New paper challenging Cognitive Load Theory. I've been hoping to read a good criticism of CLT for some time but unfortunately this is not it. THREAD ⬇️🧵 tandfonline.com/doi/full/10.10… The paper basically argues that CLT is an outdated framework, rooted in 1980s cognitive psychology, and needs to be replaced by a richer, more holistic view of the brain and learning. Fair enough, let's see what they have to say... (Although I don't think the argument that just because something is old, it is 'outdated'. Indeed, the authors offer Darwin's theory of evolution as analogous to challenges to existing orthodoxies.)

Difficulties are not always 'desirable'. New review gives new insights into how to apply this idea with retrieval practice and how to avoid lethal mutations. 🧵⬇️ Essentially this paper advocates for a subtle but important distinction: instead of designing tasks based on the content or a static judgement of the learner, we should design tasks of dynamic difficulty based on the learner's relative expertise and the complexity of the material.

Why does the brain matter for education? New edition of BJEP has four papers which are very interesting. Made some notes, here's a quick 🧵⬇️ “The particular way that the human cognitive system works and the way that humans learn is due to the way their brains work. The way their brains work is due to biology. And our biology works the way it does because of evolution." Ok fair enough, nice initial rebuttal to the 'brain-as-computer' fallacy...

Really interesting new study on worked examples which underlines just how easy it is to get things wrong and end up in lethal mutation territory 🧵 When used effectively, worked examples are a very efficient methods for scaffolding learning in the early stages of skill acquisition.They're typically used in teaching problem-solving skills in fields like mathematics, science, and programming.

The new Ofsted proposals being discussed at the moment are concerning, particularly the separation of curriculum and instruction. Some thoughts 🧵 The particular problems that any inspectorate faces in shifting away from single word judgements are pretty well established in the literature going back 50 years but it seems little has been learned. It's clear that there needs to be a system of accountability but we are in danger of going back 15-20 years, when classroom observations were essentially tarot card reading with key judgements being made based on the flimsiest of evidence. Here are some key issues to consider:

Context Blindness:
Firstly, classroom observation scores are strongly influenced by the types of students teachers work with, with those teaching higher-achieving students often receiving better ratings. This is especially true for subject specialists compared to generalists. Teachers are often assigned to classes in ways that favour this pattern, making observation scores less fair. Scores tend to reflect how well a teacher manages the classroom or creates a positive environment, but they are less impacted by teaching strategies which lead to long-term gains. These observation scores also vary a lot from year to year because they depend so much on the class dynamics.

Rater expertise:
Evaluators often lack the necessary subject-matter knowledge to make informed judgments about discipline-specific instructional practices. Even within a discipline there is often a lack of knowledge about what is being taught. An ex-Maths teacher doesn't always have the content knowledge to truly evaluate every Maths lesson. This gap in expertise can lead to superficial evaluations that miss the often unseen and covert aspects of effective instruction.

Generic observation instruments:
The problem of generic observation frameworks which are designed to be broadly applicable across subjects, actually fail to capture the unique pedagogical practices required for different disciplines. Also, decoupling curriculum from instruction is a major step backward. A very good example of this from Christine Counsell is senior leaders using verbs like “describe,” “explain,” and “evaluate” as some kind of indicator of effective learning processes, but which cause a disconnect with the disciplinary focus of causal explanations in history for example. This disconnect reflects a broader clash in education between subject-specific curricula and generic aims focused on perceived utility.

Perverse incentives:
Lessons that were considered 'outstanding' 10-15 years ago were often all-singing/all-dancing Cirque De Soleil style lessons with students running around the room and writing on posters on the wall. These lessons were rolled out for inspections, never to be seen again. We now know that engagement doesn't always mean learning and that being cognitively active doesn't have to mean being physically active. The idea that learning is an observable phenomenon has some evidence such as precision teaching and the work of Ogden Lindsley but this is a very specific methodology which is rare in most classrooms. In my experience, most of the time when observers (inspectors/ leadership) have cited evidence for learning, they have cited performance not learning. journals.sagepub.com/doi/full/10.31…

One thing we hear over and over again is that we should be teaching creativity in schools. We also often assume that certain people are just more creative than others. What is the evidence for this? A new paper examined fifty years of research. Thread ⬇️ 🧵

This paper asks a basic question: are some people simply born more creative, regardless of the subject or field and if so, what does this mean for education? this idea, known as the "domain-general hypothesis", suggests that a person with a high level of general creativity could excel in any creative task they pursue.
The article questions whether we should be testing for this general creative ability, if we should teach it and whether "general creativity" is even a thing. To test this, researchers studied how participants complete creative tasks in various domains like writing, visual arts, or problem-solving.
The general creative type or "domain-general hypothesis" would be supported if participants consistently demonstrated above-average creativity across all these domains. However, this review found several limitations:

Possibly the most difficult challenge teachers face in instructional design is the “transfer paradox” otherwise known as the deceptive trade-off between immediate performance vs. long-term transfer. A short 🧵⬇️ The “transfer paradox” refers to a counterintuitive situation in learning and instructional design: techniques that improve immediate performance often do not lead to effective transfer of skills or knowledge to new and different contexts. In other words, what helps students perform well during initial learning may not prepare them well for applying that knowledge in different situations or problems they haven’t encountered before.
ref. researchgate.net/publication/25…

Seeing a lot of schools mandating retrieval practice in every lesson but also seeing quite a few misconceptions. A quick thread: 10 ways to get retrieval practice wrong ⬇️ 🧵 1. Not providing enough challenge, especially initially: Giving quizzes, where the first retrieval is very soon after learning, can create the "illusion of competence" where students recall easily on that first attempt, but later performance suffers. The initial retrieval needs to be sufficiently challenging to be effective.

Easy retrieval often involves retrieving information based on superficial cues or associations, rather than engaging in deeper, more elaborative processing. This type of shallow processing can lead to memories that are fragile and easily forgotten.

When retrieval is effortless, the brain doesn't need to work as hard to retrieve the information. Evidence suggests that this lack of effortful retrieval can result in weaker encoding of the memory trace, making it less durable over time.

How effective are open-plan classrooms or '21st Century learning spaces'? Is a noisy classroom a 'thinking classroom'? A short thread on why they're a really bad idea 🧵 The idea of open plan classrooms became popular in the 1960s and emerged from a broader concept of 'open education' which included a set of assumptions derived from constructivist thought:

How might teachers and school leaders think about implementing the science of learning in practice?
Some thoughts from my talk at #rEDTO2024 Firstly it's important to say that we still have a large gap between evidence from experimental settings and classroom practice. @TWPerry1's review is a hugely important work and really sets out the limitations of the evidence we actually have.
Where we do have evidence of the science of learning in schools, the interventions are often not done by real teachers in real situations. This is a problem.
#rEDTO2024

What can we learn from experts on expertise? Some notes from this excellent book 🧵⬇️ 1. Experts Excel Mainly in Their Own Domains.

"There is little evidence that a person highly skilled in one domain can transfer the skill to another."

This point is possibly the central one in the book and one which most people struggle with. Being an excellent teacher in one subject doesn't mean you can teach any subject. In fact even within one subject area there is not a lot of transfer: an expert secondary school English teacher would be useless at teaching 5 year olds how to read.

Likewise, teaching students a set of generic skills is unlikely to lead to them becoming proficient in other areas. You can think deeply about something you know a lot about - generalised 'thinking skills' doesn't come into it.

"The obvious reason for the excellence of experts is that they have a good deal of domain knowledge. This is easily demonstrated; for example, in medical diagnosis, expert physicians have more differentiations of common diseases into disease variants (Johnson et al., 1981). Likewise, in examining taxi drivers’ knowledge of routes, Chase (1983) found that expert drivers can generate a far greater number of secondary routes (i.e., lesser known streets) than novice drivers."

Does cognitive science claim the brain is like a computer? A short thread ⬇️ 🧵

It's true that phrases such as 'central executive', 'process', 'encoding' and 'retrieval' are used in the field to define how we learn, however cognitive science also acknowledges the fact that we are human and that unlike computers, we have all the attendant biases and cognitive limitations that come with being human. Blake Richards argues that if we take the definition of computer from computer science then it's not a good metaphor because the differences are too vast to warrant comparison:

One of the key things to know from over 100 years of research on learning is the weird paradox that an important part of learning anything is actually forgetting it. A thread on how to harness this principle for effective learning ⬇️ 🧵 In 1914, Edward Thorndike outlined his law of effect (based on experiments with cats) which became the blueprint for the behaviourist idea that positive experiences are reinforced and negative ones are weakened.

But part of this broader theory was his 'theory of disuse' which establishes another fairly simple idea: the less you use something the more you forget it. The key idea is that unless you rehearse learned information and skills, it will fade or 'decay' as he put it, over time (use it or lose it).

Now on the surface this seems like something everybody knows, but like so much about learning, it's really not that simple at all.

One of the most important things educators need to know about learning is that human memory is not like a tape recorder or a computer. It has what Bjork and Bjork call “important peculiarities”, four of which are really important to know when planning teaching ⬇️🧵 (1) Memory has a "remarkable capacity for storing information coupled with a highly fallible retrieval process" (Bjork & Bjork, 2006)

In other words, while we can store a wealth of information, the mechanisms for recalling that information are imperfect and prone to error.

We often think we know something when we don't really know it at all.

This is the main reason why cognitive science has focused so much on the spacing effect and retrieval practice as they are powerful tools for helping students to remember stuff for the exam in June that they were taught in September.

Direct or explicit instruction seems to be widely misunderstood. It's often characterised as boring lectures with little interaction and not catering to the needs of all students. Nothing could be further from the truth. A short thread 🧵⬇️ Direct Instruction (DI) as a formal method was designed by Siegfried Engelmann and Wesley Becker in the 1960s for teaching core academic skills. This was a structured, systematic approach which emphasizes carefully sequenced materials delivered in a clear, unambiguous language with examples.

It's designed to leave little room for misinterpretation and to ensure that all students, regardless of background or ability, can learn effectively.

It's also anything but boring. Here is a video from the 1960s of Englemann teaching Maths. Notice how interactive and fast paced the teaching is:

A real shame to see some opposition to cognitive science in education as I believe it offers some incredibly powerful insights into learning and how to create the best conditions under which students can flourish. Here are some core misconceptions which I will try to address⬇️🧵 “Cognitive science is not science”
It’s not a pure science in the tradition of the natural sciences obviously but it’s very much an applied science. As Mayer points out, what makes it science is firstly that it’s based on “evidence rather than opinions, slogans, or quotations from experts”

A thread summarising my talk at #rED23 yesterday on the challenges of applying the science of learning in the classroom 🧵 As far back as the 1890s William James cautioned against thinking you can apply the principles of psychology straight into the classroom. However, without an understanding of how the brain learns, planning instruction is suboptimal. I think these two positions encapsulate the interstitial point in which we find ourselves.

How good is ChatGPT at writing English essays? As an English teacher of 15 years I've been playing about with it and I think we need to pay attention to this. ⬇️ 🧵 Two conclusions at this stage:
1. It's not at the stage yet where it can produce sophisticated A-level/undergraduate essays.
2. It has absolutely nailed how to write GCSE English essays.

THREAD: there is a growing body of evidence to show that mobile phone use is linked with lower academic achievement, a decrease in wellbeing and can often facilitate bullying in schools. Here's some of the available evidence. Firstly the amount of time children are spending in front of screens in general is alarming. To take one example, 20% of 14- to 17-year-olds in the US spend 7 hours a day in front of screens. (and that was 5 years ago) census.gov/programs-surve…