Explanations and Modelling
"In your best explanations, your artistry with words will transform complicated and abstract material into something clear and meaningful" (Allison and Tharby, 2015).
In this post I am going to briefly review some key points about explanations and modelling within the classroom setting. For a thorough introduction to both these principles I strongly recommend reading Chapters 2 and 3 of Making Every Lesson Count by Shaun Allison and Andy Tharby.
Explanations
The role of teacher explanations has taken a hit in the last decade or so, with some going so far as saying that we should limit teacher talk in lessons, and instead facilitate students making their own learning. This is based on the idea of constructivism, but it has been argued that this is a misinterpretation of the theory. Constructivism is a theory of learning: we construct meaning through making connections with what we already know, which means that any two individuals will construct different learning from any experience. However, constructivism has been adopted as a pedagogy in which students should discover things for themselves in order to construct their own meaning (Hobbiss, 2018). This pedagogy is not supported by the evidence, and some even argue the opposite (Kirschner et al, 2006).
A well-crafted explanation can get the imagination going, pique curiosity and clarify complex ideas. Perhaps rather than getting teachers to talk less, we should focus on getting teachers to talk better.
The purpose of explanations is to help students understand new learning. There are three principle for great explanations, as suggested by Allison and Tharby (2015), which are:
Connect the new learning to prior learning
Be aware of the limited working memory of students
Turn abstract concepts into concrete examples
The first of these is so important. We learn everything in relation to what we already know. This will colour both our interpretations of what is said and how well we remember it. If you know a lot about something in great depth, you are more likely to recognise the nuance in new ideas, as well as having more connections to make in trying to understand the new concept. For more on this see here, for example.
The second is also important at many points in a learning journey. Our working memory acts a little like a juggler. If you give it too many things to juggle it will, in the best case scenario, drop one of the balls, but more likely, will end up dropping everything. That is, if an explanation is too long or too convoluted then the likelihood is that students will not remember it, and may not even understand it to start with. The best explanations are concise and precise. They say what needs to be said, but nothing more. Using overtly complex language can also impede the effectiveness of an explanation as students may focus their attention on the word they do not know rather than what you are trying to explain, especially in a situation where we have students who are learning in their second language.
The third point links to the first. To make explanations more 'sticky' we need to give concrete examples of what we are explaining, and make the connections between the example and the concept. This can be done with analogies to things that students already know well, with demonstrations, visuals, or many other ways. Taking an abstract concept and making concrete examples is one of the arts of the subject specialist, and the way these look will vary greatly between departments.
Heath and Heath (2007) give the mnemonic SUCCES to help remember the key things to make an explanation "sticky":
Simple - focus on the core concept
Unexpected - generate curiosity
Concrete - make the concept real and meaningful
Credible - provide opportunities to believe the concept
Emotional - create feelings as a result of the explanation
Story - build a story into your explanation
One of the biggest problems with explanations is usually that we fall prey to the Curse of Knowledge. This happens when we know something so fail to see how somebody else does not know it. In explanations this usually leads to jumping steps which seem obvious to the teacher, but are not things that students either know about at all, or they know them but do not know them well enough yet to draw on them out of context. One of the hardest parts of being an effective teacher is overcoming our own curse of knowledge, and envisaging ourselves in our students' shoes. Carefully planning explanations before hand can help us achieve this, especially if we do this collaboratively with other teachers.
Another common pitfall is to ask students to "Guess what's in my head?". As Birbalsingh (2019) says, it is so common in teaching that we don't even realise we do it. But she argues that unless 75% of students raise their hands, then you shouldn't asking the question, as you haven't taught the material (at least not so they have learned it). Rather than playing this game, take the time to explain content and concepts to students first, then question them on their understanding. At a later date you can question them on their memory.
Even the very best explanations can fall short, given that we have a classroom full of students, and they all have different starting points and different connections they can make. So it is often useful to have a variety of explanations ready, so that if one does not get through to a student, we can pull out the next one. Our job is to help the students understand and we need to have as many different ways of explaining each concept as we can, to not only provide students with the best chance of 'getting it', but also to help them build their own complex networks of connected ideas.
Modelling
Once something new has been explained, it needs to be modelled so students can see what it looks like. This could be a physical activity (using a drill in DT, doing the long jump), a piece of written work (how to write a PEEL paragraph, how to solve an equation) or a more generic skill (how to listen to each other and respond to what is being said, how to research using JSTOR). It also applies to behaviours we want to see in the classroom (how to act at the start of a lesson, what to do when you don’t know what to do). In all these cases, we use models to make explicit the explanation.
The important thing with models is that they must be achievable for the students, but also of high quality. Your model will be the standard students set themselves.
The worked examples effect is the well-established result that, for novice learners, the use of worked examples improves their knowledge and understanding of new material (CESE, 2017). This means that by either showing students models, or possibly even better, modelling a process for them live, will be of significant use to them, especially if they are relative novices in the topic you are studying. This is because showing worked examples and models reduces the cognitive load on working memory, thus freeing up space for students to focus on the important parts of the example, and to process the information. As students gain more expertise in a given domain, the use of worked examples should be reduced, giving them more independence over their learning. This is known as the expertise reversal effect, where those with more expertise actually learn better through trying it themselves than being shown examples.
Although the aim is to develop independent and creative learners, the first step is to be able to do the basics, and do them well. This is where modelling comes in, and it is vital that after we model something, we give students an opportunity to try to mimic the model as soon as possible. This allows them and us to check if they have understood what was modelled. For example, I use example-problem pairs when teaching new processes. This involves me modelling an example of how to solve a question, and then students doing a very similar example themselves. The process of modelling a process in small step-by-step pieces, each followed by practice, is a well-documented way to effectively teach new concepts, especially complex tasks (Rosenshine, 2012).
When modelling, we should aim to make the implicit explicit. Linked to the Curse of Knowledge, we will do things automatically without needing to think, but some of those steps might be quick big for students. We should continuously ask students "What did I just do?" followed by "Why did I do that?". These two questions help students to make the connections between the model and what they already know, as well as identifying the steps in a process. Lemov (2015) suggests we "Name the Steps", which involves the teacher breaking down the process to make it more memorable for students. In doing this, the teacher has to actively think about all the steps they do without thinking, which helps them identify where they might jump through things too quickly. It is also possible to get students to reflect on the steps after showing a model, as long as the modelling has not taking too much working memory capacity.
Once students have had some initial exposure to excellent models, we should also introduce students to other models, possibly of lower quality. Getting students to compare and contrast between the models, and identify what makes one excellent and another not so great will help them pinpoint the exact traits of a piece of excellent work. Partnered with a clear explanation, this will put them in a much better position to achieve excellence themselves as they will have a much better understanding of what excellence looks like. If there are success criteria for a piece of work, discussing how the different models meet the success criteria (or not) will also aid students in being able to better meet them in their own work.
The dangers of modelling are in its overuse. Whenever a new concept or process is introduced for the first time, modelling it will help the students understand it, however, relying on models for more developed concepts is akin to spoon-feeding, and can actually be detrimental. As Daniel Willingham (2009) says, "memory is the residue of thought", and as such we want the students to be doing the thinking. So we need to remove models at a point when students have acquired enough understanding to give it a go themselves. Of course, it is likely students will make mistakes at this point, and that is fine as long as the teacher picks up on them and corrects (possibly referring them back to the model). This is the stage when students move into independent practice, also a vitally important part of the learning process, and we must be careful not to 'over-model' and hence remove the practice phase completely.
References
Allison, S. and Tharby, A. (2015). Making Every Lesson Count. Carmarthen: Crown House.
Birbalsingh, K. (2019). Guess What's In My Head. Accessed at: https://tomisswithloveblog.wordpress.com/2019/01/01/guess-whats-in-my-head/. Accessed on: 17 May 2019.
Centre for Education Statistics and Evaluation (2017). Cognitive Load Theory: Research That Teachers Really Need To Understand. Available at: https://www.cese.nsw.gov.au//images/stories/PDF/cognitive-load-theory-VR_AA3.pdf
Heath, C. and Heath, D. (2007). Made to Stick: Why Some Ideas Take Hold and Others Come Unstuck. London: Arrow Books.
Hobbiss, M. (2018). Constructivism is a theory of learning, not a theory of pedagogy. Neuroscience explains why this is important. Accessed at: https://npjscilearncommunity.nature.com/users/33200-mike-hobbiss/posts/41828-constructivism-is-a-theory-of-learning-not-a-theory-of-pedagogy-neuroscience-explains-why-this-is-important. Accessed on: 18 May 2019.
Kirschner, P., Sweller, J. and Clark, R. (2006). Why Minimal Guidance During Instruction Does Not Work: An Analysis of the failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist 41(2), pp. 75-86. Available at: http://www.cogtech.usc.edu/publications/kirschner_Sweller_Clark.pdf.
Lemov, D. (2015). Teach Like a Champion 2.0. San Francisco: Josey-Bass.
Rosenshine, B. (2012). Principles of Instruction: Research-Based Strategies That All Teachers Should Know. American Educator 36(1) pp.12-19. Available at: https://www.aft.org/sites/default/files/periodicals/Rosenshine.pdf.
Willingham, D. (2009). Why Don't Students Like School?: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom. San Francisco: Josey-Bass.