Category Archives: Engineering

Experiences in the lecture theatre

I wrote a couple of weeks ago about cycling students around Honey and Mumford’s learning modes [See ‘So how do people learn?‘ on June 20th 2018] without explaining how this might be achieved in a lecture course.  The first step in the cycle is having an experience, which is difficult for a student in a lecture theatre with dozens of other people.  A demonstration by the lecturer does not achieve it because the student is not doing and feeling.

So, how can the first step be achieved in a traditional engineering lecture course?  Well one answer, for introductory courses, is to exploit the everyday experiences of the students by choosing something that they will have done for themselves, preferably more than once.  It can be useful to perform a demonstration at the start of the lecture to engage the students and remind them about their own experience.  All of the lesson plans provided on this blog start with this kind of activity [https://realizeengineering.blog/everyday-engineering-examples/].

The lecture can proceed to reviewing the experience and building a new context around it, i.e. the engineering principles that are being taught.  It might necessary to review the experience in several different ways and make a series of connections to it.  I recommend that the third step: concluding from the experience, should be a student activity guided by the instructor – perhaps a piece of homework that leads the student to take the fourth step on their own, becoming a Pragmatist by planning their next steps.

Doris Lessing, Nobel Laureate for Literature, in ‘The Four-gated City‘ wrote ‘That is what learning is.  You suddenly understand something you’ve understood all your life, but in a new way.’  Understanding an everyday experience a new [engineering] way is what we are trying to achieve.

 

CALE #4 [Creating A Learning Environment: a series of posts based on a workshop given periodically by Pat Campbell and Eann Patterson in the USA supported by NSF and the UK supported by HEA]

Fourth industrial revolution

Have you noticed that we are in the throes of a fourth industrial revolution?

The first industrial revolution occurred towards the end of the 18th century with the introduction of steam power and mechanisation.  The second industrial revolution took place at the end of the 19th and beginning of the 20th century and was driven by the invention of electrical devices and mass production.  The third industrial revolution was brought about by computers and automation at the end of the 20th century.  The fourth industrial revolution is happening as result of combining physical and cyber systems.  It is also called Industry 4.0 and is seen as the integration of additive manufacturing, augmented reality, Big Data, cloud computing, cyber security, Internet of Things (IoT), simulation and systems engineering.  Most organisations are struggling with the integration process and, as a consequence, are only exploiting a fraction of the capabilities of the new technology.  Revolutions are, by their nature, disruptive and those organisations that embrace and exploit the innovations will benefit while the existence of the remainder is under threat [see [‘The disrupting benefit of innovation’ on May 23rd, 2018].

Our work on the Integrated Nuclear Digital Environment, on Digital Twins, in the MOTIVATE project and on hierarchical modelling in engineering and biology is all part of the revolution.

Links to these research posts:

Enabling or disruptive technology for nuclear engineering?’ on January 28th, 2015

Can you trust your digital twin?’ on November 23rd, 2016

Getting Smarter’ on June 21st, 2017

‘Hierarchical modelling in engineering and biology’ [March 14th, 2018]

 

Image: Christoph Roser at AllAboutLean.com from https://commons.wikimedia.org/wiki/File:Industry_4.0.png [CC BY-SA 4.0].

Spontaneously MOTIVATEd

Some posts arise spontaneously, stimulated by something that I have read or done, while others are part of commitment to communicate on a topic related to my research or teaching, such as the CALE series.  The motivation for a post seems unrelated to its popularity.  This post is part of that commitment to communicate.

After 12 months, our EU-supported research project, MOTIVATE [see ‘Getting Smarter‘ on June 21st, 2017] is one-third complete in terms of time; and, as in all research it appears to have made a slow start with much effort expended on conceptualizing, planning, reviewing prior research and discussions.  However, we are on-schedule and have delivered on one of our four research tasks with the result that we have a new validation metric and a new flowchart for the validation process.  The validation metric was revealed at the Photomechanics 2018 conference in Toulouse earlier this year [see ‘Massive Engineering‘ on April 4th, 2018].  The new flowchart [see the graphic] is the result of a brainstorming [see ‘Brave New World‘ on January 10th, 2018] and much subsequent discussion; and will be presented at a conference in Brussels next month [ICEM 2018] at which we will invite feedback [proceedings paper].  The big change from the classical flowchart [see for example ASME V&V guide] is the inclusion of historical data with the possibility of not requiring experiments to provide data for validation purposes. This is probably a paradigm shift for the engineering community, or at least the V&V [Validation & Verification] community.  So, we are expecting some robust feedback – feel free to comment on this blog!

References:

Hack E, Burguete RL, Dvurecenska K, Lampeas G, Patterson EA, Siebert T & Szigeti E, Steps toward industrial validation experiments, In Proceedings Int. Conf. Experimental Mechanics, Brussels, July 2018 [pdf here].

Dvurcenska K, Patelli E & Patterson EA, What’s the probability that a simulation agrees with your experiment? In Proceedings Photomechanics 2018, Toulouse, March 2018.

 

 

So how do people learn?

Here’s the next in the CALE series.  When designing a learning environment that supports the acquisition of knowledge by all of our students, we need to think about the different ways that people learn.  In the 1970s, Kolb developed his learning style inventory which is illustrated in the diagram above.  Approaches to learning are plotted on two axes: on the horizontal axis is learning by watching at one end and learning by doing at the other; while on the vertical axis is learning by feeling at one end and learning by thinking at the opposite end.  Kolb proposed that people tend to learn by a pair of these attributes, i.e. by watching and feeling, or watching and thinking, or doing and thinking, or doing and feeling, so that an individual can be categorised into one of the four quadrants.  Titles are given to each type of learning as shown in the quadrants, i.e. Divergers, Assimilators, Convergers and Accommodators.

In practice, it seems unlikely that many of us remain in one of these quadrants though we might have a preference for one of them.  Honey and Mumford [1992] proposed that learning is most effective when we rotate around the learning modes represented in the quadrants, as shown in the diagram below.  Starting in the doing & feeling quadrant by have an experience and being an Activist, moving to the feeling & watching quadrant by reviewing the experience as a Reflector, then in watching and thinking mode, drawing conclusions from the experience as a Theorist, culminating with planning the next steps as a Pragmatist in the thinking and doing quadrant before repeating the rotation.

There are other ideas about how we learn but these are two of the classic theories, which I have found useful in creating a learning environment that is dynamic and involves cycling students around Honey and Mumford’s learning modes.

References:

Kolb DA, Learning style inventory technical manual. McBer & Co., Boston, MA, 1976.

Honey P & Mumford A. The Manual of Learning Styles 3rd Ed. Peter Honey Publications Limited, Maidenhead, 1992.

 

CALE #3 [Creating A Learning Environment: a series of posts based on a workshop given periodically by Pat Campbell and Eann Patterson in the USA supported by NSF and the UK supported by HEA]