Category Archives: everyday engineering examples

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]

Formative experiences

A few weeks ago, I wrote about how we all arrive in the classroom with different experiences that are strongly influenced by the conditions in our formative years.  When I talk about this process in workshops on teaching, I invite attendees to tell us about something that has influenced their approach to learning.  However, I kick-off by sharing one of mine: I joined the Royal Navy straight from school and so I arrived at University having painted the white line down the centre of the flight deck of an aircraft carrier but also having flown a jet.  This meant that my experience of dynamics was somewhat different to most of my peers.  It’s amazing the life experiences that are revealed when we go around the room at these workshops.  Feel free to share your experiences and how they influence your learning using the comments section below.

CALE #2 [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]

Photo by Pedro Aragao [Creative Commons Attribution-Share Alike 3.0 Unported]

Everyday examples contribute to successful learning

Some weeks ago I quoted Adams and Felder [2008] who said that the ‘educational role of faculty [academic staff] is not to impart knowledge; but to design learning environments that support…knowledge acquisition’ [see ‘Creating an evolving learning environment’ on February 21st, 2018].  A correspondent asked how I create a learning environment and, in response, this is the first in a series of posts on the topic that will appear every third week.  The material is taken from a one-day workshop that Pat Campbell [of Campbell-Kibler Associates] and I have given periodically in the USA [supported by NSF ] and UK [supported by HEA] for engineering academics.

Albert Einstein is reputed to have said that ‘knowledge is experience, everything else is just information’.  I believe that a key task for a university teacher of engineering is to find the common experiences of their students and use them to illustrate engineering principles.  This is relatively straightforward for senior students because they will have taken courses or modules delivered by your colleagues; however, it is more of a challenge for students entering the first year of an engineering programme.  Everyone is unique and a product of their formative conditions, which makes it tricky to identify common experiences that can be used to explain engineering concepts.  The Everyday Engineering Examples, which feature on a page of this blog [https://realizeengineering.blog/everyday-engineering-examples/], were developed to address the need for illustrative situations that would fall into the experience of most, if not all, students.  Two popular examples are using the splits in sausages when you cook them to illustrate two-dimensional stress systems in pressure vessels [see lesson plan S11] and using a glass to extinguish a birthday candle on a cup cake to explain combustion processes [see lesson plan T11].

Everyday Engineering Examples were developed as part of an educational research project, which was funded by the US National Science Foundation [see ENGAGE] and demonstrated that this approach to teaching works.  The project found that significantly more students rated their learning with Everyday Engineering Examples as high or significant than in the control classes independent of the level of difficult involved [Campbell et al. 2008].  So, this is one way in which I create a learning environment that supports knowledge acquisition.  More in future posts…

References

Adams RS & Felder RM, Reframing professional development: A systems approach to preparing engineering educators to educate tomorrow’s engineers. J. Engineering Education, 97(3):230-240, 2008

Campbell PB, Patterson EA, Busch Vishniac I & Kibler T, Integrating Applications in the Teaching of Fundamental Concepts, Proc. 2008 ASEE Annual Conference and Exposition, (AC 2008-499), 2008

 

CALE #1 [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]

How many repeats do we need?

This is a question that both my undergraduate students and a group of taught post-graduates have struggled with this month.  In thermodynamics, my undergraduate students were estimating absolute zero in degrees Celsius using a simple manometer and a digital thermometer (this is an experiment from my MOOC: Energy – Thermodynamics in Everyday Life).  They needed to know how many times to repeat the experiment in order to determine whether their result was significantly different to the theoretical value: -273 degrees Celsius [see my post entitled ‘Arbitrary zero‘ on February 13th, 2013 and ‘Beyond  zero‘ the following week]. Meanwhile, the post-graduate students were measuring the strain distribution in a metal plate with a central hole that was loaded in tension. They needed to know how many times to repeat the experiment to obtain meaningful results that would allow a decision to be made about the validity of their computer simulation of the experiment [see my post entitled ‘Getting smarter‘ on June 21st, 2017].

The simple answer is six repeats are needed if you want 98% confidence in the conclusion and you are happy to accept that the margin of error and the standard deviation of your sample are equal.  The latter implies that error bars of the mean plus and minus one standard deviation are also 98% confidence limits, which is often convenient.  Not surprisingly, only a few undergraduate students figured that out and repeated their experiment six times; and the post-graduates pooled their data to give them a large enough sample size.

The justification for this answer lies in an equation that relates the number in a sample, n to the margin of error, MOE, the standard deviation of the sample, σ, and the shape of the normal distribution described by the z-score or z-statistic, z*: The margin of error, MOE, is the maximum expected difference between the true value of a parameter and the sample estimate of the parameter which is usually the mean of the sample.  While the standard deviation, σ,  describes the difference between the data values in the sample and the mean value of the sample, μ.  If we don’t know one of these quantities then we can simplify the equation by assuming that they are equal; and then n ≥ (z*)².

The z-statistic is the number of standard deviations from the mean that a data value lies, i.e, the distance from the mean in a Normal distribution, as shown in the graphic [for more on the Normal distribution, see my post entitled ‘Uncertainty about Bayesian methods‘ on June 7th, 2017].  We can specify its value so that the interval defined by its positive and negative value contains 98% of the distribution.  The values of z for 90%, 95%, 98% and 99% are shown in the table in the graphic with corresponding values of (z*)², which are equivalent to minimum values of the sample size, n (the number of repeats).

Confidence limits are defined as: but when n = , this simplifies to μ ± σ.  So, with a sample size of six (6 = n   for 98% confidence) we can state with 98% confidence that there is no significant difference between our mean estimate and the theoretical value of absolute zero when that difference is less than the standard deviation of our six estimates.

BTW –  the apparatus for the thermodynamics experiments costs less than £10.  The instruction sheet is available here – it is not quite an Everyday Engineering Example but the experiment is designed to be performed in your kitchen rather than a laboratory.