Tag Archives: problem-solving

Meta-knowledge: knowledge about knowledge

As engineers, we like to draw simple diagrams of the systems that we are attempting to analyse because most of us are pictorial problem-solvers and recording the key elements of a problem in a sketch helps us to identify the important issues and select an appropriate solution procedure [see ‘Meta-representational competence’ on May 13th, 2015].  Of course, these simple representations can be misleading if we omit parameters or features that dominate the behaviour of the system; so, there is considerable skill in idealising a system so that the analysis is tractable, i.e. can be solved.  Students find it especially difficult to acquire these skills [see ‘Learning problem-solving skills‘ on October 24th, 2018] and many appear to avoid drawing a meaningful sketch even when examinations marks are allocated to it [see ‘Depressed by exams‘ on January 31st, 2018].  Of course, in thermodynamics it is complicated by the entropy of the system being reduced when we omit parameters in order to idealise the system; because with fewer parameters to describe the system there are fewer microstates in which the system can exist and, hence according to Boltzmann, the entropy will be lower [see ‘Entropy on the brain‘ on November 29th, 2017].  Perhaps this is the inverse of realising that we understand less as we know more.  In other words, as our knowledge grows it reveals to us that there is more to know and understand than we can ever hope to comprehend [see ‘Expanding universe‘ on February 7th, 2018]. Is that the second law of thermodynamics at work again, creating more disorder to counter the small amount of order achieved in your brain?

Image: Sketch made during an example class

Knowledge explosions

Photo credit: Tom

When the next cohort of undergraduate students were born, Wikipedia had only just been founded [January 2001] and Google had been in existence for just over a decade [since 1998].  In their lifetime, the number of articles on Wikipedia has grown to nearly 6 million in the English language, which is equivalent to 2,500 print volumes of the Encyclopedia Britannica, and counting all language editions there are 48 million articles.  When Leonardo Da Vinci was born in 1452, Johan Gutenberg had just published his first Bible using moveable type.  By the time Leonardo Da Vinci was 20 years old, about 15 million books had been printed which was more than all of the scribes in Europe had produced in the previous 1500 years.  Are these comparable explosions in the availability of knowledge?  The proportion of the global population that is literate has changed dramatically from about 2%, when Leonardo was alive, to over 80% today which probably makes the arrival of the internet, Wikipedia and other online knowledge bases much more significant than the invention of the printing press.

Today what matters is not what you know but what you can do with the knowledge because access to the internet via your smart phone has made memorisation redundant.

Learning problem-solving skills

Inukshuk: meaning ‘in the likeness of a human’ in the Inuit language. A traditional symbol meaning ‘someone was here’ or ‘you are on the right path’.

One definition of engineering given in the Oxford English Dictionary is ‘the action of working artfully to bring something about’.  This action usually requires creative problem-solving which is a common skill possessed by all engineers regardless of their field of specialisation.  In many universities, students acquire this skill though solving example problems set by their instructors and supported by example classes and, or tutorials.

In my lectures, I solve example problems in class using a pen and paper combined with a visualiser and then give the students a set of problems to solve themselves.  The answers but not the solutions are provided; so that students know when they have arrived at the correct answer but not how to get there.  Students find this difficult and complain because I am putting the emphasis on their learning of problem-solving skills which requires considerable effort by them.  There are no short-cuts – it’s a process of deep-learning [see ‘Deep long-term learning’ on April 18th, 2018].

Research shows that students tend to jump into algebraic manipulation of equations whereas experts experiment to find the best approach to solving a problem.  The transition from student to skilled problem-solver requires students to become comfortable with the slow and uncertain process of creating representations of the problem and exploring the possible approaches to the solution [Martin & Schwartz, 2014].  And, it takes extensive practice to develop these problem-solving skills [Martin & Schwartz, 2009].  For instance, it is challenging to persuade students to sketch a representation of the problem that they are trying to solve [see ‘Meta-representational competence’ on May 13th, 2015].  Working in small groups with a tutor or a peer-mentor is an effective way of supporting students in acquiring these skills.  However, it is important to ensure that the students are engaged in the problem-solving so that the tutor acts as consultant or a guide who is not directly involved in solving the problem but can give students confidence that they are on the right path.

[Footnote: a visualiser is the modern equivalent of an OverHead Projector (OHP) which instead of projecting optically uses a digital camera and projector.  It’s probably deserves to be on the Mindset List since it is one of those differences between a professor’s experience as a student and our students’ experience [see ‘Engineering idiom’ on September 12th, 2018]].

References:

Martin L & Schwartz DL, A pragmatic perspective on visual representation and creative thinking, Visual Studies, 29(1):80-93, 2014.

Martin L & Schwartz DL, Prospective adaptation in the use of external representations, Cognition and Instruction, 27(4):370-400, 2009.

 

CALE #9 [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] – although this post is based on an introduction to tutorials given to new students and staff at the University of Liverpool in 2015 & 2016.

Photo: ILANAAQ_Whistler by NordicLondon (CC BY-NC 2.0) https://www.flickr.com/photos/25408600@N00/189300958/

Getting it wrong

Filming for the MOOC Energy: Thermodynamics in Everyday Life

Last week’s post was stimulated by my realisation that I had made a mistake in a lecture [see ‘Amply sufficiency of solar energy?‘ on October 25th, 2017]. During the lecture, something triggered a doubt about a piece of information that I used in talking about the world as a thermodynamic system. It caused me to do some more research on the topic afterwards which led to the blog post.  The students know this already, because I sent an email to them as the post was published.  It was not an error that impacted on the fundamental understanding of the thermodynamic principles, which is fortunate because we are at a point in the course where students are struggling to understand and apply the principles to problems.  This is a normal process from my perspective but rather challenging and uncomfortable for many students.  They are developing creative problem-solving skills – becoming comfortable with the slow and uncertain process of creating representations and exploring the space of possible solutions [Martin & Schwartz, 2009 & 2014].  This takes extensive practice and most students want a quick fix: usually looking at a worked solution, which might induce the feeling that some thermodynamics has been understood but does nothing for problem-solving skills [see my post on ‘Meta-representational competence‘ on May 13th, 2015].

Engineers don’t like to be wrong [see my post on ‘Engineers are slow, error-prone‘ on April 29th, 2014].  The reliability of our solutions and designs is a critical ingredient in the social trust of engineering [Madhaven, 2016].  So, not getting it wrong is deeply embedded in the psyche of most engineers.  It is difficult to persuade most engineers to appear in front of a camera because we worry, not just about not getting it wrong, but about telling the whole truth.  The whole truth is often inconvenient for those that want to sensationalize issues for their own purposes, such as to sell news or gain votes, and this approach is anathema to many engineers.  The truth is also often complicated and nuanced, which can render an engineer’s explanation cognitively less attractive than a simple myth, or in other words less interesting and boring.  Unfortunately, people mainly pass on information that will cause an emotional response in the recipient, which is perhaps why engineering blogs are not as widely read as many others! [Lewandowsky et al 2012].

 

This week’s lecture was about energy flows, and heat transfer in particular; so, the following posts from the archive might be interest: ‘On the beach‘ on July 24th, 2013, ‘Noise transfer‘ on April 3rd, 2013, and ‘Stimulating students with caffeine‘ on December 17th, 2014

Sources:

Martin L & Schwartz DL, Prospective adaptation in the use of external representations, Cognition and Instruction, 27(4):370-400, 2009.

Martin L & Schwartz DL, A pragmatic perspective on visual representation and creative thinking, Visual Studies, 29(1):80-93, 2014.

Madhaven G, Think like an engineer, London: One World Publications, 2016.

Lewandowsky S, Ecker UKH, Seifert CM, Schwarz N & Cook J, Misinformation and its correction: continued influence and successful debiasing, Psychological Science in the Public Interest, 13(3):106-131, 2012.