• “In the broad educa4onal experience, some topics seem systema4cally to be extremely diﬃcult for students. Learning and teaching in these areas are problema4c and present persistent failures of conven4onal methods of instruc4on. Many areas in the sciences, from elementary school through university level, have this characteris4c, including, in physics: concepts of maMer and density, Newtonian mechanics, electricity, and rela4vity; in biology: evolu4on and gene4cs.” (DiSessa, 2006), p. 1
Is that because physics is especially diﬃcult? yes no
Learning sciences & conceptual change • Uncontroversial: – Students arrive to instruc4on with prior ideas – Prior ideas constrain successive learning • Controversial: – In what consists the change? – What changes? – How does change occurs? • Further issues: – What is “understanding”? – How do experts diﬀer from novices?
S. Carey: Deep reorganiza4on of knowledge vs enrichment • Conceptual change = deep reorganiza4on – incommensurability between conceptual systems dis4nguishes conceptual change from “enrichment” (adding new ideas or beliefs) or even mere change of beliefs.” • 2 main inﬂuences : – Thomas Kuhn – Jean Piaget
Scien4ﬁc revolu4ons • Kuhn: – Scien4ﬁc revolu4ons: all changes in the shia from a paradigm to another, including what counts as good science – The shia is not just a maMer of ra4onality and logic, but involves sociological reasons, pragma4c opportuni4es, etc. – Paradigms are reciprocally incommensurable – Science is not a linear, incremental path from ignorance to truth
Qualita4ve changes in thought • Piaget: – Stages of development – The way children think is qualita4ve diﬀerent from adults • From concrete to abstract thinking – Disequilibra4on/re‐ equilibra4on – Accomoda4on/Assimila4on – Construc4vism: new ideas are built upon old ones
Construc4on of new knowledge • Jerome Bruner has developed Piaget’s construc4vism into an educa4onal theory – Students should construct principles by themselves from ac4ve explora4on and construc4on: • Instructors must present experiences they are ready for, and mo4vated to learn • Structure the body of knowledge in a way that can be grasped • Favor the extrac4on of principles – Knowledge is comprised in simultaneous types of representa4ons (no stages of development, as in Piaget): • Enac4ve • Iconic • Symbolic
WHAT CHANGES? HOW STRUCTURED IS THE KNOWLEDGE TO BE CHANGED? DO CHILDREN REALLY HAVE THEORIES?
A. Gopnik: Theory theory • Premise 1/ Scien4ﬁc realism: – Scien4ﬁc inves4ga4on is the right course to ﬁnd the truth • Premiser 2/ Cogni4ve naturalism: – Knowledge can be understood from scien4ﬁc inves4ga4on of the mind • Then: There are learning mechanisms that allow humans to derive theories from evidence • It is at least logically possible that these mechanisms are involved in our development in other kinds of knowledge, such as everyday knowledge – Children build their theory of the world using the same cogni4ve devices that adults use to build scien4ﬁc theories (knowledge) • Observa4on and predic4on • Tes4ng of predic4ons • Revision of theories
S. Vosniadou: Frameworks • Concepts are comprised in bigger structures that constrain them • Theories: structured • Frameworks : less structured, internal quasi‐ coherent explanatory systems • Children do not possess theories of the physical world, but rather frameworks of presupposi4ons • Change happens through enrichment of concepts or through revision of beliefs and presupposi4ons or theories and frameworks • Revision of frameworks is the most diﬃcult process of change
M. Chi: Ontologies • Misconcep4ons are robust: they make surface in several situa4ons and can be abandoned only with great eﬀort • Conceptual change concerns those contents of knowledge for which change is really diﬃcult: – No incremental informa4on, correc4ons, tradi4onal instruc4on can produce change – Where the diﬃculty arises from? • Misconcep4ons derive from miscategoriza4ons • = • diﬃcult changes concern beliefs that have assigned to the erroneous category
J. Minstrell: Facets of par4al knowledge • Children’s (non‐experts, non‐ scien4sts) knowledge is not structured, but fragmentary and local = • Pieces of = facets – Facets are schemas and parts of schemas that are used to reason about the physical world. – Students typically choose and apply facets in the basis of the most striking surface features of a problem. – They derive their naïve facets from everyday experience. – Facets are useful in par4cular situa4ons – Facets are most likely false in general, and for the most part they are only loosely interrelated. Thus students can quickly fall into contradic4ons
diSessa: knowledge in pieces • Children’s knowledge is not organized in a small number of rela4vely well‐deﬁned and internally consistent interpreta4ons of force • Knowledge is in pieces: • intui4ve physics consists largely of hundreds or thousands of elements = p‐primes • They have roughly the size‐scale of Minstrell’s facets. – All pieces are not incorrect – Pieces are not coherently structured, but only loosely – Pieces can be highly contextual, ad‐hoc and instable: be created on the spot – P‐primes can be useful to build new concepts in learning physics • The diﬃculty is not inherent to previous structures: collec4ng and coordina4ng pieces is diﬃcult even in the absence of a compe4tor – The same diﬃcul4es can be present when a system is created from scratch from observa4on and when a system requires a change
G. Posner: Conﬂicts and ra4onal choices • Children change their views only when a conﬂict arises, that is, when they have good (ra4onal) reasons to change their mind • And children change their mind in accord with the most ra4onal hypothesis – (1) they became dissa4sﬁed with their prior concep4ons (experience a “sea of anomalies” in Kuhn’s terms); – (2) the new concep4on is intelligible ; – (3) the new concep4on should be more than intelligible, it should be plausible ; – (4) the new concep4on should appear fruioul for future pursuits.
J. Minstrell: Conﬂict and analogy • Some facts are anchors for instruc4on; others are target for change • the trick is to iden4fy the students’ correct intui4ons – their facets that are consistent with formal science – and then build on these – Iden4fy each facet – Conduct crucial experiments – Iden4fy the limits of each facet • Erroneous facets are put in conﬂict with experiences, and their limits revealed – Correct facets are iden4ﬁed and used to create good explana4ons
J. Clement: Use correct intui4ons and analogies • Analogical teaching strategy – Expose misconcep4ons through appropriate ques4ons: e.g. no upward force on a book res4ng on a table – Find an analogy (e.g. hand holding up the book)
• «1. Instruc4on is a complex mixture of design and theory, and good intui4ve design can override the power of theory to prescribe or explain successful methods. Almost all reported innova4ve interven4ons work; almost none of them lead to improvements that dis4nguish them categorically from other good instruc4on. • 2. The very general construc4vist heuris4c of paying aMen4on to naïve ideas seems powerful, independent of the details of conceptual change theory. Interven4ons that merely teach teachers about naïve ideas have been surprisingly successful. • 3. Researchers of diﬀerent theore4cal persuasions oaen advocate similar instruc4onal strategies, if for diﬀerent reasons. Both adherents of knowledge in pieces and of theory theories advocate student discussion, whether to draw out and reweave elements of naïve knowledge, or to make students aware of their prior theories in prepara4on for judgment in comparison to instructed ideas. The use of instruc4onal analogies, metaphors, and visual models is widespread and not theory‐dis4nc4ve. • 4. Many or most interven4ons rely primarily on pre/post evalua4ons, which do liMle to evaluate speciﬁc processes of conceptual change. » (diSessa, 2006, p. 14)
• “One of the great posi4ve inﬂuences of misconcep4ons studies was bringing the importance of educa4onal research into prac4cal instruc4onal circles. Educators saw vivid examples of students responding to apparently simple, core conceptual ques4ons in non‐norma4ve ways. Poor performance in response to such basic ques4ons, oaen years into theinstruc4onal process, could not be dismissed. One did not need reﬁned theories to understand theapparent cause: entrenched, “deeply held,” but false prior ideas. The obvious solu4on was veryoaen phrased, as in the quota4on heading this sec4on, in terms of “overcoming,” or in terms of convincing students to abandon prior concep4ons.” (DiSessa, 2006, p. 7)
GOOD LEARNING ‐ LEARNING FOR RE‐USE ‐ TRANSFER ‐ EXPERTISE ‐ INTELLIGENT NOVICES
Understanding Transfer Intelligent Exper4se novices
Learning deep • Good learning implies the understanding of how it can be used in real life and in diﬀerent circumstances – re‐usable – generalizable • Understanding requires deep learning: few ideas thrown in every possible combina4on – Avoid the superﬁcial instruc4on of disconnected ideas • (Whitehead, 1929)
The problem of transfer • “Imagine that a small, peaceful country is being threatened by a large, belligerent neighbor. The small country is unprepared historically, temperamentally, and militarily to defend itself; however, it has among its ci4zens the world’s reigning chess champion. The prime minister decides that his country only chance is to outwit its aggressive neighbor. Reasoning that the chess champion is a formidable strategic thinker and a dea tac4cian … the prime minister asks him to assume responsibility for defending the country. Can the chess champion save his country from invasion? ” (Bruer, 1993, p. 53)
Related ques4ons • Is the brain a muscle? • Can we train the brain’s general capaci4es ? • Which are the appropriate exercises? • Can we learn to learn?
The chess player is good at playing chess • Chess players are beMer than non‐chess players at reconstruc4ng chess board posi4ons, but only for meaningful conﬁgura4ons (Simon, 1969) • Transfer from one domain of exper4se to another (far transfer) is far from automa4c • A lot of domain knowledge is required to become an expert – = a lot of 4me (50000 hours for becoming expert at chess, Simon and Chase, 1973) – Possible role for mo4va4on
Training memory enhances memory only in trained domains • Increasing the capacity to memorize digit strings of numbers (from 7 to 70) requires – Prac4ce – Organiza4on of knowledge into structures – Metacogni4ve skills • But they work for the speciﬁc domain of exper4se, not for others – When tested with leMer strings performances get back to 7 – Ericsson et al. (1980) cited by Bransford, et al., 2000
Knowing how to be a good general does not help at being a good doctor • Students memorized the • 1. A general wishes to capture a fortress located in the center of a country. There are many roads radia4ng informa4on in the passage 1 outward from the fortress. All have been mined so that and were then asked to try task while small groups of men can pass over the roads safely, a large force will detonate the mines. A full‐scale direct 2 aMack is therefore impossible. The generals solu4on is to • Few college students were able divide his army into small groups, send each group to the to solve problem 2 when lea to head of a diﬀerent road, and have the groups converge simultaneously on the fortress. their own devices • 90 percent were able to solve the tumor problem when they • You are a doctor faced with a pa4ent who has a malignant were explicitly told to use tumor in his stomach. It is impossible to operate on the pa4ent, but unless the tumor is destroyed the pa4ent will informa4on about the general die. There is a kind of ray that may be used to destroy the and the fortress to help them. tumor. If the rays reach the tumor all at once and with (Gick and Holyoak, 1980:309, suﬃciently high intensity, the tumor will be destroyed, but surrounding 4ssue may be damaged as well. At lower cited by Bransford, et al. 2000, intensi4es the rays are harmless to healthy 4ssue, but they p. 52) will not aﬀect the tumor either. What type of procedure might be used to destroy the tumor with the rays, and at the same 4me avoid destroying the healthy 4ssue?
Exper4se • exper4se is based on: – A large and complex set of representaSonal structures – A large set of procedures and plans – The ability to improvisaSonally apply and adapt those plans to each situaSon’s unique demands – The ability to reﬂect on one’s own cogniSve processes while they are occurring (Sawyer, 2009, p. 7) • exper4se is domain‐speciﬁc
Meta‐cogni4on • The concept of metacogni4on • Intelligent novices are novices was originally introduced in capable of becoming experts the context of studying young in a new domain quickly and children (e.g., Brown, 1980; eﬀec4vely (in comparison with Flavell, 1985, 1991). For other novices) example, young children oaen • Meta‐cogni4ve skills and self‐ erroneously believe that they regula4on seem to play a role can remember informa4on in becoming “ready to become and hence fail to use eﬀec4ve experts” strategies, such as rehearsal. • But no shortcuts: domain The ability to recognize the limits of ones current knowledge remains essen4al knowledge, then take steps to remedy the situa4on, is extremely important for learners at all ages.
Selec4ve aMen4on training • « Everywhere in cogni4ve neuroscience, speciﬁc brain networks seem to underly performance. However, some of those networks have the important property of being able to modify the ac4vity in other networks. For exemple, … (Posner & Rothbart, 2007, p. 16)
Execu4ve func4ons training • Training of execu4ve func4ons – Working memory – Execu4ve control, Inhibi4on – AMen4on • Before school, enhances school performances – (A. Diamond)
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