Feedback and what good ‘looks like’

I’ve been thinking a lot about feedback lately and reminiscing on my younger days as a sports coach. When introducing a new skill to an individual, it was imperative that I could model, or show an example of what good looks like, otherwise learners would simply not know what they were aiming to achieve.


Learning something new is really challenging, it becomes more so if we don’t know what good ‘looks like’. I’m not an engineer, but let’s take the example of learning a fillet lap weld. Without seeing what a good fillet lap weld looks like, it would be nigh on impossible for a learner to do one successfully. Take the correct use of apostrophes – without seeing the various uses of an apostrophe, one simply wouldn’t know know how to use it.


Just knowing what good ‘looks like’ isn’t enough to learn something effectively however. Along the way to mastering a fillet lap weld, or correct apostrophe use, there’ll no doubt be mistakes made. This is where feedback is essential. According to Ramaprasad (1983, p.4) ‘feedback is information about the gap between the actual level and the reference level of a system parameter which is used to alter the gap in some way’. In other words, feedback should identify the strengths and weaknesses of performance in relation to what good ‘looks like’. But is it that simple?


No. In 1996, Kluger and DiNisi explored the effects of feedback on performance. Their meta-analysis revealed that on average, feedback improved performance but bizarrely, in over a third of cases, feedback actually impeded performance. Upon further exploration, their work revealed that the more effective feedback focussed on the quality of the work (task-oriented), rather than the person (ego-oriented). In other words, focus was on the strengths and areas for development of the work, rather than assigning numbers or grades to the work, which allow for comparisons between learners. In addition to this, they found that more effective feedback focussed on what and how the individual could improve their performance (the future), rather than focussing too much on the performance itself (the past). I liken this to the analogy of driving a car. If we focus too much on what we can see in our rear view mirror, we’ll probably crash (image 1). Whereas, if we acknowledge our mirror, but focus our attention on the road in front, we’re more likely to be moving forward positively (image 2).

Similar findings were noted in the work of Hattie and Timperley (2007); they determined that feedback was best served with clear goals for improvement. If we think back to my above mentioned point about knowing what good ‘looks like’, if feedback is provided in relation to a good example of a fillet lap weld and looks at how current work could be developed to achieve a good standard, then it is more likely that the learner will make improvements.


The thing with feedback is that it becomes extremely challenging for a teacher to provide 20-30 learners with regular individual feedback in a session. Here’s the thing, you don’t need to. Once learners are clear with what good ‘looks like’, there are 20-30 other resources at a teacher’s disposal, so why not ask them to provide feedback to one another?


Some common methods to do this are identified in Petty’s (2009) fantastic Evidence Based Teaching book. One of his diamonds is the ‘medal and mission’ approach – very simple, yet also very effective. Firstly task centred information is provided to the learner in relation to the goals (what good ‘looks like’) – the medal. Following this, learners are given a clear target for improvement in relation to the goal – the mission. For example:


‘Jamal, you have clearly fit-up the plates accurately and your weld indicates that the distance to the joint was good, as the arc is the correct depth (medal). If you look at the model example, the bead size is slightly larger. To increase the size of the bead, you need to decrease the speed that you move along the joint. In your next attempt, continue in the same manner as before, but with a slightly slower speed’ (mission).


Similar approaches that may be used include:

  • 2 Stars and a Wish – useful for peer assessment, the learners give one another 2 stars (i.e. 2 things they think their peer has done well in relation to what good ‘looks like’) and a wish (i.e. something they wish could be improved upon in relation to what good ‘looks like’).
  • WWW/EBI – as before, this acknowledges the past – What Went Well (in relation to what good ‘looks like’), before looking to the future with clear guidance for improvement, Even Better If…(in relation to what good ‘looks like’).


Whilst peer feedback is really useful, it is worth noting the limitations of the above approaches. Indeed, Nuttall (2007) acknowledges that around 80% of feedback in a typical classroom is between peers, yet around 80% of that feedback is inaccurate. If we can provide suitable structures, such as the above, and ensure that clear success criteria is provided (what good ‘looks like’), then we improve the effectiveness of peer to peer feedback.


To summarise, if we really want to maximise feedback in classrooms, we need to ensure the following:

  • Everyone is clear with what good ‘looks like’
  • Feedback looks forward and not back
  • Feedback focuses on the task and not the person
  • Feedback involves everyone



Hattie, J. and Timperley, H. (2007). The power of feedback. Review of Educational Research. 77 (1), p. 81-112.

Kluger, A.N. and DiNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis and a preliminary feedback intervention theory. Psychological Bulletin, 119 (2), p. 254-284.

Nuthall, G. (2007). The Hidden Lives of Learners. NZCER Press

Petty, G. (2009). Evidence Based Teaching. Cheltenham: Nelson Thornes.

Ramaprasad, A. (1983). On the definition of feedback. Behavioral Science, 28, 4–13.


Learning my Craft pt 1.

I’ve been reflecting on where it all began for me as a teacher. At 16, I left school with six GCSEs above grade C and didn’t think that further study was for me, so I embarked upon a career in the leisure industry. I worked for a couple of years as a lifeguard, swimming teacher and fitness instructor before going back into education. When I think about it, it was during this period that I learnt most about the craft of teaching. Let me explain why:


Like many activities, both gym based exercise and swimming involves a range of motor skills. From the breaststroke technique, to performing a bench press, both involve complex motor skills and for novices, both can be difficult to master. Whilst learner confidence is an important ‘affective’ characteristic in both environments (particularly in swimming, which I might blog about at a later date due to its relevance to FE learning), once a level of confidence is developed, the teaching of a new skill can be done with efficiency and impact. However, the teaching of a skill can also be very inefficient and ineffective. In this post I hope to share some of the theories/strategies that I learnt early on in my career which have helped me to hone my craft and I’d like to think are the more efficient/effective approaches.


Further Education (FE) caters for a diverse group, which makes it challenging when recommending particular teaching strategies. Last year I blogged about the different approaches one might take with 3 learners.  There are many technical subjects where the vast majority of learning is skill based (procedural knowledge to the cognitive scientists). When one learns a practical (motor) skill, for example, welding, sewing, cutting, drilling etc, according to Fitts and Posner (1967), there are certain stages that one goes through in order to develop ‘automaticity’. A summary can be found in the table below:

Image Source

STAGE 1: Cognitive Stage Huber (2013) states that the cognitive stage is:

‘verbal–cognitive in nature (Schmidt & Lee, 2005) because it involves the conveyance (verbal) and acquisition (cognition) of new information. In this stage, the person is trying to process information in an attempt to cognitively understand the requirements and parameters of motor movement.’

In other words, this involves the learner making sense about how to perform a skill. In order to do this, they need to see what ‘good looks like’ (blog to follow). To see this, they require explicit instruction by a competent individual. In the case of a teacher, the most effective way of doing this is to accurately model the skill and explain each step clearly. This is supported by research in the fields of fitness and gymnastics where it was found that effective modelling improved performance over other methods of instruction/development. Of course, as McCueeagh, Weiss and Ross note, there are many other factors to consider when modelling skills, e.g age and stage of learners, but if we think about principles of cognitive load theory, clear, chunked explanations and a combination of coherent visual and auditory information (dual coding) are proven techniques for supporting knowledge acquisition.  When I think back to my fitness instructor course in the early 00’s, effective modelling and instruction was inherent. The main strategy adopted when supporting gym users with new exercises/equipment was NAMSET:

  • N= Name of the Exercise – the name of the skill is outlined by the teacher
  • A= Area of the body worked – the teacher identifies the area of the body that is being worked
  • M= Muscles used – the teacher uses the correct anatomical terminology for muscles used
  • S= Silent demonstration – the teacher demonstrates the new skill in silence
  • E= Explanation of the exercise – the teacher explains the skill in small steps, with key points of consideration.
  • T = Teach the exercise – the teacher supports the learner as they complete the skill

Whilst I didn’t always follow this to the letter, I used the principle to instruct clients and found that they often managed to grasp techniques quickly. Incidentally, I hadn’t heard about cognitive load theory until around 18 months ago, but had been implementing key principles in my instruction. As with any new information, one needs to manage cognitive load and the NAMSET steps allow for this. I’ve placed in bold, the sections that are perhaps most relevant to teaching any new skill.

  1. Name the skill/task. What will you be showing and why? Giving reason and purpose to any new skill is likely to improve the focus.
  2. Where possible, demonstrate how to do it in silence. This allows the learner the opportunity to observe and self talk. I’d like to explore this a little further if I’m honest. I’m not sure that this should come before or after the explanation. Thoughts?
  3. Explain whilst demonstrating. This uses both the visual and auditory pathways to working memory (dual coding) if the explanations are clear and concise. Using complex terminology and excessive information risks losing the focus of learners, and/or overloading their working memory.  What are the key points for consideration? How can you explain the process clearly and concisely?
  4. Allow learners to complete the skill independently, but guide as required.  This is an opportunity for learners to apply their new knowledge and carry out the procedure themselves. As they do, the teacher should guide, reinforce key points and question the learners to ensure accuracy.

It is this early stage of skill development that the learner is likely to make quick gains in their performance of the task (as outlined by Fitts and Posner above), so this is arguably the most important stage for a teacher to consider when introducing new and complex practical skills.

In summary, this post has focussed on the early stages of learning a new motor skill. The discussion is supported by Kirschner, Sweller and Clark, whose work with novice learners found that minimal guidance during instruction is less effective and less efficient than explicit instruction. Here we can see that this stage of learning a new skill requires a lot of teacher input, but this needs to be done so with accurate modelling and clear explanations. My next blog post will focus on stage 2 and 3 of Fitts and Posner’s model, where the teacher begins to move towards the role of a coach to support learners with fluency/automaticity with their skills.




Strictly deliberate practice

Okay, I admit it, I’m an avid Strictly Come Dancing watcher and my Saturday evenings are just not the same at the minute…


One of the many things I enjoy about the show is the progress made by the celebrity dancers over the course of the programme; in many cases, it’s phenomenal. What I find even more amazing is the work of the professionals aka the experts.

The professionals have been selected due to their expertise in a repertoire of dance disciplines. Each week, not only are they required to train a novice (celebrity) how to dance a particular routine, but they are also involved in at least one other routine during the show, which they perform with gusto and grace. In order to reach this standard, they have acquired thousands of hours of practice over a number of years and, as a result, have highly organised and fine tuned schema in their long term memories, which allows them to access new routines with efficiency and ease (I’ve discussed cognitive architecture in previous posts, so don’t intend to dwell on it here).

The work of Ericson and colleagues found that for individuals to become experts in their respective domains, it generally takes about 10 years of deliberate practice.

‘Simon and Chase (1973) observed that nobody had attained the level of an international chess master (grandmaster) “with less than about a decade’s intense preparation with the game”.’

They found similar results when reviewing other domains (teaching not included of course) and concluded that:

‘the differences between expert performers and normal adults reflect a life-long period of deliberate effort to improve performance in a specific domain.’

What does an expert teacher look like?

Whilst it is acknowledged that there is currently little consensus as to what constitutes an expert teacher, one could argue that as it currently stands, it is one that gains the highest value added achievement that is likely to be considered an expert. The often discredited work of Hattie and Marzano, along with studies in the domain of cognitive science provide us with many examples of methods and approaches that have greater impact on achievement. For example, we know that feedback which looks forward and is task-centred is more effective than no feedback or ego-centred feedback. We know that testing learners on material supports their ability to retain and retrieve knowledge. We know that spacing practice supports retention better than massed practice.

Using these (and the many other research informed approaches) as a barometer for expertise is arguably a starting point for all teachers in their deliberate practice towards expertise.

What is the difference between deliberate practice and normal practice then?

Ericsson et al inform us that ‘deliberate practice includes activities that have been specifically designed to improve the current level of performance’. They go on to state that key conditions for deliberate practice include: intrinsic motivation, feedback, and focused practice on specific areas of weakness. Unlike deliberate practice, normal practice is generally unstructured and feedback-free.

As a teacher, what can I do with this information?

This blog is timely in light of the recent Deans for Impact – Practice with Purpose release for teacher educators, and using the framework suggested will provide trainees with a good foundation for deliberate practice. For those already teaching, there are many aspects of this report that you might use, but as Ericsson et al point out, practice is not inherently motivating, requires time and is effortful. Therefore, it might be something you discuss with colleagues in order to cooperatively agree targets, structure practice opportunities, and monitor and provide feedback to one another to make progress towards ‘expertise’, or rather,  a 10 from Len!

Songs of Memory

A few weeks ago whilst teaching about Ebbinghaus’ forgetting curve and distributed practice*, one of my trainees was able to conceptualise it by way of learning songs… Here we go, I thought…


“You know when you hear a song for the first time, you can only remember a little of it, but the more you listen to it, the more you remember of the song?”


Well, yes. I suppose there may be a point to this. I thought about some examples that I could use to explain the forgetting curve and distributed practice via the ‘learning a song’ approach and here are my thoughts:


Twenty years ago, Puff Daddy (AKA P Diddy, AKA Sean Coombs, AKA whatever the latest is) and Faith Evans released ‘Missing you’ a song in memory of the Notorious BIG – Notorious! My summer was spent with the CD on loop, playing it over and over again. A 5 minute song turned into 5 weeks, until I became sick of it. To this day, I can still spit bars like the 13 year old me – word.


But was this distributed practice I thought? I mean, my whole summer was blocked with that song – I had overlearned it. Whilst I could use this to discuss the forgetting curve, I suppose frequent visiting over a long period wasn’t the best example to use for distributed practice…


I then thought about a song that I hadn’t heard as much, but with sufficient space between listening… aha! Christmas songs!


A yearly dose of Maria, George, The Pogues et al and… wait, I can’t say that I know all of the words to any of those songs… there’s bits I mumble my way through in an attempt to appear like I know, but I really don’t. Is it because I almost completely forget with such a long period between listening? Maybe.


So what could I use as an example? Well, I have struggled with this one. Would I have remembered ‘missing you’ if I hadn’t listened to it intensely for such a long time and overlearned it? Maybe I could have been more efficient with my time and had I thought about improving my memory of the song, would have listened to it a couple of times every week? Then I might have remembered a few other songs from that summer.


I can’t think of an example to demonstrate distributed practice, but I’m going to conduct a little experiment on myself and listen to the pogues twice a week up to Christmas in an attempt to get the words… then I might have a decent example to use in future. I’ll keep you posted! 

*For those of you that have no idea what I have just been on about, a summary of the forgetting curve and distributed practice can be read by clicking the links.

Experts and novices


This week I stumbled across a fantastic article online written by a self-taught card counter (Steve Pavlina) who, when reflecting on the Blackjack table was able to draw upon some lessons for life. I read this article and it immediately resonated from an educational perspective too.

Steve begins the article by outlining his fascination with the game and went on to outline how he became an expert at beating the casino:

‘I bought a book on blackjack, learned the rules of the game, memorized the basic strategy, and then studied a simple +/- card counting system. It took a heck of a lot of practice and was tedious to learn, but I eventually felt comfortable with it…Between Vegas trips I studied blackjack and card counting ever more deeply. I read 10-12 books on the subject and mastered different counting systems (Thorpe, Uston, Revere, etc.). I practised advanced counting systems that keep a side-count of aces. I drilled myself until I could count down a deck of cards in under 14 seconds. I learned to vary the play of hands according to the count, memorized optimal strategies for different rule sets, and learned the subtleties of the game that would increase my edge even the slightest degree. We’re talking a total edge of maybe 1%.’

Steve made some observations whilst playing. Below I have attempted to make sense of these through an education lens.

1. Novices will make correct decisions most of the time – It was observed that most of the time (80-90%), novices would make the same decisions as an expert, but cumulatively that 10-20% they make incorrect decisions have a big impact on their losses.


In education, we may assume that learners are learning well if, in most cases, they answer questions correctly, or produce a lot of work. Aside from these being generally poor proxies for learning (Coe, 2014), learners themselves may also believe that they’re doing well; mistaking their ability as superior to what it is (the Dunning-Kruger effect). This is dangerous because it’s the bits they may be getting wrong that cumulatively have a considerable impact on future learning (the 10-20%). Taking even the smallest misconception forward could make future learning less clear and more difficult.

Illustration by Oliver Caviglioli

For example, upon taking students into my Biology class, I have found many to arrive with the belief that all arteries carry oxygenated blood. Whilst in the vast majority of instances this is correct, it is a misconception that could cause confusion when later learning about pulmonary circulation, where in fact the pulmonary artery carries deoxygenated blood. The misconception should be corrected to ‘arteries carry blood away from the heart’, thus removing the confusion about oxygenated/deoxygenated blood. So what I’m getting at here is that we as teachers are supporting our learners’ development from novices to experts by not making assumptions about learning (as a result of insufficient assessment) and not allowing misconceptions to leave our classrooms.


2. Novices miss golden opportunities – It was observed that novices lost more money on the blackjack table due to a lack of understanding about when to gamble more and when to go bust; instead they tended to play it safe. Experts on the other hand would go bust more often and gamble high when the time was right. They used their knowledge of the odds to their advantage.


Daley found in her research of novice and expert learning that novices are ‘scared to death [and] terrified of making mistakes’, and that they want to be told what it is they needed to know in their learning. They are risk averse and as such don’t like to put themselves in positions where they may make a mistake. On the other hand, experts adopted a more constructivist approach to their learning, assimilating new information with old through experience, and because of a solid base of prior knowledge they were more inclined to know when to make calculated risks (or take golden opportunities). This is why it is essential that there is sufficient hand holding and teacher led instruction to ensure that the learner is provided with the key knowledge that they need, in order to develop into experts. Effective scaffolding should be slowly removed over a series of weeks/months to enable learners to become less dependent on the teacher and support their transition towards being an expert.

Illustration by Oliver Caviglioli


3. Novices don’t put in the time to fully understand the game – Novices don’t take the time to master the basics, whereas experts put in hours of practice and understand the basics and the more nuanced elements of the game.


Deliberate practice is crucial to becoming an expert according to Ericson et al who states that ‘many characteristics once believed to reflect innate talent are actually the result of intense practice’. Many novices (myself included) may be subject to the Dunning-Kruger effect so are misinformed and feel that they may not need the practice to master something. Our duty as teachers is to not only provide time to practise, but also encourage learners to understand the benefits of doing so (more on this below).

Illustration by Oliver Caviglioli



4. Experts are more disciplined – Experts tend to be more consistent in making decisions and taking action. Experts understand that you can make the correct decision and still lose, but they focus on making correct decisions, not on trying to force a particular outcome


In his book, David Didau (2015) informs us that ‘we are predisposed to examine the surface structure of a problem rather than recognising that its underlying deep structure is the same as something we already know’. In essence, when approached with a new problem, unless we are an expert, we are less likely to make links with existing knowledge and prior experiences to solve a problem. Novices simply don’t have sufficient information to draw upon and so can’t make informed decisions, thus focusing on the detail, whereas experts are more likely to focus on the structure of a problem and take a more consistent approach. For example, if given a maths problem to solve, the expert may think of similar problems they’ve faced and compare the structures to help them make sense of the information, whereas a novice may just try to tackle the problem without an idea of what they’re trying to find, or what the outcome might be. With this in mind, teachers need to be modelling explicitly how to approach problems making use of prior knowledge, before scaffolding problems for learners with support mechanisms that can be removed once experience is acquired.

Illustration by Oliver Caviglioli



5. Private victory precedes public victory – Experts spend a lot more time practising, which takes tremendous patience. Their real victories aren’t at the blackjack table, but in their homes practising.


As mentioned above, expert performances only arise through dedicated and deliberate practice. This according to Ericsson et al requires motivation and perseverance, which in itself is problematic, particularly if we want learners to engage in deliberate, directed practice outside of the classroom.

‘Deliberate practice is not inherently enjoyable and that individuals are motivated to engage in it by its instrumental value in improving performance. Hence, interested individuals need to be engaging in the activity and motivated to improve performance before they begin deliberate practice.’

So our role as educators is to establish an environment where learners focus on long term improvement through having a high self-efficacy for learning. To avoid learned helplessness and to encourage a high self-efficacy we should guide students towards success through modelling, scaffolding and giving sound feedback to help move them forward.

Illustration by Oliver Caviglioli


In summary, to support our learners from novice to expert we need to treat them as a novice initially and not as an expert. If we try to teach our novice learners to be scientists by giving them inquiry based science projects to complete, or treat them as hair stylists by placing them straight into a hair salon, they will act as novices (Kirschner et al). I believe, based upon what I have written (here, here and here) that the following approaches should be taken to support our learners to become experts:

  • We are experts in the subject matter ourselves
  • We plan the learning to maximise long term retention (distributed and interleaved practice)
  • We model correct practice and chunk the learning to reduce cognitive load
  • We scaffold difficult concepts to enable learners to more easily understand, before slowly removing the support mechanisms to allow greater independence
  • We provide regular opportunities for retrieval practice
  • We provide learners with sufficient time and space to practise, hone their skills and take necessary risks
  • We support the transfer of knowledge and skills within the subject through well planned and scaffolded activities.
  • We conduct regular checks on all learners’ understanding which goes beyond that of superficial questioning/observation
  • We provide task-oriented, rather than ego-oriented feedback in a timely and specific manner to move learning forward
  • We involve learners in their own assessment and one another’s against clear success criteria
  • We actively encourage learners to practise beyond the classroom through challenging homework that feeds into future lessons


Special thanks go to Oliver Caviglioli for his brilliant visuals to support the text.


When I was a youngster, my nan collected her spare change in a huge glass bottle for me. At the time, I think the bottle was probably my height – it was huge and made of thick, clear glass. Every so often she would allow me to pour the contents of the bottle out and count it. This was the fun part!  Once the money had been counted, the arduous task began. This involved getting the coins back into the bottle; grabbing a handful at a time and slowly releasing them into the bottle neck. The main bottle could hold what seemed like endless amounts, but getting the coins in was no easy task.


The more I did it, the more I realised that if I collected the same coins together and put them into small piles, the more efficient I could become as they would slide in smoothly, rather than attempting to drop a load of random shaped and sized coins in, which would fight to get in through the bottleneck.


In 1956 George A Miller asserted that our capacity for processing information is limited to seven, plus or minus two pieces of information. This later led to the working memory model by Baddeley and Hitch. Essentially, the working memory (WM) is the narrow bottleneck to the huge long term memory we have. The working memory can only handle a limited amount of information at one time (much like the bottleneck can only handle a limited amount of coins) and therefore, the more efficient our methods of teaching are, the more we are likely to minimise ‘overload’ in order to aid long term memory (the endless bottom of the bottle).


Chunking information for learners seems an obvious way to do this, doesn’t it? How many of us do though? I am certainly guilty of trying to cram lots into lessons from time to time, leaving learners bamboozled and actually causing me more work later down the line. Here’s some ideas as to how you might ‘chunk’ the learning to support learners in processing information more effectively in lessons:


1. Firstly we need to understand what our learners already know. If we can link the new information to this, then we can reduce the burden on WM. Using multiple choice quizzes at the start of lessons can provide you with some information on this. Furthermore, knowing other things about your learners is always useful for analogies and metaphors.

2. Secondly we should try to chunk information so as not to burden the WM of learners (we can do this best following the above). This might include:

  • organising key concepts visually for learners in advance of the teaching (advanced organisers). For example, showing how the concepts/components of a topic relate to each other to form the whole.
  • breaking concepts down into their component parts (chunks) for delivery. For example, breaking a skill down into its simplest form before building each part together once mastered.
  • using mnemonics – further information can be found in a previous post here
  • using analogies and metaphors to help learners to link new information to prior knowledge. As mentioned above, the more we know about what our learners know, the more we will be able to link new learning to it. More information can be found here
  • using visual representations of things being explained, so that both the visual (visuo-spatial) and the auditory (phonological) information can ease the burden on the WM. See further information here

3. Finally, we need to be conducting regular formative assessment to ensure that we are monitoring the learner’s WM. We can then determine whether further support is required to address misconceptions, or whether we can move forward with additional learning. A post on formative assessment can be found here.


So when attempting to maximise the impact of your teaching, try thinking about getting a load of coins into a bottle*

*The astute of you may have noticed what I’ve done in this post…

Metacognition – How can you do it?

Metacognition is a bit of a buzzword in the education sector, but if I’m honest, I have always been a bit wary of it. I think it’s one of those terms that gets used without much understanding of it. In essence it means ‘thinking about thinking’.


The concept has been broadly and rather loosely defined as ‘any knowledge or cognitive activity that takes as its object, or regulates, any aspect of any cognitive enterprise’ (Flavell, Miller, & Miller, 2002 cited in Waters and Schneider, 2010). This may include, but is not limited to planning how to approach a given learning task, monitoring comprehension, and evaluating progress toward the completion of a task. Arguably many of the above overlap, but both classroom experiments and cognitive science have found metacognitive strategies useful to cement learning and also develop ‘higher order thinking’ within a domain. For example, the Education Endowment Foundation recently found metacognitive strategies to have an effect of +8 months on achievement, though it must be noted that this meta, meta-analysis is unspecific and encompasses a broad range of ‘metacognitive skills’, including less effective strategies such as ‘changing mindset sessions’. Despite this, broadly speaking, 8 months is pretty much a whole school year, so if we use strategies with learners that get them thinking about their thinking, then we may be able to increase achievement.  Below I attempt to provide some clarity on how we might use a range of metacognitive skills in practice, using  Gorrell et al’s (2009 cited in Sart, 2014) list of skills:

Metacognitive skill What this might look like in practice
Evaluation (or criticality of sources or task success). Provide learners with regular opportunity to peer and self-assess against success criteria. Encourage them to ask the following: ‘What am I doing well/how well did I do against success criteria? What can/could I do better? How will I do it better next time?
Monitoring (the assessment of progress through a cognitive task).
Metamemory (a person’s knowledge and awareness of his or her memory usage). Help learners to understand what they already know and how they best remember new information? Do they remember best by creating a mnemonic? By using analogies or metaphors? By taking notes? By self-testing?
Metacomprehension (an awareness of the extent to which a task is understood). Self-explanation – is a comprehension-monitoring approach to learning where the learner explains what they have learnt and how it links to prior learning. In practice, towards the end of a lesson, try asking the learner to write an explanation of their understanding. For example: ‘How do I know that Earth is closer than Mars to the Sun?’
Planning (appropriate structure is assigned to the task). Encourage learners to plan effectively prior to tackling a problem or learning task. Learners could be provided with cue questions such as: What am I supposed to learn from doing this task? What prior knowledge will help me with this task? What should I do first? How much time do I have to complete this?
Schema training (the generation of a cognitive framework to help understand the task). Graphic organisers can be used to help learners visualise their knowledge and understanding.  This visualisation of knowledge and understanding yields high effect-sizes according Marzano. Try using graphic organisers for comparison (Venn diagrams), for classification (Flow charts) for metaphors, or for cause and effect etc.
Transfer (the ability to use strategies learned on one task to complete a different task). The Learning Scientists expertly discuss the notion of transfer in this series of posts (1 and 2). In essence, near transfer (a closely related problem) is easier to achieve than far (a problem without the same prerequisite knowledge). They argue that learners should be supported to:

1.     Recognise that it is a transfer situation (i.e. that they have prior knowledge on the problem/task) – Try  to explicitly inform learners of this when presenting new problems/tasks.

2.     Retrieve the prior knowledge/skills – Try to build in retrieval practice of knowledge and skills through testing, distributed and interleaved practice.

3.     Know how to apply this to the problem/task – Try to provide opportunities for learners to apply their knowledge in different ways.

All of the above lead me to argue the case for scaffolding well when attempting to teach these skills. Each of the strategies are tools that require attention when planning and delivering them to learners. They should be effectively modelled by the teacher before learners are supported with prompts to enable them to become more independent in their ‘thinking about thinking’. Effectual use of these strategies by learners is not something that will happen over night; like anything, it will take time to become proficient in using them. With this in mind, why not pick something that you can introduce to your teaching this forthcoming academic year and see if it has benefit to your learners?

Playing with science

A teacher shortage recently led to me being asked to take Biology for the last 10 weeks of an Access to HE course (this includes holidays). My background in sports science lends itself well to the subject and having deliberately tried to implement some key principles of cognitive psychology (science) into my practice recently, I thought it would be a great opportunity to experiment further with this biology class.


With a subject that requires excellent domain knowledge, it is important that I structure the content in a way that minimises cognitive load. According to Sweller (here and here), Cognitive Load Theory extends upon Miller’s work, positing that working memory has a limited capacity and therefore learners cannot perceive all information that they encounter, particularly if it is presented in a poorly organised way.  Ultimately, the more we can limit the burden on our working memory, the more likely it is that we can create schema (memory patterns) in long-term memory. In order to reduce working memory load we must ensure that instruction is designed effectively. Sweller informs us that cognitive load can be separated into three forms:

  1. Intrinsic load is the level of difficulty associated with the topic (in this case Biology).
  2. Germane load is related to the work put into creating a permanent store of knowledge (schema).
  3. Extraneous load is the way things are delivered to the learner and if this is not mapped well to the learner’s existing knowledge and understanding (schema), then it may become burdensome.

If we can reduce Extraneous load (by having sound instructional design), then this means that we can free up working memory to increase the Germane load – a good thing for developing long term memory.


With the above mentioned in mind, I created the following outline/delivery schedule. Below this, you will see a rationale with links to research for why I planned as I did.

plan A

  1. The module covers three interlinked topics: the respiratory system, the cardiovascular system and blood. You will see that topics are spaced in order to be revisited every 2-3 weeks, which is considered about optimum according to the research of Dunlosky et al. Furthermore, there is no pattern to the way the topics are planned, instead I have attempted to interleave the topics, which is another aspect of design that supports long-term retention.
  2. Despite only being taught for 10 weeks (2 hours per week), learners revisit each topic area on at least two occasions by way of formal exposition and then throughout tests from week to week. Where gaps in knowledge are identified, this is immediately addressed. Repetition of knowledge in various ways occurs through the workbooks that they complete during each lesson. For example, labelling diagrams, gap fills, self-explanation, and multi-choice questions. This means that they are revisiting key information many times both within and beyond the lesson.
  3. You will see that I have built in tests everywhere. Testing offers multiple benefits to learners, as highlighted by Roediger et al.  here, not least to identify gaps in knowledge and also provide opportunities to practice retrieval of information, which strengthens the memory trace. These tests are planned in an array of ways, including initial and end of lesson multiple choice quiz, teacher questioning, self-quizzing, and Google form multiple choice quiz which is sent a few days after each session. Online learning also culminates with an online quiz to be completed following a period of learning (via video/reading).
  4. I planned for all exposition to include clear visuals to reduce cognitive load. This includes a combination of simple and more complex diagrams. In splitting the attention using a multi-mode delivery (both visual and audio information), for example, the flow of blood through the heart, the learners are able to more easily understand key processes.
  5. Analogies and metaphors (see previous post) are also frequently included in explanations in order to reduce cognitive load. For example, the flow of blood through the heart is like a heating system. The red blood cells bio-concave shape means it is like a rubber ring which can be manipulated to alter its shape etc. These help learners link new information to simple things that they should already be aware of and thus support the acquisition of knowledge.
  6.  Use of memory aids whilst delivering information is also planned. This tends to occur with complex terminology, for instance ‘erythrocytes’ (red blood cells) aka ‘Aretha Franklin’ (similar sounding). ‘S‘ympathetic nervous system ‘S‘peeds things up (Same starting letter). Mnemonics (see previous post) for the components of the respiratory system: ‘Never, Over, Play, Long, Through, Balls, Because, [it’s] Awful’ (Nasal cavity, Oral cavity, Pharynx, Larnynx, Trachea, Bronchus, Bronchiloes, Alveoli). Each of these reduces the strain of learning complex terminology and therefore should assist in the acquisition of this information.


If I’m honest, the above practice isn’t anything drastically different to how I’d typically teach, but I am being more purposeful in the planning and delivery of content to enhance the learning experience. I’m just about to start week 5 and already several learners are commenting that they feel they’re learning a lot and that it is sticking and quiz results corroborate this. Initial reflections are around how I might further enhance Germane load through the organisation of the content, so if I’m given the opportunity to do this again, then the above plan is likely to change.


Can you use any of the above principles in your planning to make your teaching just that little bit better?

The Butterfly Effect: Behaviour Management


Anyone who knows me will be aware of my penchant for Monster energy drinks (there are other brands available). At the start of my 50 minute drive to work in the morning, I tend to ‘crack’ open the can in the hope that I will have slowly consumed the 500ml of chemical infused liquid by the time I arrive at work, where I will be all set for the day ahead.


One morning last week, I made a small (bad) decision at the start of my journey that had a knock on effect for the rest.


So the bad decision I made? Well, rather than rid the cup holders in my car of the empty cans of past drives, I decided to leave them be. My drive began as usual and around 5 minutes in, I opened my fresh can of Monster. However, my decision not to remove the old cans meant that I had no where to place the new can between sips. I could have thrown the old cans on the passenger floor, but decided that I would just keep the can between my legs. One sharp manoeuvre later left me with a seriously wet and squelchy seat. My trousers were drenched. I had 40 mins left of the drive to go, so thought I could dry them off by directing the fans to my crotch and turning up the heat and fan to maximum settings. I thought I had it solved, yet ten minutes further along, a burning smell started to make its way around the car. Then the hot air turned to warm, warm went to cool and cool remained. Wet bum and broken heater all due to one bad decision.


I happened to be teaching a session on behaviour management on this day and there was something that resonated about this incident. If we make one bad choice when it comes to behaviour, we could potentially create a much bigger problem for ourselves. Whether it be ignoring that swear word that was said, or allowing learners to sit where they want, this small action may have a knock on effect to more inappropriate behaviour with the learners.


When I started my first teaching role as a fresh faced graduate, I was little more than a few years older than the learners I was teaching. My initial decision was to try and find a common ground and gain respect by being the ‘cool teacher’. I didn’t really have any particular behaviour management strategies and just went in to lessons to teach. At first, the group responded well. They would work generally hard and remain focussed. But on the odd occasion where there was misbehaviour, I would ignore it, or make a joke to help refocus the class on me. As the year went on, I felt that I was losing more and more respect from the group. If they didn’t want to do work, they wouldn’t do it. If I challenged, they would laugh. It was tough. I got through the first year and was adamant that this would not happen again. A few simple things that I developed over subsequent years to help with this was:


  1. Ownership – I owned my  classroom. Meeting and greeting learners at the door allowed me to control them coming in. I could acknowledge each and every one of them, I could seat them where I wanted, I could ask them to remove hats/stop eating before entering the room. Further to this, I would change the classroom layout frequently to prevent poor routines from being established. This also helped me to establish a more inclusive environment.
  2. Policy – I used the behaviour policy of the college in a consistent manner. If a learner was breaching the ground rules, then I would ensure that the consequences were adhered to. This was not only a benefit to them, but also ensured that I covered my own back – there is no way you can complain about classroom behaviour to a superior if you haven’t used the behaviour policy correctly.
  3. Challenge – I kept my lessons challenging for all learners so that they didn’t become bored. In retrospect, at the start of my career, I often tried to do this in a fun way, but realised later that fun was often at the expense of learning. My recommendation to anyone is to get the learners to think hard about what they’re meant to be learning.
  4. Relationships – I ensured that I kept relationships professional. In doing this,  I tried to know everything I could about each individual that entered my room, ensuring that I gave them all the individual attention they need, whilst acquiring information about what made them ‘tick’. I learnt fast that celebrating the positive behaviours was far more productive than highlighting the negatives and this not only helps the learners to learn the acceptable behaviour, but also helps with building strong working relationships.


Anyone that is aware of behaviour management theory will see that my strategies are underpinned by an assertive discipline approach (Canter, 1976), whose roots are formed by operant conditioning (Skinner, 1938). Evidence suggests that this approach to disciplinary interventions can have a huge effect-size of between 0.7 and 0.9  (Marzano, 2003 cited in Petty, 2006). However, I am not for one minute saying that this is foolproof, but used and developed over time, it helped me to improve the behaviour in my classroom. The more experienced I became, the better behaviour became, but there would still be occasions where I would make a bad decision which would have a knock-on effect, causing chaos in my classroom. On reflection, the bad decisions did tend to occur when I was being a ‘lazy teacher’ –  The days when I arrived at the classroom ten seconds prior to the start, or the times when I just couldn’t be bothered to address a poor behaviour. This is much like the lazy decision I made when not removing my litter from the car. On that note, I’m off with my bin bag in tow.