expected and aggressively scheduled to sustain faculty momentum and to avoid wheel-spinning frustration. A description is given on how the new model of performance indicators was tested and assessed for the approval by the faculty. The new model having been approved for use was a clear indication that streamlining of learning outcome data collection was a success; it was something very much welcomed and appreciated by the faculty as well.

Van, D. (2016, June), Improving Engineering Learning Outcomes Assessment through Performance Indicators Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.25615

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