The Engineering Education Assessment Process - A Signals and Systems Perspective

George E. Hassoun

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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: June 29, 2016
ISBN: 978-0-692-68565-5
ISSN: 2153-5965
Conference Session: Works in Progress: Assessment and Research Tools
Tagged Division: Educational Research and Methods
Page Count: 21
DOI: 10.18260/p.26156
Permanent URL: https://peer.asee.org/26156
Download Count: 594

Paper Authors

biography

George E. Hassoun Notre Dame University - Louaize, Lebanon orcid.org/0000-0003-3412-2340

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G. Hassoun received the Licence en Physique degree from the Lebanese University, Beirut, Lebanon, in 1982, the Mastère en Avionique diploma from ENSAE, Toulouse, France, in 1984, the M.S. degree in Aeronautical and Astronautical Engineering from the Ohio State University, Columbus, OH, in 1989, and the Ph.D. degree in Electrical Engineering from the University of Adelaide, South Australia, in 1996.

In 1997, he worked as a Senior Research Assistant at the University of New South Wales - Sydney, with the Satellite Navigation and Positioning Group, Department of Geomatic Engineering. In 1998, he joined the Avionics Group of the Air Operations Division DSTO – South Australia, as a Research Scientist.

Since 2001, he has been an Assistant Professor with the Electrical, Computer and Communication Engineering Department at Notre Dame University – Louaize, Lebanon. His research interests include control, avionics, navigation and guidance, optimization and estimation theories, in addition to aerospace applications. He is presently interested in the application of signals and systems theory to engineering education.

Dr. Hassoun is a current member of the American Society for Engineering Education.

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Abstract

In this work in progress, a signal model is suggested for the knowledge acquired by engineering students during their study of a specific engineering subject, based on signals and systems theory. This model is illustrated using three courses typically taught in modern American-style engineering schools, namely Circuit Analysis, Signals and Systems, and Feedback Control. Additionally, a signals-and-systems-based model is suggested for the assessment process that inputs the acquired knowledge signal, mentioned above, and produces the so-called assessed (engineering) knowledge signal. Based on the largely acknowledged continuous nature of the brain activity, the cumulative nature of the acquired subject-specific engineering knowledge, and the discrete nature of assessment schemes typically administered in engineering schools, it is argued that the acquired engineering knowledge could appropriately be modeled by a continuous-time signal, however the assessed engineering knowledge could be more realistically modeled by a discrete-time signal.

As such, the assessment process could then be modeled as a sampling process, where samples of the acquired knowledge signal are captured at various periods of time for the purpose of reconstructing a good image of the student acquired knowledge. It is reasoned that the perceived level of student learning does depend to a large extent on the successful reconstruction of the (continuous) acquired knowledge signal from the (discrete) assessed knowledge signal, i.e. if the reconstruction process is flawed then the perceived level of learning can also be flawed!

Towards that end, Shannon’s sampling theorem is used to place a condition on the minimum assessment frequency, in order to avoid aliasing errors in the assessment/sampling process. Based on that theorem, a minimum of two samples (assessments) are necessary in the smallest period of the sampled signal (i.e. the acquired knowledge signal). Accordingly, and based in part on previous works in this domain, a preliminary figure of merit is suggested as a necessary minimum assessment frequency, below which the assessment process, and consequently the validity of perceived learning may be questionable.

This work is in line with recent studies by educational experts/psychologists advocating the switch from a 2-or-3-assessment-per-semester assessment scheme to a more frequent assessment scheme. The work has also the potential of benefiting engineering schools in their pursuit of quality education by drawing their attention not only to the quality (level) of their learning and assessment activities, but also to the frequency of these activities and to the importance of appropriately weighing each learning and assessment activity, including homework assignments and quizzes.

Citation
Format

Hassoun, G. E. (2016, June), The Engineering Education Assessment Process - A Signals and Systems Perspective Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26156

TY  - CPAPER
AB  - In this work in progress, a signal model is suggested for the knowledge acquired by engineering students during their study of a specific engineering subject, based on signals and systems theory. This model is illustrated using three courses typically taught in modern American-style engineering schools, namely Circuit Analysis, Signals and Systems, and Feedback Control. Additionally, a signals-and-systems-based model is suggested for the assessment process that inputs the acquired knowledge signal, mentioned above, and produces the so-called assessed (engineering) knowledge signal. Based on the largely acknowledged continuous nature of the brain activity, the cumulative nature of the acquired subject-specific engineering knowledge, and the discrete nature of assessment schemes typically administered in engineering schools, it is argued that the acquired engineering knowledge could appropriately be modeled by a continuous-time signal, however the assessed engineering knowledge could be more realistically modeled by a discrete-time signal.

As such, the assessment process could then be modeled as a sampling process, where samples of the acquired knowledge signal are captured at various periods of time for the purpose of reconstructing a good image of the student acquired knowledge. It is reasoned that the perceived level of student learning does depend to a large extent on the successful reconstruction of the (continuous) acquired knowledge signal from the (discrete) assessed knowledge signal, i.e. if the reconstruction process is flawed then the perceived level of learning can also be flawed!

Towards that end, Shannon’s sampling theorem is used to place a condition on the minimum assessment frequency, in order to avoid aliasing errors in the assessment/sampling process. Based on that theorem, a minimum of two samples (assessments) are necessary in the smallest period of the sampled signal (i.e. the acquired knowledge signal). Accordingly, and based in part on previous works in this domain, a preliminary figure of merit is suggested as a necessary minimum assessment frequency, below which the assessment process, and consequently the validity of perceived learning may be questionable.

This work is in line with recent studies by educational experts/psychologists advocating the switch from a 2-or-3-assessment-per-semester assessment scheme to a more frequent assessment scheme. The work has also the potential of benefiting engineering schools in their pursuit of quality education by drawing their attention not only to the quality (level) of their learning and assessment activities, but also to the frequency of these activities and to the importance of appropriately weighing each learning and assessment activity, including homework assignments and quizzes.

AU  - George E. Hassoun
CY  - New Orleans, Louisiana
DA  - 2016/06/26
PB  - ASEE Conferences
TI  - The Engineering Education Assessment Process - A Signals and Systems Perspective
UR  - https://peer.asee.org/26156
DO  - 10.18260/p.26156
ER  -