Processus d'apprentissage, savoirs complexes et traitement de l'information : un modèle théorique à l'usage des praticiens, entre sciences cognitives, didactique et philosophie des sciences.
11 Jun 2013-
TL;DR: Cherchant et al. as mentioned in this paper propose a modele allosterique, which is an interface between the sciences of education, the sciences cognitives, and the philosophy des sciences.
Abstract: Cherchant a etablir un pont theorique et pratique entre les sciences de l'education, les sciences cognitives et la philosophie des sciences, la these developpe un modele didactique a l'interface entre ces disciplines : le modele allosterique de l'apprendre initie et developpe par Giordan (1988) et al. (1992), qui s'inscrit dans le paradigme des theories du changement conceptuel. Nourri par les travaux recents des psychologues cognitifs sur les processus d'apprentissage tels que les theories du recyclage neuronal (Dehaene, 2007) ou de l'inhibition cerebrale (Houde & Tzourio-Mazoyer, 2003), ainsi que sur diverses theories relatives a l'elaboration de la pensee telles que l'economie comportementale (Tversky & Kahnernan, 1982) ou le modele-cadre SRK (Rasmussen, 1990), ce modele developpe et precise le concept d’allosterie a travers la description et la formalisation des processus de deconstruction-reconstruction des conceptions, qui ont lieu lors des apprentissages complexes. De la phase de theorisation du modele, effectuee par un recours aux formalismes de la reactivite chimique en accord avec la metaphore initiale de l'allosterie, il est possible de deduire divers environnements didactiques operatoires et feconds pour le praticien de l'enseignement et de la mediation scientifiques. Ces previsions theoriques sont alors mises a l'epreuve de l'experimentation didactique a travers une recherche de terrain centree sur la notion d'experience contre-intuitive (Eastes & Pellaud, 2004) menee aupres de differents types de publics.
Citations
More filters
272 citations
01 Jan 2000
TL;DR: In this article, the authors examined the implications of individual differences in performance for each of the four explanations of the normative/descriptive gap, including performance errors, computational limitations, wrong norm being applied by the experimenter, and a different construal of the task by the subject.
Abstract: Much research in the last two decades has demonstrated that human responses deviate from the performance deemed normative according to various models of decision making and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be interpreted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experimenter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the model response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implications of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance errors are a minor factor in the gap; computational limitations underlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Unexpected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate.
231 citations
Journal Article•
149 citations
79 citations
01 Dec 1997
TL;DR: In this article, positron emission tomography (PET) was used to examine the neural substrates of topographical memory retrieval in licensed London taxi drivers of many years experience while they recalled complex routes around the city.
Abstract: Functional imaging to date has examined the neural basis of knowledge of spatial layouts of large-scale environments typically in the context of episodic memory with specific spatiotemporal references. Much human behavior, however, takes place in very familiar environments in which knowledge of spatial layouts has entered the domain of general facts often referred to as semantic memory. In this study, positron emission tomography (PET) was used to examine the neural substrates of topographical memory retrieval in licensed London taxi drivers of many years experience while they recalled complex routes around the city. Compared with baseline and other nontopographical memory tasks, this resulted in activation of a network of brain regions, including the right hippocampus. Recall of famous landmarks for which subjects had no knowledge of their location within a spatial framework activated similar regions, except for the right hippocampus. This suggests that the hippocampus is involved in the processing of spatial layouts established over long time courses. The involvement of similar brain areas in routes and landmarks memory indicates that the topographical memory system may be primed to respond to any relevant topographical stimulation; however, the right hippocampus is recruited specifically for navigation in large-scale spatial environments. In contrast, nontopographical semantic memory retrieval involved the left inferior frontal gyrus, with no change in activity in medial temporal regions.
44 citations
References
More filters
TL;DR: Understanding the role of dynamic auditory processing in speech perception and language comprehension has led to the development of neuroplasticity-based intervention strategies aimed at ameliorating language and literacy problems and their sequelae.
Abstract: Developmental deficits that affect speech perception increase the risk of language and literacy problems, which can lead to lowered academic and occupational accomplishment Normal development and disorders of speech perception have both been linked to temporospectral auditory processing speed Understanding the role of dynamic auditory processing in speech perception and language comprehension has led to the development of neuroplasticity-based intervention strategies aimed at ameliorating language and literacy problems and their sequelae
434 citations
Additional excerpts
...…adaptée qui est choisie par l’opérateur ; dans le mode K, la conception doit être construite à partir des conceptions pré‐existantes. On comprend notamment l’intérêt de l’approche de Rasmussen à la lecture …...
[...]
TL;DR: Neural representations of numerical information can engage extensive cerebral networks, but the posterior parietal cortex and the prefrontal cortex are the key structures in primates.
Abstract: Numbers are an integral part of our everyday life - we use them to quantify, rank and identify objects. The verbal number concept allows humans to develop superior mathematical and logic skills that define technologically advanced cultures. However, basic numerical competence is rooted in biological primitives that can be explored in animals, infants and human adults alike. We are now beginning to unravel its anatomical basis and neuronal mechanisms on many levels, down to its single neuron correlate. Neural representations of numerical information can engage extensive cerebral networks, but the posterior parietal cortex and the prefrontal cortex are the key structures in primates.
433 citations
Additional excerpts
...…également recourir à l’expérimentation et la mesure de paramètres physiques ou chimiques : temps de 90 réaction, rythmes cardiaques, sudation, trajet du regard (eye tracking, figure 21), attention et rythme …...
[...]
TL;DR: A view is presented that is currently developing out of the converging work of developmental psychologists, evolutionary psychologists and cognitive anthropologists about the emergence and evolution of cultures.
421 citations
Additional excerpts
...…des travaux des logiciens (révision des croyances : Gärdenfors, 1992), des économistes (finance comportementale : Tversky & Kahneman, 1974), des anthropologues (évolution culturelle : Sperber & Hirschfeld, 1999, 2004) et des linguistes (linguistique générative : …...
[...]
TL;DR: In this paper, an instructional design review of programs and strategies developed by cognitive psychologists, using the cognitive apprenticeship model as a conceptual framework is presented, and the authors identify common design strategies common to all of the model programs.
Abstract: The purpose of this paper is to review from an instructional-design (ID) point of view nine teaching programs developed by cognitive psychologists over the last ten years. Among these models, Collins' cognitive apprenticeship model has the most explicit prescriptions for instructional design. The paper analyzes the cognitive apprenticeship model, then uses components of the model as an organizing framework for understanding the remaining models. Differences in approach are noted between traditional ID prescriptions and the cognitive teaching models. Surprisingly, we were unable to identify common design strategies common to all of the model programs. Key differences among programs included: (1) problem solving versus skill orientation, (2) detailed versus broad cognitive task analysis, (3) learner versus system control, and (4) error-restricted versus errordriven instruction. The paper concludes by arguing for the utility of continuing dialogue between cognitive psychologists and instructional designers. The field of instructional design (ID) emerged more than 30 years ago as psychologists and educators searched for effective means of planning and producing instructional systems (Merrill, Kowallis, & Wilson, 1981; Reiser, 1987). Since that time, instructional designers have became more clearly differentiated from instructional psychologists working within a cognitivist tradition (Glaser, 1982; Glaser & Bassok, 1989; Resnick, 1981). ID theorists tend to place priority on developing explicit prescriptions and models for designing instruction while instructional psychologists focus on understanding the learning processes in instructional settings. Of course, the distinction between designers and psychologists is never clear-cut. Over the years, many ID theorists have explored learning processes, just as many psychologists have put considerable energy into the design and implementation of experimental instructional programs. However, because the two fields support different literature and theory bases, communication is often lacking. Cognitive psychologists have tended to overlook contributions from the ID literature, and likewise much design work of psychologists has gone unnoticed by ID theorists. (A happy exception to this trend is the pointed dialogue on constructivism in the May and September issues of Educational Technology magazine.) Collins, Brown, and colleagues (e.g., Collins, 1991; Collins, Brown, & Newman, 1989) have developed an instructional model derived from the metaphor of the apprentice working under the master craftsperson in traditional societies, and from the way people seem to learn in everyday informal environments (Rogoff & Lave, 1984). They have called their model cognitive apprenticeships, and have identified a list of features found in \"ideal\" learning environments. Instructional strategies, according to the Collins-Brown model, would include modeling, coaching, scaffolding and fading, reflection, and exploration. Additional strategies are offered for representing content, for sequencing, and for maximizing benefits from social interaction. Of course, many of Collins' recommended strategies resemble strategies found in the ID literature (e.g., Reigeluth, 1983a). Clearly both fields could benefit from improved communication concerning research findings and lessons learned from practical tryout. With that goal in mind, the purpose of this paper is to do an ID review of programs and strategies developed by instructional psychologists, using the cognitive apprenticeship model as a conceptual framework. Several teaching systems employing cognitive apprenticeship ideals are described. The resultant review should prove valuable in two ways: cognitive psychologists should be able to make a better correspondence between their models and current ID theory, hopefully seeing areas needing improvement, and ID theorists also should be able to see correspondences and differences, which may lead to revision or expansion of our current models. The Need for Cognitive Apprenticeships The cognitive apprenticeship model rests on a somewhat romantic conception of the \"ideal\" apprenticeship as a method of becoming a master in a complex domain (Brown, Collins, & Duguid, 1989). In contrast to the classroom context, which tends to remove knowledge from its sphere of use, Collins and colleagues recommend establishing settings where worthwhile problems can be worked with and solved. The need for a problem-solving orientation to education is apparent from the difficulty schools are having in achieving substantial learning outcomes (Resnick, 1989). Another way to think about the concept of apprenticeship is Gott's (1988a) notion of the \"lost apprenticeship,\" a growing problem in industrial and military settings. She noted the effects of the increased complexity and automation of production systems. First, the need is growing for high levels of expertise in supervising and using automated work systems; correspondingly, the need for entry levels of expertise is declining. Workers on the job are more and more expected to be flexible problem solvers; human intervention is often most needed at points of breakdown or malfunction. At these points, the expert is called in. Experts, however narrow the domain, do more than apply canned job aids or troubleshooting algorithms; rather, they have internalized considerable knowledge which they can use to flexibly solve problems in real time (Gott, 1988b). Gott's second observation relates to training opportunities. Now, at a time when more problem-solving expertise is needed due to the complexity of systems, fewer on-the-job training opportunities exist for entry-level workers. There is often little or no chance for beginning workers to acclimatize themselves to the job, and workers very quickly are expected to perform like seasoned professionals. True apprenticeship experiences are becoming relatively rare. Gott calls this dilemmamore complex job requirements with less time on the job to learn-the \"lost\" apprenticeship, and argues for the critical need for cognitive apprenticeships and simulation-type training to help workers develop greater problem-solving expertise. A Brief Review of ID Models It is assumed that readers will have some prior knowledge of ID models and theories; however, we offer a short overview to allow a clear contrast with certain cognitive approaches. ID models come in two generic varieties: procedural models for systems design (e.g., Andrews and Goodson, 1980) and conceptual models that incorporate specific instructional strategies for teaching defined content (Reigeluth, 1983a, 1987). The procedural models often are represented as flowcharts reflecting a series of project phases, progressing from needs and problems analyses to product implementation and maintenance. Procedural ID models depend less on learning theory and more on systems theory and project management methodologies (Branson & Grow, 1987). Of greater interest for our purposes are the instructional-strategy models. All such models are based on Robert Gagné's conditions-of-learning paradigm (Gagné, 1966), which in its time was a significant departure from the Skinnerian operant conditioning paradigm dominant among American psychologists. The conditions-of-learning paradigm posits that a graded hierarchy of learning outcomes exists, and for each desired outcome, a set of conditions exists that leads to learning. Instructional design is a matter of clarifying intended learning outcomes, then matching up appropriate instructional strategies. The designer writes behaviorally specific learning objectives, classifies those objectives according to a taxonomy of learning types, then arranges the instructional conditions to fit the current instructional prescriptions. In this way, designers can design instruction to successfully teach a rule, a psychomotor skill, an attitude, or piece of verbal information. A related idea within the conditions-of-learning paradigm claims that sequencing of instruction should be based on a hierarchical progression from simple to complex learning outcomes. Gagné developed a technique of constructing learning hierarchies for analyzing skills: A skill is rationally decomposed into parts and sub-parts; then instruction is ordered from simple subskills to the complete skill. Elaboration theory uses content structure (concept, procedure, or principle) as the basis for organizing and sequencing instruction (Reigeluth, Merrill, Wilson, & Spiller, 1980). Both methods depend on task analysis to break down the goals of instruction, then on a method of sequencing proceeding from simple to gradually more complex and complete tasks. Some of the teaching models being offered by cognitive researchers bear strong resemblance to traditional ID models. Larkin and Chabay (1989), for example, offer design guidelines for the teaching of science in the schools (pp. 160-163): 1. Develop a detailed description of the processes the learner needs to acquire. 2. Systematically address all knowledge included in the description of process. 3. Let most instruction occur through active work on tasks. 4. Give feedback on specific tasks as soon as possible after an error is made. 5. Once is not enough. Let students encounter each knowledge unit several times. 6. Limit demands on students' attention. By any standard, these design guidelines are very close to the prescriptions found in component display theory, elaboration theory, and Gagné's instructional-design theory. The strong correspondence can be seen as good news for ID theories: Many current cognitive researchers seem to agree on some fundamentals of design that also form the backbone of ID models. On the other hand, other cognitive teaching models emphasize design elements that traditional ID models historically have under-emphasized, such as learnerinitiated inquiry and exploration, social \"scaffolding,\" cooperative learning methods, and empathi
402 citations
TL;DR: For example, this article found that students who were encouraged to use multiple particle models displayed more scientific understandings of particles and their interactions than did students who concentrated on a correct or best analogical model.
Abstract: Analogical models are frequently used to explain science concepts at all levels of science teaching and learning. But models are more than communicative tools: they are important links in the methods and products of science. Different analogical models are regularly used to teach science in secondary schools even though little is known about how each student's mental models interact with the various models presented by teachers and in textbooks. Mounting evidence suggests that students do not interpret scientific analogical models in the way intended, nor do they find multiple and competing models easy to understand. The aim of this study is summarized in the research question: How can students' understanding of the multiple models used to explain upper secondary chemistry concepts be enhanced? This study qualitatively tracked ten students' modeling experiences, intellectual development, and conceptual status throughout grade 11 as they learned about atoms, molecules, and chemical bonds. This article reports in detail a year-long case study. The outcomes suggest that students who socially negotiated the shared and unshared attributes of common analogical models for atoms, molecules, and chemical bonds, used these models more consistently in their explanations. Also, students who were encouraged to use multiple particle models displayed more scientific understandings of particles and their interactions than did students who concentrated on a “correct” or best analogical model. The results suggest that, when analogical models are presented in a systematic way and capable students are given ample opportunity to explore model meaning and use, their understanding of abstract concepts is enhanced. © 2000 John Wiley & Sons, Inc. Sci Ed84:352–381, 2000.
401 citations
Additional excerpts
...…qui deviendra plus formel mais restera phénoménologique, par la confrontation avec les résultats expérimentaux réalistes relatifs aux structures cérébrales. La philosophie des sciences sera quant à …...
[...]