Cognitive ScienceCognitive Science (along with Cognitive Psychology - often the two are hard to separate) is concerned with how people think, what motivates them to think, the importance of what they think about, and what conditions foster effective thinking. For Daniel Willingham, memory is the residue of thought (I love that phrase!), so if we can help students think, then we can help them remember and learn.
Research Paper Title: The Science of Learning
Author(s): Deans for Impact
This paper was a game-changer for me, and is a superb way to begin any voyage into the world of educational research. It is a summary of existing cognitive science research about how students learn. The questions addressed are:
1. How do students understand new ideas?
2. How do students learn and retain new information?
3. How do students solve problems?
4. How does learning transfer to new 4 situations in or outside of the classroom?
5. What motivates students to learn?
6. What are common misconceptions 6 about how students think and learn?
Each of the cognitive principles is stated clearly, and is presented alongside classroom implications, making it both practical and incredibly teacher-friendly. All of the principles identified are fascinating (and references are provided if you wish to dig into each one deeper) and most will be covered at length on this page. However, what struck me in particular were the common misconceptions about how students think and learn: Students do not have different “learning styles”; Humans do not use only 10% of their brains; People are not preferentially “right-brained” or “left brained” in the use of their brains; Novices and experts cannot think in all the same ways; and Cognitive development does not progress via a fixed progression of age-related stages. How many of those have you heard? How many have you had training on? As I say, this was the paper that first opened my eyes. It is an essential read for all teachers.
My favourite quote:
The Science of Learning does not encompass everything that new teachers should know or be able to do, but we believe it is part of an important — and evidence-based — core of what educators should know about learning.
Research Paper Title: Do Learners Really Know Best? Urban Legends in Education
Author(s): Paul A. Kirschner and Jeroen J. G. van Merrienboer
Before we get into what Cognitive Science does recommend as good practice for improving student learning, I feel it is worth considering one further paper to debunk a few incredibly pervasive educational myths. In this fascinating paper, the authors tackle three "urban legends", all of which I have heard (and attending training on!) in recent years. For each one they provide examples of the myth, together with supporting research to debunk it. If you enjoy this, then you will absolutely love Daisy Christodoulou's wonderful book Seven Myths about Education, which investigates these issues and more. The urban legends tackled in this paper are:
1. Learners as digital natives who form a generation of students knowing by nature how to learn from new media, and for whom “old” media and methods used in teaching/learning no longer work.
2. Learners have specific learning styles and that education should be individualized to the extent that the pedagogy of teaching/learning is matched to the preferred style of the learner.
3. Learners ought to be seen as self-educators who should be given maximum control over what they are learning and their learning trajectory. This latter point has been an absolute game-changer for me and will be discussed in detail in the Explicit Instruction section.
My favourite quote:
Our analysis of three urban legends in teaching and education clearly shows that, although widespread, widely believed, and even widely implemented as well-meaning educational techniques or innovations, they are not supported by scientific evidence. It should be clear by now that students are really not the best managers of their own learning with respect to navigating through and learning in the digitalworld, choosing the best way inwhich to study and learn (i.e., learning styles), or gathering useful information from the Internet. However, a continuum of available evidence exists for refuting these and other legends. At one extreme, are urban legends for which there is a tiny bit of incomplete support—but the legend itself is false or at least a severe overgeneralization (e.g., the claim that giving learners full control over the learning process will have positive effects on learning). At the other extreme of the continuum are urban legends for which there is strong empirical evidence for the opposite, showing that they are totally counterproductive in education (e.g., the claim that children are capable of effective multitasking). Finally, there are urban legends for which researchers claim that there is evidence, and for which there are even empirical studies purporting to support the legend, but the research itself or the body of research is flawed. This was demonstrated for the learning styles hypothesis but also, for example, by Lalley and Miller (2007) with respect to the learning pyramid and by Kirschner, Sweller, and Clark (2006) with respect to minimal guidance during instruction. It is yet true for all legends that they are primarily based on beliefs and convictions, not on scientific theories supported by empirical findings.
Research Paper Title: Working Memory: Theories, Models, and Controversies
Author(s): Alan Baddeley
Many models of memory are based upon the model proposed by Alan Baddeley, and this paper provides a wonderful introduction into the development of the model. It explain the multi-component aspects of working memory (divided up into the phonological loop, the visuo-spatial sketchpad, the episodic buffer, all governed by the central executive), and later how this interacts with long term memory. This model provides the basis for much of the work on this page, and you could very well make the argument that trying to understand working memory - and specifically the limits of working memory - is just about the most important thing a teacher can do.
My favourite quote:
What then are the essentials of the broad theory? The basis is the assumption that it is useful to postulate a hypothetical limited capacity system that provides the temporary storage and manipulation of information that is necessary for performing a wide range of cognitive activities. A second assumption is that this system is not unitary but can be split into an executive component and at least two temporary storage systems, one concerning speech and sound while the other is visuo-spatial. These three components could be regarded as modules in the sense that they comprise processes and storage systems that are tightly interlinked within the module and more loosely linked across modules, with somewhat more remote connections to other systems such as perception and LTM
Research Paper Title: Executive Attention, Working Memory Capacity, and a Two-Factor Theory of Cognitive Control
Author(s): Randall W Engle and Michael J. Kane
This comprehensive paper aims to describe the nature of working memory capacity (WMC), specifically with regard to its limitations, the effects of these limitations on higher order cognitive tasks, their relationship to attention control and general fluid intelligence, and their neurological substrates. I'll be honest with you, things got a little too technical for me, but the key finding was a clear one: higher order cognitive tasks are easier if you have a higher working memory capacity. To quote the authors: One of the most robust and, we believe, interesting, important findings in research on working memory is that WMC span measures strongly predict a very broad range of higher-order cognitive capabilities, including language comprehension, reasoning, and even general intelligence. This makes perfect logical sense when we look at the models of how students think later in this section, and especially when we consider the findings from Cognitive Load Theory. Unfortunately, as we will also see, it may not be possible to expand the capacity of students' (or indeed anyone's) working memories, at least not in a general way that can be transferred to all cognitive situations.
My favourite quote:
We proposed a two-factor model by which individual differences in WMC or executive attention leads to performance differences; We argued that executive attention is important for maintaining information in active memory and secondly is important in the resolution of conflict resulting from competition between task-appropriate responses and prepotent but inappropriate responses. The conflict might also arise from stimulus representations of competing strength. This two-factor model fits with current thinking about the role of two brain structures: the prefrontal cortex as important to the maintenance of information in an active and easily accessible state and the anterior cingulate as important to the detection and resolution of conflict.
Research Paper Title: Why don't students like school? Because the mind is not designed for thinking.
Author(s): Daniel T. Willingham
This is an excellent summary of one of my all-time favourite books, Why Don't Students Like School. There is so much to takeaway from this, but the biggest thing for me is about how students think. Students (like all of us) are naturally curious and enjoy thinking, but that curiosity is fragile, and if the conditions are not right then students will avoid thinking. For Willingham, memory is the residue of thought, so unless students are thinking, there is little chance they will remember or learn. Willingham stresses that successful thinking requires three things:
1) information from the environment
2) facts and procedures stored in long term memory
3) space in working memory.
When students are given new information, they hold it in working memory as they connect it to other new information and experiences and evaluate it against known concepts. The problem is that working memory capacity is limited, and gets filled when students need to carry out basic procedures or search for facts. Hence, when faced with complex problems, if those facts and procedures are missing from long term memory, working memory gets filled up, thinking becomes hard, students stop doing it, and no learning takes place. Here is the key point: experts and novices think differently. Experts rely on retrieving whole schema (connected items of information) from long-term memory to get around the limits on our fragile working memories. Novices (i.e. most students) don’t have these memorised schema to rely on so attempt to hold too much information in their working memories which leads to cognitive overload. Therefore, we can make thinking easier for students by ensuring they have access to sufficient knowledge and procedures stored in long term memory, which allows them to develop these schema, which frees up capacity in working memory, which can then be used for problem solving. As teachers we can help students acquire this knowledge and these procedures via Explicit Instruction, emphasising the value of deliberate practice to improve, not taking study skills for granted, and praising effort not ability. Furthermore, once embedded, we can help students retain this knowledge and these procedures using the work discussed in the three Memory sections. It's as easy as that ;-)
My favourite quote:
Teachers often seek to draw students in to a lesson by presenting a problem that they believe interests students, or by conducting a demonstration or presenting a fact that they think students will find surprising. In either case, the goal is to puzzle students, to make them curious. This is a useful technique, but it’s worth considering whether these strategies might also be used not at the beginning of a lesson, but after the basic concepts have been learned.
Research Paper Title: Is Working Memory Training Effective? A Meta-Analytic Review
Author(s): Monica Melby-Lervåg and Charles Hulme
A question that might arise from reading Willingham's work is "can we increase working memory capacity?", because if we can then thinking will be a lot easier for students. In recent years, the there has been an increase in the popularity of brain training games and strategies, which claim to boost this all important working memory capacity. Unfortunately, the findings of this comprehensive meta-analysis of existing research suggest that any benefits are short-term, and crucially do not transfer to other situations. So, whilst you may be able to train your brain to hold more of a specific type of written or oral information, this will not transfer across to enable you to solve more complex maths problems. However, all is not lost, because we can get around the limits of working memory using the power of knowledge, as the next two papers in this section discuss.
My favourite quote:
Currently available working memory training programs have been investigated in a wide range of studies involving typically
developing children, children with cognitive impairments (particularly ADHD), and healthy adults. Our meta-analyses show clearly that these training programs give only near-transfer effects, and there is no convincing evidence that even such near-transfer effects are durable. The absence of transfer to tasks that are unlike the training tasks shows that there is no evidence these programs are suitable as methods of treatment for children with developmental cognitive disorders or as ways of effecting general improvements in adults’ or children’s cognitive skills or scholastic attainments.
Research Paper Title: A Simple Theory of Complex Cognition
Author(s): John R. Anderson
I am not entirely sure that "simple" is the best description of this theory, at least not for me anyway! In the Adaptive Character of Thought (ACT—R) theory, complex cognition arises from an interaction of procedural and declarative knowledge. Declarative knowledge is represented as an associative memory network which contains the facts known by the system. It is represented in units called "chunks". Procedural knowledge is represented as a production system and enables the system to apply its knowledge and execute behaviour to achieve its goals. It is represented in units called production rules. A great many such knowledge units underlie human cognition. From this large database, the appropriate units are selected for a particular context by activation processes that are tuned to the statistical structure of the environment. Declarative knowledge can be acquired quickly from direct encoding of the environment, while procedural knowledge takes longer and must be compiled from declarative knowledge through practice. After a certain amount of practice, the path or production becomes stable and procedural learning has occurred. The conditions under which we learn procedures, therefore, are determined by existing declarative knowledge. According to the ACT-R theory, the power of human cognition depends on the amount of knowledge encoded and the effective deployment of the encoded knowledge. The main message is a crucial one: to be able to think, we need knowledge, and the more knowledge the better. Long-term memory is capable of storing thousands of facts, and when we have memorised thousands of facts on a specific topic, these facts together form what is known as a schema - connected items of information. When we think about that topic, we use that schema. When we meet new facts about that topic, we assimilate them into that schema. In other words, when students are given new information, they hold it in working memory as they connect it to other new information and experiences and evaluate it against known concepts. Crucially, if we already have a lot of facts in that particular schema, it is much easier for us to learn new facts about that topic. New knowledge builds on existing knowledge. And how best to learn and retain these facts? Well, the sections on Explicit Instruction, Cognitive Load Theory, and Memory should provide some answers.
My favourite quote:
All that there is to intelligence is the simple accrual and tuning of many small units of knowledge that in total produce complex cognition. The whole is no more than the sum of its parts, but it has a lot of parts.
Research Paper Title: Brain Changes in the Development of Expertise: Neuroanatomical and Neurophysiological Evidence about Skill-Based Adaptations
Author(s): Nicole M. Hill & Walter Schneider
This paper proposes that experts (those with high levels of domain-specific knowledge) don't just know more than novices, they actually think in a fundamentally different way. They explain that experts differ from novices in terms of their knowledge, effort, recognition, analysis, strategy, memory use, and monitoring, and that these differences are due to the structure of their long-term memories. As we learn, our brain architecture changes and thoughts are processed differently. This means that as we move to mastery of a given skill or concept, our brains form different links in long-term memories, and it is actually possible to observe different activation patterns during problem solving. They conclude that in addition to processing efficiency, enriched representations, and structural expansions, experts can flexibly use strategies, by recruiting the associated brain regions, to solve a range of problems, whereas novice performers can not. The fact that experts and novices think in a fundamentally different way, and that knowledge is at the route of this, will have huge implications throughout this page, but for for Problem Solving in particular.
My favourite quote:
The specific nature of the representational areas suggests that both training and performance will be sensitive to the strategy and nature of the training. What is learned is based on which representational areas are active during training. Typically, as practice develops, activity decreases, and there are rarely new areas that develop in laboratory studies of skill acquisition. This suggests that training causes local changes in the specific representational areas that support skilled performance. In studies of extensive training, there is ample evidence for changes in cognitive processing as well as structural changes in the nervous system.
Research Paper Title: How Knowledge Helps
Author(s): Daniel T. Willingham
This is a great paper, with the same powerful message as the one above by Anderson: knowledge does much more than just help students hone their thinking skills: it actually makes learning easier. Willingham argues - providing links to research along the way - that knowledge helps students take in more information, think about new information, and remember new information. What will perhaps be of most interest to teachers is that knowledge helps students solve problems. This sounds ridiculously obvious, but I don't think it is - at least not to me, anyway! One teaching strategy that I have been guilty of over my career is assuming that exposing students to lots of problems will help them become good problem solvers. The simple answer is that it won't. Without existing knowledge, the student cannot recognise familiar patterns (the ability to "chunk" information is crucial here and is related to the concepts of schema above), and hence attempts to solve problems individually, which inevitably overburdens working memory. This issue is discussed more in the Problem Solving section.
My favourite quote:
Those with a rich base of factual knowledge find it easier to learn more—the rich get richer. In addition, factual knowledge enhances cognitive processes like problem solving and reasoning. The richer the knowledge base, the more smoothly and effectively these cognitive processes—the very ones that teachers target—operate. So, the more knowledge students accumulate, the smarter they become.
Research Paper Title: Categorization and Representation of Physics Problems by Experts and Novices
Author(s): Michelene T H Chi, Paul J Feltovich and Robert Glaser
I found this study fascinating. We have seen in the paper above that knowledge is crucial to aid thinking, and that an obvious distinguishing feature between experts and novices is that experts have more knowledge than novices. Here we have clear evidence of just how big an advantage that extra knowledge is - it actually enables exerts to see problems differently. The authors asked physics novices (undergraduates) and experts (PhD students) to sort physics problems into categories. The novices sorted by the surface features of a problem—whether the problem described springs, an inclined plane, and so on. The experts, however, sorted the problems based on the physical law needed to solve it (e.g., conservation of energy). The advantage of the latter approach was that the experts could see the deeper structure of the problem, which meant they could more readily access the appropriate schema to solve the problem, which aided transfer. Novices, on the other hand, focused on the surface structure and hence did not make the necessary connections between problems. experts (those with strong subject knowledge) don't just know more than novices - they actually see problems differently. It is not the use of an inefficient means-ends strategy that allows experts to solve problems so readily. Instead, experts have richly organised knowledge (analogous to problem schemas) that allow them to represent the problems in such a way that the solutions became transparent. Indeed, experts are likely expanding less mental energy solving problems than novices because they have the relevant schemas in place. Because of their extensive domain-specific knowledge, experts quickly recognise the problem’s deep structure, relate it to similar problems they have met in the past, and embark upon a (usually successful) strategy to solve it.
An immediate implication for me here is going through past papers with students. How often do we find that students focus on the surface structure of a problem? A question about lowest common multiple set in the context of bus timings on one paper becomes a question all about buses, and no connection is made between a question with essentially the same deep structure but set in the context of boxes of cakes and donuts on another paper. They are treated as two completely separate problems. Without strong, domain-specific knowledge, students are unlikely to recognise the deep structure of problems, and hence will be unlikely to transfer the knowledge successfully to different situations. Going through past papers, covering loads of different topics, before learners are secure i each of those topics, is likely to be a waste of time. It is perhaps better to focus on domain-specific knowledge (i.e. teach the basics of lowest common multiple so the calculations become fluent) and then carefully expose students to problems in different contexts but with the same deep structure, guiding them by means of example-pairs, clearly articulating your thought processes. These techniques are covered in detail in the Cognitive Load Theory and Problem Solving sections.
My favourite quote:
Our research goal has been to ultimately understand the difference between experts and novices in solving physics problems. A general difference often found in the literature and also in our own study is that experts engage in qualitative analysis of the problem prior to working with the appropriate equations. We speculate that this method of solution for the experts occurs because the early phase of problem solving (the qualitative analysis) involves the activation and confirmation of an appropriate principle-oriented knowledge structure, a schema. The initial activation of this schema can occur as a data-driven response to some fragmentary cue in the problem. Once activated, the schema itself specifies further (schema-driven) tests for its appropriateness. When the schema is confirmed, that is, the expert has decided that a particular principle is appropriate, the knowledge contained in the schema provides the general form that specific equations to be used for solution will take.
Research Paper Title: Top 20 Principles from Psychology for PreK-12 Teaching and Learning
Author(s): American Psychological Association
This is a wonderful collection of key principles from psychology that have direct implications for teaching and learning, all of which are backed up by research. Many of these have directly influenced how I plan and deliver my lessons, but for the sake of brevity, I will focus on just three here:
1) What students already know affects their learning. This so ridiculously obvious, but is also ridiculously easy to overlook - at least for me, anyway. The authors explain that learning consists of either adding to existing student knowledge (conceptual growth), or transforming or revising student knowledge (conceptual change). Conceptual growth cannot occur is their existing knowledge is incomparable with the new knowledge, or incomplete, and strategies such as Formative Assessment may be needed to establish this. Likewise, conceptual change can be tricky to achieve if students hold misconceptions, and simply telling students they need to think differently will generally not lead to substantial change in student thinking. Ways to deal with this are covered in the Explicit Instruction section.
2) Students tend to enjoy learning and to do better when they are more intrinsically rather than extrinsically motivated to achieve. This is directly related to research on Motivation discussed later on this page. As students develop increasing competence, the knowledge and skills that have been developed provide a foundation to support the more complex tasks, which become less effortful and more enjoyable. When students have reached this point, learning often becomes its own intrinsic reward. This is obviously preferable to being externally motivated, as students may disengage once the external rewards are no longer provided. However, one point I feel is worth mentioning is that some (or even all) students may need a certain degree of external motivation during the initial stages of learning a topic, before mastery has a chance to become its own reward.
3) Students persist in the face of challenging tasks and process information more deeply when they adopt mastery goals rather than performance goals. This is very much related to the work on student Mindset, but has some very practical applications. Students can engage in achievement activities for two very different reasons: They may strive to develop competence by learning as much as they can (mastery goals), or they may strive to display their competence by trying to outperform others (performance goals). Performance goals can lead to students’ avoiding challenges if they are overly concerned about performing as well as other students. In typical classroom situations, when students encounter challenging materials, mastery goals are generally more useful than performance goals. How do we help develop these mastery goals? The authors advise strategies such as avoiding social comparisons, avoiding non-task-specific feedback such as "brilliant", conduct student evaluations in private, and focus on improvement over achievement.
My favourite quote:
Psychological science has much to contribute to enhancing teaching and learning in the classroom. Teaching and learning are intricately linked to social and behavioral factors of human development, including cognition, motivation, social interaction, and communication. Psychological science can also provide key insights on effective instruction, classroom environments that promote learning, and appropriate use of assessment, including data, tests, and measurement, as well as research methods that inform practice. We present here the most important principles from psychology—the “Top 20”—that would be of greatest use in the context of preK–12 classroom teaching and learning, as well as the implications of each as applied to classroom practice.
Research Paper Title: When More Pain is preferred to Less: Adding a Better End
Author(s): Daniel Kahneman, Barbara L. Fredrickson, Charles A. Schreiber, and Donald A. Redelmeier
Often I would end my lessons with a tricky question - maybe a past exam question. This would be the most difficult question I had asked all lesson, and its purpose would for both myself and my students to see how far they had come. I would usually build it up: "okay, this is as tough as it gets. Can you do it?". And often the result was that some could and some couldn't, and I was okay with that as it was meant as an extension question. But having read this paper, I now look at it from the perspectives of the students who could not do the question. What is their impression of that lesson? What will they remember? Will it be the 45 minutes of success they enjoyed at the start, or the 5 minutes of "failure" at the end. Cognitive psychology suggests the latter. This fascinating paper looks at how people's memory of an experience is often dominated by the feelings of pain and discomfort during the final moments, as opposed to what happened during the rest of the experience. Applying this to my students, when thinking about their maths lesson, many of them would have judged it as a failure because of that final, tricky problem. This negative emotion - potentially swirling around their heads the couple of days until their next lesson - could lead to a subsequent lack of confidence and a lack of engagement in mathematics, neither of which are conducive to learning. Hence, I now always end my lesson with a question that is of mid-range difficulty, or maybe even easier. More often than not, it will be a diagnostic multiple choice question (see the Assessment for Learning section). The majority of the learning has happened in the first 45 minutes - my objectives in those last 5 minutes are for me to identify any key misconceptions that will inform my future planning, and for my students to feel good about themselves ready for their next maths lesson.
My favourite quote:
The results add to other evidence suggesting that duration plays a small role in retrospective evaluations of aversive experiences; such evaluations are often dominated by the discomfort at the worst and at the final moments of episodes.
Research Paper Title: Organizing Instruction and Study to Improve Student Learning
Author(s): Harold Pashler et al
Much like The Science of Learning above, this is an outstanding overview of key research findings from cognitive psychology that have direct implications for the classroom. A wonderful feature of this paper is that it also addresses potential roadblocks to implementing the finding in the classroom and suggests possible strategies to overcome them. The recommendations, each of which are covered in a comprehensive and yet easy to follow way, are: Space learning over time, Interleave worked example solutions and problem-solving exercises, Combine graphics with verbal descriptions, Connect and integrate abstract and concrete representations of concepts, Use quizzing to promote learning, Help students allocate study time efficiently, Help students build explanations by asking and answering deep questions. These recommendations have implications for planning, teaching, and helping students formulate effective revision strategies.
My favourite quote:
We recommend a set of actions that teachers can take that reflect the process of teaching and learning, and that recognizes the ways in which instruction must respond to the state of the learner. It also reflects our central organizing principle that learning depends upon memory, and that memory of skills and concepts can be strengthened by relatively concrete—and in some cases quite nonobvious - strategies
Research Paper Title: Do Visual, Auditory, and Kinesthetic Learners Need Visual, Auditory, and Kinesthetic Instruction?
Author(s): Daniel T. Willingham
When I first started teaching 12 years ago, Visual, Auditory and Kinesthetic learning (or the VAK model) was all the rage. My students had to fill out surveys which indicated what type of learner they were, and I had to adapt my lessons accordingly. Needless to say, it was chaos (how exactly do you make solving quadratic equations appropriate for a kinesthetic learner? Maybe do a dance about them?). This wonderful paper presents a clear summary of the evidence on learning types, along with some key implications:
1) Some memories are stored as visual and auditory representations—but most memories are stored in terms of meaning. How you first learn something may be in a visual or auditory way, but how you remember it is tied to what it actually means. When I think about how to add two fractions together (as I regularly do), that information is neither stored in an visual or auditory way. I just know it.
2) The different visual, auditory, and meaning-based representations in our minds cannot serve as substitutes for one another. This is crucial. Our minds have these different types of representations for a reason: Different representations are more or less effective for storing different types of information. The particular shade of green of a frozen pea would be stored visually because the information is inherently visual, whereas the sound of my wife's voice shouting at me is stored in auditory form. These cannot be swapped around.
3) Children probably do differ in how good their visual and auditory memories are, but in most situations, it makes little difference in the classroom. This is the big one. It's likely that some students should have a relatively better visual memory or auditory memory, but that doesn't mean we should always teach to it. The key is that teachers should focus on the content's best modality—not the student's. I teach geometry topics in a visual way, regardless of the preferred learning styles of my students, because even a so-called auditory learner will understand it better that way.
My big takeaway is that it is far more important to carefully consider the best form of presentation for a concept than to worry about catering for each and every child's differing preferences.
My favourite quote:
Experiences in different modalities simply for the sake of including different modalities should not be the goal. Material should be presented auditorily or visually because the information that the teacher wants students to understand is best conveyed in that modality. There is no benefit to students in teachers' attempting to find auditory presentations of the Mayan pyramids for the students who have good auditory memory. Everyone should see the picture. The important idea from this column is that modality matters in the same way for all students.
Research Paper Title: Learning Styles: Concepts and Evidence
Author(s): Harold Pashler, Mark McDaniel, Doug Rohrer, and Robert Bjork
For me, this is the final nail n the coffin for learning styles. A comprehensive review of the evidence by respected researchers, which reaches a clear conclusion. I can do no better than directly quote the authors themselves:
My favourite quote:
Our review of the learning-styles literature led us to define a particular type of evidence that we see as a minimum precondition for validating the use of a learning-style assessment in an instructional setting. As described earlier, we have been unable to find any evidence that clearly meets this standard…
…The contrast between the enormous popularity of the learning-styles approach within education and the lack of credible evidence for its utility is, in our opinion, striking and disturbing. If classification of students’ learning styles has practical utility, it remains to be demonstrated
Research Paper Title: Improving Education: A Triumph of Hope ever Experience
Author(s): Robert Coe
This is the transcript from a lecture that Rob Coe made in Durham in 2013 and it caused a big outcry on Twitter. The theme of the paper is that much of the evidence that attainment in schools has risen over the last 30 years is questionable, and the entire paper is worth a read. However, the part I want to focus on here are the "Poor Proxies for Learning" as identified by the author. They are:
1. Students are busy: lots of work is done (especially written work)
2. Students are engaged, interested, motivated
3. Students are getting attention: feedback, explanations
4. Classroom is ordered, calm, under control
5. Curriculum has been ‘covered’ (ie presented to students in some form)
6. (At least some) students have supplied correct answers (whether or not they really understood them or could reproduce them independently)
It is worth reminding ourselves what this means. Coe is not saying that any of these things prohibit learning taking place, nor is he dismissing the possibility that they can occur alongside learning. The point Coe is making is that because learning is invisible and hence we can only observe proxies of learning, and the items in this list are not particularly good proxies. If we observe any of them alone, without further evidence, then we should be extremely careful in concluding that learning is taking place. Perhaps the most controversial one of these is engagement. For many years that was the number one thing I strived for in my own lessons, and also one of the main things I looked for when observing others. But when you think about it, it makes sense. Just because a student (or a group of students) is engaged, it does not necessarily mean they are learning. We will see in the Encoding section that students remember what they think about, so if students are engaged in the wrong thing (such as the colour they will choose for the next bit of the revision poster they are working on), then they are unlikely to be learning. Likewise, we have all witnessed the quiet, passive student who does not take part in class discussion, appears away with the fairies, and yet performs really well on homeworks and assessments. They do not appear "engaged", but they are learning. So, what are we to take from this? Well, for me it is all about not assuming learning is taking place by relying on easily observable things like those listed above. Instead, we need evidence. And where should we get this evidence? The most obvious way is from test performance, hence my obsession with low-stakes quizzes that I will discuss in the Testing section.
My favourite quote:
If it is true that teaching is sometimes not focussed on learning, how can we make them better aligned? One answer is that it may help to clarify exactly what we think learning is and how it happens, so that we can move beyond the proxies. I have come up with a simple formulation: Learning happens when people have to think hard. Obviously, this is over-simplistic, vague and not original. But if it helps teachers to ask questions like, ‘Where in this lesson will students have to think hard?’ it may be useful.
Research Paper Title: Are Sleepy Students Learning?
Author(s): Daniel T. Willingham
Some of my Year 11 lads yawn more than they speak, so I was fascinated by this paper, and its relation to the one above. I was fascinated to find out that children undergo a biological change (a change in chronotype) in their teenage years that gives them a preference to staying up late versus getting up early. This effect is compounded if teenagers use electronic devices (phones, tablets, consoles watching tv etc) late into the evening as exposure to the back-lit screens makes them wakeful. And what effect does this lack of sleep have? In sum, sleep deprivation influences many (but not all) aspects of children's mood, cognition, and behaviour. Lack of sleep is associated with poorer school performance as rated by students themselves and by teachers. Restricted sleep is also associated with lower grades in studies in the United States, a finding replicated in Norway and Korea. So, what can we teachers do to help? Unfortunately, not a great deal. The two things that would likely make the most difference are out of our control - the time students go to bed, and the time lessons start in the morning. In terms of the latter, studies suggest the later the better, and interesting implications for school administrators are discussed. That finding is interesting, because I always have a preference for teaching morning lessons versus those in the afternoon - trying to get anything out of some classes during the last period of the day can be torture! But it seems likely that Period 1 might be difficult as well! My takeaway is to make the most of the lessons later in the morning, and crucially to pass these findings onto students and their parents. In terms of the work they do outside of the lesson, if your students can combine a good night's sleep with the strategies advocated in the Revision session, then... well, maybe you might even end up with a good night's sleep yourself :-)
My favourite quote:
Inadequate sleep represents a challenge to educators that is in one sense overt—teachers see students drowsy in class every day—and in another sense subtle, because it seems like a common nuisance rather than a real threat to education. And indeed, the problem should not be overstated, at least insofar as it affects education. The impact of typical levels of inadequate sleep on student learning is quite real, but it is not devastating. All the same, its impact lasts for years, and there is every reason to think that it is cumulative.