Tag Archives: Engineering

A big question for engineers

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galleyhead lightThe proportion of women graduating with engineering degrees in the UK and US has remained around a sixth for at least the last thirty years despite many campaigns to achieve gender equality.  One of my colleagues, Professor Elena Rodriguez-Falcon, writing in the New Statesmen asked whether it will take another world war to get more women into engineering.  I think that the sort of seismic shift in attitude caused by such events will be required.  Many in the engineering profession claim that problem-solving is a unifying skill, which is common to all branches of engineering, and yet we have been unable to solve the problem that our profession is one of the least gender diverse.  Does this mean that we have not really been trying to solve the problem, or that we are not the problem-solvers we claim to be?

Sources:

Landivar LC, Disparities in STEM employment by sex, race and Hispanic origin, US Census Bureau, September 2013

 

Entropy in poetry

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WIN_20140716_190901 (2)Few weeks ago I mentioned about reading undergraduate dissertations [see my post entitled ‘A Startling Result‘ on May 18th, 2016] and about a year ago I wrote about the low quality of prose produced by engineers [see my post entitled ‘Reader, Reader, Reader‘ on April 15th, 2015 ].  Coleridge described prose as words in the best order and poetry as the best words in the best order. So today I’d like to direct you to a poem entitled ‘Entropy‘ by Neil Rollinson from his anthology ‘Spanish Fly’.  Here are a few lines from it:

“I open the window, the sky is dark
and the house is also cooling, the garden,
the summer lawn, all of it finding an equilibrium.”

I came across it while reading an anthology called ‘A Quark for Mister Mark: 101 Poems about Science‘ edited by Maurice Riordan and Jon Turney.  I was dipping into it while enjoying a pint in our backyard after a personal battle with entropy: painting rusting railings in our yard.

I was reviewing ‘A Quark for Mister Mark’ as potential reading material for a module on Technical Writing as part of our new CPD programme on Advanced Technical Skills.

Engaging learners on-line

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Filming at Quarry Bank Mill

Filming at Quarry Bank Mill

The Everyday Engineering Examples page of this blog continue to be very popular.  More than 70 engineering schools in the USA have signed up to use this approach to teaching engineering science as part of the ENGAGE project.  The lesson plans on that page assist instructors to deliver traditional lectures that are engaging and effective.  Now, we have transferred the approach to online delivery in a MOOC that was designed to support undergraduate learning as well as to increase public engagement and understanding of engineering science.

The MOOC entitled ‘Energy: Thermodynamics in Everyday Life‘ was completed by more than 960 learners from about 35 countries who ranged in age from 13 to 78 years old with a correspondingly wide range of qualifications in terms of both subject and level.  I believe that this is the first MOOC to use Everyday Engineering Examples within a framework of the 5E lesson plans and it seems to have been effective because the completion rate was 50% higher than the average for FutureLearn MOOCs.

We also included some experiments for MOOC learners to do at home in their kitchen.  Disappointingly only a quarter of learners performed the experiments but surprisingly almost half of all learners(46%) reported that the experiments contributed to their understanding of the topics.  This might be because results and photos from the experiments were posted on a media wall by learners.  There was also a vibrant discussion throughout the five-week course with a comment posted every 8 minutes (or more than 6,500 comments in total).

More than half the undergraduates (53%) who followed the MOOC did not continue to attend the traditional lectures and roughly the same percentage agreed or agreed strongly that the MOOC could replace the traditional lecture course with only 11% disagreeing.  So maybe the answer to my question about death knell for lectures [see my post ‘Death Knell for the lecture?‘ on October 7th, 2015] is that I can hear the bell tolling.

I gave a Pecha Kucha 20×20 on these developments at an International Symposium on Inclusive Engineering Education in London last month, which is available as a short video.

Credibility is in the eye of the beholder

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Picture1Last month I described how computational models were used as more than fables in many areas of applied science, including engineering and precision medicine [‘Models as fables’ on March 16th, 2016].  When people need to make decisions with socioeconomic and, or personal costs, based on the predictions from these models, then the models need to be credible.  Credibility is like beauty, it is in the eye of the beholder.   It is a challenging problem to convince decision-makers, who are often not expert in the technology or modelling techniques, that the predictions are reliable and accurate.  After all, a model that is reliable and accurate but in which decision-makers have no confidence is almost useless.  In my research we are interested in the credibility of computational mechanics models that are used to optimise the design of load-bearing structures, whether it is the frame of a building, the wing of an aircraft or a hip prosthesis.  We have techniques that allow us to characterise maps of strain using feature vectors [see my post entitled ‘Recognising strain‘ on October 28th, 2015] and then to compare the ‘distances’ between the vectors representing the predictions and measurements.  If the predicted map of strain  is an perfect representation of the map measured in a physical prototype, then this ‘distance’ will be zero.  Of course, this never happens because there is noise in the measured data and our models are never perfect because they contain simplifying assumptions that make the modelling viable.  The difficult question is how much difference is acceptable between the predictions and measurements .  The public expect certainty with respect to the performance of an engineering structure whereas engineers know that there is always some uncertainty – we can reduce it but that costs money.  Money for more sophisticated models, for more computational resources to execute the models, and for more and better quality measurements.