Zone of Proximal Development


The zone of proximal development, often abbreviated as ZPD, is the difference between what a learner can do without help and what he or she can do with help. It is a concept introduced, yet not fully developed, by Soviet psychologist Lev Vygotsky (1896–1934) during the last ten years of his life (…) Vygotsky stated that we can’t just look at what students are capable of doing on their own; we have to look at what they are capable of doing in a social setting. In many cases students are able to complete a task within a group before they are able to complete it on their own.

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ADAPT-r is an ITN network that aims to develop new knowledge and understanding of Creative Practice Research (CPR) thus design thinking, public behavior, as well as the emergence of new methods oriented towards the explication of tacit knowledge. It comprises of 33 early stage researchers all creative practitioners and PhD candidates, 7 experienced researchers and 7 institutional partners. Research was conducted in the form of 9 paired interviews.

WORK PACKAGE 01_Primary Research: it follows the logic of the referential focuses of creative practice research training;

  • case studies: these are the venturous practices of the creative practitioners
  • community of practice: the communities that contextualize these case studies
  • transformative triggers: what shifts and transforms their creative practice and how it is related to social contexts; triggers uncover the challenges and the challengers of creativity the practitioners are not aware of; the revisiting, sorting and mapping past work triggers changing understandings; they are the markers of knowledge creation and recognition of development and change in the creative research practice; when things fall into place; Embracing Uncertainty: The space of not knowing; Other ways of knowing: intuition, hunch, feeling and bodily knowledge; they are not immediate insights but rather a means of opening up
  • public behaviors: it means that the practitioner positions himself/herself in his/her communities of practice/relevance; they point to navigating contexts; it is an interaction ritual
  • explicating tacit knowledge,
  • explication of methods

Methodology Analysis: Wording/Metaphoring/Anecdoting/ Diagramming*/ Choosing/ Playing/ Manifesting/ Structuring

Interesting findings on knowledge creation and creativity. 

(…) by thinking about knowledge as socially constructed, something that operates in networks, in relationships between actors, it becomes clear that there is no singular thing that amounts to knowing, instead, there are multiple knowledges. Knowledge represents multiple considerations about creativity. creativity can be a new idea, imagination and/or innovation; it too is multiple. As such it can be thought of as a responsive and relational, not classic and timeless.

There are three types of knowledge. There is input knowledge: the knowing before action. There is output knowledge: the knowing after action. There is relational knowledge: the knowing in action (communities of practice) developed relationally through interaction and collaboration

In order for innovation to be innovative it must be recognized as such by the creative practice researcher’s community of practice (…) the outputs of creative practice go beyond any objects of practice(…) doing creative practice is not the same as doing creative practice research; the practice needs to be framed differently



J. Verbeke, K. Heron, T. Zupancic, Relational Knowledge and Creative Practice, 2017, A publication by ADAPT-r (eds Tadeja Zupancic, Claus Peder Pedersen), ISBN 9789082510850, available here

ADAPT-r official webpage

*Diagrams as a research tool, Annotated, Different Aesthetics, Handmade, Collage, Landscape-like, as tools to discover or represent, as texts, to measure and visualize the projects, spider diagrams, time diagrams, architectonic diagrams, research space diagrams

From design to cybernetics


Scientific Research is a restricted form of design. Design is thus not necessarily scientific.

Design: is central to the act of design is circularity (…) it is a conversation often involving a paper and a pencil with an other; ourselves or someone else (…) a distinguished element of design is novelty (…) scientific research is a design activity (..) we design our experiences and objects by finding commonalities (simplification) we design how we assemble them into patterns (…) looking at these patterns we make further patterns, thus in doing science, we learn (…) design is the object of study and as a means we carry out this study (…) scientific  research should be judged by design criteria, not the other way around (…) rigorous, honesty, clarification, testing and the relative strength of argument over assertion are essential qualities of design

The role of the observer as participant: making knowledge, abstracting it to theory, theorising about theory, constructing the way we obtain this knowledge, all is done by the actor (…) at every step it is the actor designing (…) the designer is central to science

The nature of these circular systems are examined in cybernetics. According to Norbert Wiener,  cybernetics is the scientific study of control and communication in the animal and the machine, whereas currently it is used as in ‘control of any system using technology’. In Glanville’s terms:

cybernetics has elucidated conversation, creativity and the invention of the new; multiple points and their implications for their objects of attention; self-generation and ‘the emergence’ of stability; post-rationalization; representation and experience; constructivism; and distinction drawing and the theory of boundaries



Ranulph Glanville, Researching Design and Designing Research. In Design Issues Vol. 15, No. 2, Design Research (Summer, 1999), pp. 80-91, available here

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The circular relationship of experiment and theory


  • Theory from experiment: it involves pattern finding (…) it is the making of a concept from many distinct perceptions (…) it formalises the significance and necessity of pattern (…) theories are patterns with widespread credence and accepted as accounting for a part of our experience (…)  we simplify to make our continuum of our experience de-finite (…) if we did not simplify we wouldn’t be able to recognise nor generalise (..) this allows us to make predictions as a means of extending the range of our observations
  • Theory from theory: it is the examination of concepts to clarify these concepts further (…) science depends not only on theory based on collecting and organising of empirical evidence but also on theory based on the consequences of that evidence (…) theory on theory is us acting self-referentially by using the devices of simplification and pattern finding (…) our understandings help us develop our understandings but also restrict them (…) when we find contradictions we modify or reject this understanding; this cyclical process is a design  process (…) the continuous modification and the inclusion of more and more in a coherent whole (…) one form of theory in theory is criticism



Ranulph Glanville, Researching Design and Designing Research. In Design Issues Vol. 15, No. 2, Design Research (Summer, 1999), pp. 80-91

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Bandura’s Social Learning Theory (Part I)


In the social learning system new patterns of behavior can be acquired through direct experience or by observing the behavior of others.

Modelling phenomena are governed by four interrelated sub-processes:

  • attentional processes: a person should recognize the essential features of one’s behavior (…) association preferences play a major role in determining observational experiences (…) within groups some members are to command greater attention that others
  • retension processes: a person is influenced by observation if he/she has a memory of the model he/she is observing (…) long-term retention of activities also play a major role (…) there are two representational systems on OL -an imaginal and a verbal one. During exposure modelling stimuli produce relatively enduring retrievable images of modelled sequences of behavior (…) verbal coding of the visual information accounts for the notable speed of OL and long term retention (…) observers who code modeled activities into words, images etc learn and retain the behavior better than those who simply observe (…) rehearsal serves also as an aid (…) people who mentally rehearse or actually perform modeled patterns are less likely to forget them.
  • motoric reproduction processes: where symbolic representations guide overt actions (…) to achieve behavioral reproduction a learner must put together a given set of responses according t the modeled patterns (…) the amount of OL depends on whether he/she has acquired the component skills (sub skills also exist) (…) performers cannot see the responses they are making (swimming) they depend on onlookers (…) it is exceedingly difficult to guide actions that are not easily observed.
  • reinforcement and motivational processes: actions depend on positive incentives which affect the level of OL by controlling what people attend to.

Provision of models will not automatically create similar patterns of behavior to others.



Albert Bandura, 1971, Social Learning Theory, New York: General Learning Press

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The Minerva Project or The Death of the Lecture


MINERVA’S PROJECT: Founded by Ben Nelson. Minerva majors include are: Social Sciences, Computational Sciences, Natural Sciences, Arts & Humanities, and Business. Each one of these offers six different concentrations. Minerva uses the Active Learning Forum™, a platform that allows students to interact online. Instead of building a campus, Minerva chooses to invest to students co-living in a same physical setting, one that changes each semester across continents, while learning occurs only online.

The courses may occur online but they are not massive, says Graeme Wood, a journalist that reported on Minerva back in 2014 for the Atlantic.  Wood joined a course of inductive logic ran by professor Eric Bonabeau (physicist, Minerva’s dean of computational sciences). All of the courses in Minerva assume the form of online seminars. They do so however, by profiting from the existence of MOOCs. Ben Nelson, the founder, likens MOOCs to publishing and considers them to be in the future sole providers of content in terms of lectures.

Nelson bases the Minerva layout to his belief that ‘when you have a noncurated academic experience, you effectively don’t get educated’. He also insists that ‘the lectures’ model is dead, soon to be completely obliterated’. Kosslyn, a former Harvard dean and a renowned cognitive neuro­scientist who has joined Minerva as a founding dean, also claims that lectures might be ‘cost-effective but they are pedagogically unsound’. Kosslyn in particular, says Wood, in his 32 years at Harvard has realized an extended research on education and cognitive science and that now he has the chance to put this research into motion.

Claire Cain Miller from New York Times claims that Minerva’s faculty concluded that a key skill is being able to apply learning in new and different contexts. Toward that end, students keep blogs during their travels about how they’re using the concepts they learned freshman year. “As we define it”, adds Nelson in Bized Magazine, “fully active learning means that 100 percent of students must be engaged at least 75 percent of the time in every class.”

Wood’s article is the most thorough of all but is inconclusive as to whether this initiative will prove strong enough to alter the current educational practices of the ivy league institutions. It is also unevenly written as strangely enough, the author concludes the article with the ambitious expectations of Nelson instead of his stronger initial remarks in regard to Minerva’s courses seminar-alike setup:

For one thing, it was exhausting: a continuous period of forced engagement, with no relief in the form of time when my attention could flag or I could doodle in a notebook undetected (…)  I was forced, in effect, to learn. If this was the education of the future, it seemed vaguely fascistic. Good, but fascistic.

Three years later, in April 2017, the Minerva project is still on. In fact what started timidly with a small cohort of 33 students, was strengthened in 2015 with a cohort that reached 100 students, while this year, 150 new students have enrolled (Minerva accepted only 1.9 percent out of 16,000 applicants).




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Oh, I even love the word itself, but what are they, really? Well, it is a type of artificial neuron that takes several binary inputs and produces a single binary output.

What one needs to know are the binary inputs and their relative weight in the decision-making process (…) by varying the weights and the threshold, we can get different models of decision-making (…) a perceptron can weigh up different kinds of evidence in order to make decisions (…) another way perceptrons can be used is to compute the elementary logical functions we usually think of as underlying computation, functions such as AND, OR, and NAND.



Michael Nielsen, Neural Networks and Deep Learning, Chapter 01, full book available here



Connectomics uses advanced brain imaging techniques to identify and map the intricate web of white matter (communication lines) that link gray matter (neural brain volume). Mapping such networks occurs at the level of synaptic connections. This research began in the 70’s but has recently gained interest thanks to technical and computational advances that automate the collection of electron-microscopy data and offer the possibility of mapping even large mammalian brains. “Connectome” was coined in analogy with the “genome”—the entirety of an organism’s hereditary information—studied by biologists. To imagine how the story of the connectome will unfold over the next few decades, it’s helpful to recall the history of the genome. Connectomics is more challenging than genomics; the structure of the brain is extraordinarily complex. With an electron microscope, the branches of neurons can be seen clearly, even when they are tightly packed together in the brain.

People with high creative capacity have more connections between their left and their right hemispheres of their cerebral cortex.



  • Highly Creative People Have Well-Connected Brain Hemispheres, full article and image available here
  • The big data challenges of connectomics, available here
  • Connectomics: Tracing the Wires of the Brains, available here
  • video

Cognitive Science and Learning


Excerpts from Clark Quinn’s article on cognitive science entitled “The Cognitive Science Behind Learning

01 .learning is about strengthening the connections between certain neurons. It’s safe to say the neurons that fire together, wire together (…) 02. to make learning persistent, it needs to be spaced, or reactivated and strengthened over a period of time (…) 03. the amount of time over which to practice, and the total quantity needed, depends on the complexity of the task and the amount of time between practice and performance as well as the time between performance opportunities (…) 04. learning and instruction is about designed action and guided reflection (…) 05. at the cognitive level, content ideally is a mental model, a suite of causal and conceptual relationships that provide a basis for explanation of what happened and predictions about what will happen (…) 06. one robust finding around models is that learners will build them, and they’re remarkably hard to extinguish if wrong; instead, they get patched (…) 07. another robust finding is that learning leaders go wrong by bringing in inappropriate models. Most of the mistakes we see are systematic, not random

I object to number 04’s designed action and guided reflection as they insinuate that the person who does this is not the individual learner but someone else p.e. the instructor (?) but I do like the reference to the temporal parameter in learning (nos 02 and  03).

*Cognitive science is the interdisciplinary, scientific study of the mind and its processes. It examines the nature, the tasks, and the functions of cognition. Cognitive scientists study intelligence and behavior, with a focus on how nervous systems represent, process, and transform information. Mental faculties of concern to cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology.

Image and definition available here

Bruce Edmonds and Contextual Cognition in Social Simulation


How does individual behavior influence the social behavior and vice versa? Social Simulation models (remember eigenbehaviors and Gero’s Situatedness) capture this interplay through various aspects of cognition. (eg social norms) Edmonds argues that in order to better understand this process it is best to contextualize the cognitive processes of the interacting agents. Below the author distinguishes among different types of context:

  • Situational Context: time, location, who was there, the knowledge of the people, the history of the place and all the objects present, “those factors that are relevant to understand this particular occurrence”
  • Linguistic Context: the words that surround an utterance or phrase, elements of the relevant culture, very common as social interactions are composed of linguistic communications. [Peter Gardenfors: Action is primary, pragmatics consists of the rules for linguistic actions, semantics is conventionalised pragmatics and syntax adds markers to help disambiguation (when context does not suffice]
  • Cognitive Context and Framing: some knowledge is acquired in a particular situation and then made available in similar situations and this abstraction is what we refer to as cognitive context, coincides with Goffman’s frames seen as schemata of interpretation used by individuals to locate, perceive, identify and label experiences, the “framing” is a cognitive context of opinion and choice
  • Social Context: events, habits, conventions, norms, a situation that has its own characteristics associated with it.

Learning and Reasoning Edmonds claims, “are far more feasible when their scope is restricted to a particular context“. If, however, one wishes to generalize a thesis, then he/she must be able to appropriately change this focus as the external context, that is the context we inhabit in and thus assess the relevance of knowledge via identifiable “contexts“. This is how we deal with complexity: our limited learning is efficient because many of the possible causes or affects of events that are important remain relatively constant.

The figure above, illustrates what Edmond proposes the architecture of a simulation model should be. There are four modules:

  • Context Identification System (CIS): takes the inputs and learns in a flexible and imprecise way an indication of context
  • Context Dependent Memory (CDM): takes the indication of CIS and identifies all the memory items stored in that context, it evaluates the current truth of these and returns negative feedback to the CIS which will then identify another context
  • Local Learning Algorithm (LL): performs a local update of the knowledge in the memory, propagates successful items towards focus, deletes or corrects items that were false, inserts new items
  • Inference System (IS): it tries to deduce some decisions as to the actions or plans to execute, two common problems, under-determination (not enough info) or over-determination (contradicting info), in the first case the context is expanded, in the second case, the context is reduced.

Edmond’s analysis on the general heuristics are: Formation, Abstraction, Specialization, Content Correction, Content Addition, Content Restriction, Content Expansion, Content Removal

Back to Gero, the heuristics were: Formulation, Synthesis, Analysis, Evaluation, Documentation, Reformulation type 1, Reformulation type 2, Reformulation type 3



  • Edmonds, B., 2014. Contextual Cognition in Social Simulation. In Context in Computing, Patrick Brézillon & Avelino J. Gonzalez (Eds.) New York: Springer DOI 10.1007/978-1-4939-1887-4_18, full paper available here
  • Downes, St., Commentary on Contextual Cognition in Social Simulation, available here

Educational Research, Cognitive Science & Neuroscience


Notes from MIT’s Online Education Policy Initiative Report, Pages 6-10

ER: Constructivism: Dewey, experiential learning, Piaget, Vygotsky, Montessori, inquiry and discovery. Active learning, teaching laboratories, Amos Eaton (1824), active instruction, Mazur, peer learning, all-hands-on courses, mini-lectures, simulations experiments. Online counterparts are flipped classroom. Project -based learning, video disks, personal computers and calculators. Papert’s Constructionism, a refinement of constructivism, development of Lego Mindstorms, robot design, prototyping technologies. Problem-based learning, imprecisely defined problems, self-directed learning peer learning, teamwork, internships, work-study programs, blurred boundaries between college and workplace. Student-centered education, reflection, discussion, interdisciplinary thinking, self-paced learning, Bloom, students in small cohorts. Online counterparts are Peer2Peer University where peer is the primary instructor.

CS: level of the brain, Ebbinghaus, how memories form and persistmind wandering, task-unrelated thoughts, make students curious, retrieval practice, engaging repeatedly in recall activities
is called interpolated testing, block of practice right after students have learned a topic, contrast between storage strength and retrieval strength, concept of desirable
difficulties,generation effect, generation of answers can help learning even if they are wrong, and feedback is effective even if it is corrective. Cognitive load theory, “compression” of new information, novices should be given worked examples
rather than open-ended problems. Impact of context, the context of the learning reflect the context in which that information will likely be used.

N: level of the neurons, initial encoding, integration of memories, consolidation, synaptic and system levels, sleep, blocked learning may be associated with saturation at the synapse during a process known as long-term potentiation, cognitive load has been shown to be measurable using pupillary dilation, activation of sensorimotor brain regions would enhance understanding of torque and angular momentum, MRI shows more active training
methods correlated not only with better test performance but also with greater stimulation of the predicted brain regions

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Remembering Henri Bergson

Matter and Memory played a key role for me to the understanding of how human perception and memory, work. Back in 2012 when I was studying museum structures this book gave me an incredible insight to how we see and memorize objects.

(…) we have distinguished three processes, pure memory, memory-image and perception, of which none of them in fact, occurs apart from the others. Perception is never a mere contact of the mind with the object present; it is impregnated with memory-images which complete it as they interpret it. The memory-image, in its turn, partakes of the “pure memory”, which it begins to materialize, and of the perception in which it tends to embody itself: regarded from the latter point of view, it might be defined as nascent perception.

Bergson, H., (1991 [1908]), ‘Matter and Memory’, trans. by N. M. Paul and W. Scott Palmer, New York: Zone Books.

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