Visible Learning Meta-Study


Visible teaching and learning occurs when learning is the explicit goal, when it is appropriately challenging, when the teacher and student both seek to ascertain whether and to what degree the challenging goal is attained, when there is deliberate practice aimed at attaining mastery of the goal, when there is feedback given and sought, and when there are active, passionate and engaging people participating in the act of learning” (p. 22).

Hattie also convincingly argues that the effectiveness of teaching increases when teachers act as activator instead of as facilitator. He developed a way of ranking various influences in different meta-analyses related to learning and achievement according to their effect sizes. In his ground-breaking study “Visible Learning” he ranked 138 influences that are related to learning outcomes from very positive effects to very negative effects. Hattie found that the average effect size of all the interventions he studied was 0.40. Therefore he decided to judge the success of influences relative to this ‘hinge point’, in order to find an answer to the question “What works best in education?”




Ivo Arnold, 2011. Book Review: John Hattie: Visible learning: A synthesis of over 800 meta-analyses relating to achievement, Int Rev Educ (2011) 57:219–221, DOI 10.1007/s11159-011-9198-8, Routledge, Abingdon, 2008, 392 pp, ISBN 978-0-415-47618-8 (pbk)

Hattie Ranking: 195 Influences And Effect Sizes Related To Student Achievement available here

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John Bigg’s Constructive Alignment


Constructive alignment is an example of outcomes-based education (OBE).

In constructive alignment, we start with the outcomes we intend students to learn, and align teaching and assessment to those outcomes (…) learning is constructed by what activities the students carry out; learning is about what they do, not about what we teachers do.

  • SOLO 1: pre-structural level_the student has no understanding, uses irrelevant info, misses the point all together
  • SOLO 2: uni-structural level_the student understands one relevant aspect only, the student is able to identity, to do a procedure, and/or to recite
  • SOLO 3: multi-structural level_the student can focus on several relevant aspects, is able to classify, combine, enumerate
  • SOLO 4: relational level_the student can link and integrate several parts into a coherent whole, details are linked to conclusion, and the meaning is understood, the student has the ability to relate, to compare, to analyze
  • SOLO 5: extended abstract_ the student has the capacity to generalize this structure beyond the information given and even produce new hypotheses or theories which may then be scrutinized

Defining learning goals as clearly and concisely as possible is an essential step in setting down the outcome of a teaching session. To encourage students to apply “deep learning” techniques for competency acquisition, examinations in particular, not just course content, must be designed in accordance with constructive alignment standards; this is the only way to ensure that students will acquire the requisite target competencies. Thus, courses and course units should be designed as follows: (1) define learning outcomes, (2) decide on examination formats, and (3) bring the structure and sequence of course content into alignment with the examination tasks. This process is not strictly linear, however: If experience shows that certain competencies cannot be tested or evaluated, for example, your learning goals may have to be readjusted accordingly.



John Biggs, Constructive Alignment, available here

John Biggs, Teaching Teaching & Understanding Understanding (2/3), duration from min 3:16 to min 5:28, available here

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It is the name for the computer modelling approach to information processing based on the design or architecture of the brain. Connectionist computer models are based on how computation occurs in neural networks where neutrons represent the basic information processing structures in the brain.

All connectionist models consist of four parts:

  • units: they are what neutrons are to the biological neural network, the basic information processing structures. Most connectionist models are computer simulations run on digital computers. Units in such models are virtual objects and are usually represented by circles. A unit receives input, it computes an output signal and then it sends the output to other units. This is called activation value. The purpose of the unit is to compute an output activation.
  • connections: connectionist models are organised in layers of units, usually three (3). A network however, is not simply an interconnected group of objects but an interconnected group of objects that exchange information with one another. Network connections are conduits. The conduits through which information flows from one member of the network to the next are called synapses or connections and are represented with lines. (in biology synapses are the gaps between neutrons, the fluid-filled space through which chemical messengers -neurotransmitters- leave one neutron and enter another)
  • activations: activation value in connectionist models are analogous to a neuron’s firing rate or how actively it is sending signals to other neurons. There is a big variability between the least active and the most active neutrons expressed in a scale fro 0 to 1
  • connection weights: The input activations to a unit are not the only values it needs to know before it can compute its output activation. It also needs to know how strongly or weakly an input activation should affect its behaviour. The strength or weakness of a connection is measured by a connection weight. They range between -1 to 1. Inhibitory connection reduce a neuron’s level of activity; excitatory connections increase it.

Yet, the behaviour of a unit is never determined by an input signal sent via a single connection, however strong or weak that connection might be. It depends on its combined input. That is the sum of each input activation multiplied by its connection weight. The output activation of a unit represents how active it is, not the strength of its signal.

Connectionist networks consist of units and connections between units and have some very interesting features like emergence of behaviour. This does not reduce to any particular unit (liquidity in water). Graceful Degradation and Pattern Completion are two ways in which activations are spread through a network. They are not classical computers, their behaviour does not arise from an algorithm, they learn to behave the way they do.



Robert Stufflebeam, 2006. Connectionism: An Introduction (pages 1-3), in CCSI (Consortium on Cognitive Science Instruction) supported by the Mind Project, full article available here

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Self-efficacy and Cognitive Load & Prior Knowledge by Keith Brennan


Both terms are connected to the meaning of self-efficacy in Albert Bandura’s work. Self-efficacy is our belief that a task is achievable by us. High self-efficacy students work harder and are less likely discouraged. Low self-efficacy work less and for shorter periods of time.

  • Cognitive load: the amount of information we can take in, process and retain. It’s a critical mechanism to explain why novice learners may have difficulty in unstructured environments.
  • Prior Knowledge: the idea that what we already know has a powerful determining effect on what we can learn, and how quickly.

Educators encourage or undermine SE in four ways:

  • physical and psychological responses: educators need reassuring students, especially novices
  • encouragement and verbal persuasion: educators need to scaffold the learning experience for students
  • vicarious experience: our capability increases when we see people we consider similar to ourselves achieve a task.
  • mastery experiences: these experiences are characterised by corrective feedback, achievability, and cognitive load that represents both a challenge, but also leaves enough space for complex learning.

The author advocates for guided instruction because modes of learning such as discovery learning/ problem-based learning/ inquiry learning/ experiential learning/ constructivism &/ connectivism despite their popularity, do not support novices enough. The focus is on novices as they are the ones who might be discouraged and withdraw in case their learning experiences requires more than they can give.

Long-term memory is the central dominant structure of human cognition. Everything we see, hear and think about is critically dependent on and influenced by our long-term memory (…) we are skillful in an area because our long-term memory contains huge amounts of information concerning the area (…) the aim of the instruction is alter long-term memory (…) any instruction recommendation that does not or cannot specify what has been changed in long-term memory, or that does not increase the efficiency with which relevant information is stored in or retrieved from long-term memory, is likely to be ineffective. (Kirschner, Sweller, Clark)



Brennan, K., 2013. In Connectivism, no one can hear you scream: a guide to understanding the mood novice, in Digital Pedagogy Lab (24th July 2013), full article available here

Kirschner, P.A., Sweller, J., Clark R.E., 2006. Why minimal guidance during instruction does not work: an analysis of the failure of constructivist, discovery, problem-based, experiential and inquiry-based teaching, in Educational Psychologist, 4l(2), pp.75-86, Lawrence Erlbaum Associates, Inc, full paper available here

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Learning Rules

hebbs rule

Hebb’s rule: is a neuroscience theory where an increase in synaptic efficacy arises from the presynaptic cell’s repeated and persistent stimulation of the postsynaptic cel (…) Hebbian theory concerns how neurons might connect themselves to become engrams (=means by which memories are stored thus biophysical/biochemical changes in the brain in response to external stimuli) (…) The theory attempts to explain associative or Hebbian learning, in which simultaneous activation of cells leads to pronounced increases in synaptic strength between those cells, and provides a biological basis for errorless learning methods for education and memory rehabilitation. In the study of neural networks in cognitive function, it is often regarded as the neuronal basis of unsupervised learning.


Back-propagationis a method used in artificial neural networks to calculate the error contribution of each neuron after a batch of data (in image recognition, multiple images) is processed  [=computing systems inspired by the biological neural networks that constitute animal brains, these systems learn to do tasks by considering examples](…) Backpropagation is sometimes referred to as deep learninga term used to describe neural networks with more than one hidden layer (layers not dedicated to input or output)


Boltzmann machine: is a type of stochastic recurrent neural network [a stochastic or random process is a mathematical object usually defined as a collection of random variables] (…) They were one of the first neural networks capable of learning internal representations, and are able to represent and (given sufficient time) solve difficult combinatoric problems (…) Boltzmann machines with unconstrained connectivity have not proven useful for practical problems in machine learning or inference, but if the connectivity is properly constrained, the learning can be made efficient enough to be useful for practical problems


References & Images


The TIMN Model


The TIMN Model describes different ways of teaching:

  • T for Tribe_the campfire
  • I for Institutions_the lecture
  • M for Markets_the debate
  • N for Networks_the conversation

Societies, within this framework, began with tribal structures (99.9% of our history as human beings was spent in tribal structures) and expanded into institutional structures (nation-state bureaucracies) and finally into markets. Previous forms weren’t abandoned, instead, they were enhanced by the addition of a new form.  David Ronfeldt, the global guerrilla guru who came up with the acronym, argues that the tri-form structure of tribes, institutions and markets are now nearly universal.




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Gagne’s 9 events of instruction


  • Gain the attention of the students: Stimulate students with novelty, uncertainty and surprise/ Pose thought-provoking questions to the students/ Have students pose questions to be answered by other students
  • Inform students of the objectives: Describe required performance/ Describe criteria for standard performance/ Learner establishes criteria for standard performance
  • Stimulate recall of prior learning: Ask questions about previous experiences/ Ask students about their understanding of previous concepts
  • Present the content: Present vocabulary/ Provide examples/ Present multiple versions of the same content, e.g., video, demonstration, lecture, podcast, group work/ Use a variety of media to address different learning preferences
  • Provide learning guidanceProvide instructional support as needed – as scaffolds (cues, hints, prompts) which can be removed after the student learns the task or content/ Model varied learning strategies – mnemonics, concept mapping, role playing, visualising/ Use examples and non-examples – in addition to providing examples, use non-examples to help students see what not to do or the opposite of examples/ Provide case studies, analogies, visual images and metaphors – case studies for real world application, analogies for knowledge construction, visual images to make visual associations, metaphors to support learning
  • Elicit performance: Elicit student activities – ask deep-learning questions, make reference to what students already know or have students collaborate with their peers/ Elicit recall strategies – ask students to recite, revisit, or reiterate information they have learned/ Facilitate student elaborations – ask students to elaborate or explain details and provide more complexity to their responses/ Help students integrate new knowledge – provide content in a context-rich way (use real-world examples)
  • Provide feedback:
    • Confirmatory feedback – Informs the student they did what he or she were supposed to do
    • Corrective and remedial feedback – informs the student the accuracy of their performance or response
    • Remedial feedback – Directs students in the right direction to find the correct answer but does not provide the correct answer
    • Informative feedback – Provides information (new, different, additions, suggestions) to a student and confirms that you have been actively listening – this information allows sharing between two people
    • Analytical feedback – Provides the student with suggestions, recommendations, and information for them to correct their performance
  • Assess performance: Pretest for mastery of prerequisites/ Use a pretest for endpoint knowledge or skills/ Conduct a post-test to check for mastery of content or skills/ Embed questions throughout instruction through oral questioning and/or quizzes/ Include objective or criterion-referenced performances which measure how well a student has learned a topic/ Identify normative-referenced performances which compares one student to another student
  • Enhance retention and transfer to the jobParaphrase content/Use metaphors/ Generating examples/ Create concept maps or outlines/ Create job-aids, references, templates, or wizards



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