Sidney Pressey’s Automatic Teacher

Pressey-Testing-Machine

Pressey wanted the Automatic Teacher to give the human teacher more time for individual students (…) The machine was built out of typewriter parts and employed an intelligence test with 30 questions (…) The user responded to text question using four keys; each time the user pressed a key the machine advanced the test paper to the next question, but the counter registered only correct answers (…) In December 1925 Pressey began to seek investors, first among publishers and manufacturers of typewriters, adding machines, and mimeograph machines, and later, in the spring of 1926, extending his search to scientific instrument makers (…) in contrast to his peers, investors failed to see the virtues of Pressey’s machine (…) multiple efforts were made by him to massively the machine (he even invested his own money) but high production costs and difficulties in alignment made dragged production to an halt (…) after several attempts Pressey publicly admitted defeat. In a third and final School and Society article, he skewered education as “the one major activity in this country which is still in a crude handicraft stage (…) The Automatic Teacher was a technology of normalization, but it was at the same time a product of liberality.

 

References

  1. Petrina, S., 2004. Sidney Pressey and the Automation of Education, 1924-1934. In Technology and Culture, 45(2): 305-330, DOI: 10.1353/tech.2004.0085, full text available here

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Visible Learning Meta-Study

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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?”

hattie-ranking-influences-effect-sizes-student-achievement-rangliste-updated-2009-2011-2015-new-web

 

References

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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Connectionism

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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.

 

References

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

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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)

 

References

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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Zone of Proximal Development

The-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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Think-maps

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Competence in design praxis appears not to be measured by the quantity of knowledge gained, but by knowing where to find it, which specific kind of knowledge to apply in a particular situation, and how to use it when needed. It is the development of thinking skills that is critical in design education (…) there is more in knowing how to design than just knowing about designs. Meta-knowledge is the knowledge of how to organize what one knows (…) knowledge acquisition is based upon the organization and development of conceptual structures (…) in order to model design thinking processes, the conceptual mapping of design ideas can be constructed into larger structures, the think-maps.

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Think maps

  • they are founded on constructivism (active learner/learning by doing) and mapping (organizing and representing knowledge)
  • they propose that by constructing a map that reflects one’s thinking in a domain, we make knowledge learned explicit. they attempt to convey knowledge directly.
  • they are a cognitive teaching framework based upon the student’s ability to organize and formulate knowledge structures in design thinking.

A concept map is a representation of knowledge structures through a graphlike
structure of nodes and links (…) a map is achieved when a meaningful structure has been created (…) an important distinction is frequently made between in-domain linkages in the map and cross-domain linkages (…) Think-Maps is a form of conceptual mapping for design

References

Rivka Oxman, 2004. Think-maps: teaching design thinking in design education. In Design Studies 25, pp. 63–91, doi:10.1016/S0142-694X(03)00033-4

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Knowledge claims

Types-of-Knowledge-Claims

  • Claims of fact: those that can be verified or falsified, proven true or false
  • Claims of value: value judgments
  • Claims of policy: what should be done instead of what is being done
  • Claims of concept: those that are about the meaning of things
  • Claims of interpretation: how are some data understood

The authors claim that natural and social science publications tend to make singular knowledge claims of similar kinds whereas design publications often contain multiple knowledge claims of different kinds.

Multiple knowledge claims of different kinds within individual journal publications might be the consequence of a young, multidisciplinary field. Another explanation might be that scholars publishing in Design Studies tend to embrace the values of design and science, which may account for those publications making claims of fact and claims of policy. Finally, a third explanation might be that scholars publishing in Design Studies are writing for multiple audiences with diverse needs. (bold is mine)

 

References

Jordan Beck, Erik Stolterman, 2016. Examining the Types of Knowledge Claims Made in Design Research. In she ji, Tongji University and Tongji University Press.

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