Tag Archives: Engineering

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.

Super channel system

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polina bayvelPerhaps we can be characterized by whether or not we believe we have an acceptable speed of internet access.  At home and work, I’m in the category that’s never satisfied by the speed provided.  Well, now there is a completely new standard: 1.125 Tb/s.  That’s 50,000 times faster than anything commercially available at the moment.  You could download a boxed set of the entire Games of Thrones saga in a second; at least that’s how Professor Polina Bayvel described her latest research in a recent conference that I attended at the Royal Society.  Professor Bayvel is head of the Optical Networks Group at University College London.  I think the UK government should abandon attempting to extend the current internet technology to everyone in the country and instead leap-frog the rest of the world by working on rolling out Prof Bayvel’s new technology.

Sources:

Maher R, Xu T, Galdino L, Sato M, Alvarado A, Shi K, Savory SJ, Thomsen BC, Killey RI & Bayvel P, Spectrally shaped DP-16QAM super-channel transmission with multi-channel digital back propagation, Scientific Reports, 5:8214, 2015.

Models as fables

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moel arthurIn his book, ‘Economic Rules – Why economics works, when it fails and how to tell the difference‘, Dani Rodrik describes models as fables – short stories that revolve around a few principal characters who live in an unnamed generic place and whose behaviour and interaction produce an outcome that serves as a lesson of sorts.  This seems to me to be a healthy perspective compared to the almost slavish belief in computational models that is common today in many quarters.  However, in engineering and increasingly in precision medicine, we use computational models as reliable and detailed predictors of the performance of specific systems.  Quantifying this reliability in a way that is useful to non-expert decision-makers is a current area of my research.  This work originated in aerospace engineering where it is possible, though expensive, to acquire comprehensive and information-rich data from experiments and then to validate models by comparing their predictions to measurements.  We have progressed to nuclear power engineering in which the extreme conditions and time-scales lead to sparse or incomplete data that make it more challenging to assess the reliability of computational models.  Now, we are just starting to consider models in computational biology where the inherent variability of biological data and our inability to control the real world present even bigger challenges to establishing model reliability.

Sources:

Dani Rodrik, Economic Rules: Why economics works, when it fails and how to tell the difference, Oxford University Press, 2015

Patterson, E.A., Taylor, R.J. & Bankhead, M., A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103, 2016

Hack, E., Lampeas, G. & Patterson, E.A., An evaluation of a protocol for the validation of computational solid mechanics models, J. Strain Analysis, 51(1):5-13, 2016.

Patterson, E.A., Challenges in experimental strain analysis: interfaces and temperature extremes, J. Strain Analysis, 50(5): 282-3, 2015

Patterson, E.A., On the credibility of engineering models and meta-models, J. Strain Analysis, 50(4):218-220, 2015