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References about pedagogy

References about the cognitive processes and about pedagogy

Among the most flagrant paradox of the higher teaching system all around the world, is the evaluation of the professors mainly by their research results and not according to the quality of their teaching. In most cases, the pedagogic training of professors is extremely reduced.

When you get some interest in any knowledge acquisition processes or any memorization processes of knowledge, it is highly important to know what are the essential cognitive mechanisms involved in this learning process. This is precisely the matter of cognitive sciences which are relevant to the study of superior mental processes, especially those concerned by memorization and knowledge acquisition and the resolution of problems.

Therefore, we shall give some important results, in our sense, coming from cognitive sciences.

Essential cognitive mechanisms

The cognitive sciences are at the junction of numerous other research objects from human sciences and cybernetics, insofar as cybernetics frequently uses analogies between the operation of the human spirit and the working of a computer. These computers are, among all the human artifacts, those which may show some (artificial) intelligence.  

Some phenomena are essential in the cognitive process. Among them, there are the following :

  • the structure of memory and the memorization operation.

  • the representation of the knowledge.

About the structure of memory and the memorization operation

Researches about memory led for about fifty years has resulted in a better understanding of memory and in the proposition of models for it. Among the most quoted models is the Atkinson & Shiffrin’s model (1968), which perceives three main components :

  • The sensorial memory,

  • The working memory,

  • The long-term memory.

The sensorial memory is activated when one perception organ, eye or ear, detects information. Its main characteristics is to be very ephemeral. The raw information we receive is stored only during a very short time in the sensorial memory. (1\3 s. duration for the visual information, 3-4 s. duration for the aural information).

Miller has shown in the fifties that the working memory is very limited : the human brain can operate with only 5 to 7 elements maximum in the same time. However, these elements may even be very complex, so that this restriction may be overpowered, as we shall show it later on. Besides the working memory may have some extensions whether we operate with the discursive and visual representation, so that several kind of intelligence are operating.

The working memory does the operations of analysis and decomposition of the received raw information, so that the information is decoded, reformulated and linked to the already stored knowledge. Then the working memory gives them a meaning according to their nature and the already existing knowledge in the memory. If the imposed cognitive load is beyond the treatment capacity of the working memory, at least one part of the information is lost.

In the long-term memory, the knowledge is stored permanently, according to a currently yet unknown processes. Concerning long term memory, numerous questions have not been solved, e.g. how we forget (the forgetting). Only the information with a meaning, can be memorized in the long term so that they can be reminded easily.

The memorization is easier when we use various technical means such as repetitions, images associations, the use of mnemonic means such as acronyms, keywords or small stories …

To sum up the above, the superior cognitive processes have to give a sense to raw information coming from the sensorial memory, by using the working memory. This is to be be done very quickly, if it isn't, a new information flux is coming for a substitution. If this information is estimated significant during this very short time, it may be registered in the long term memory.

Of course, the operation of these processes and their synchronization is done essentially in an unconscious manner.

Representation of the knowledge and memorization

The informations perceived by the sensorial channels have to be interpreted before its comparison to the already memorized knowledge. To possibly be memorized, the informations which are not already part of a knowledge acquisition, undergo some transformations and some -possibly complex - elaborations.

What we may call the elements that are likely to be stored into memory may take various forms. The scientists in favour of the cognition sciences quite commonly refer to the following kinds :

  • The general concepts, as for instance the concept of 'home', or the concept of 'dog', which can represent a class of objects. This representation is characterized by a set of specific properties, as well as by its connections with other concepts;

  • The propositions which may be defined as elementary discursive knowledge;

  • The productions, which represent some elementary bricks of procedural knowledge;

  • The schemas or the mental schemes, which correspond to structures of organized knowledge. Considering how essential they are in knowledge acquisition, we shall develop hereunder their representations.

Schemas are organized elements which enable us to build representations of our world that can be stored into memory. Most schemas are built during training, although it is a common opinion of scientists that some primitive schemes are inborn. Among them are the schemes on which the language is built.

There are a lot of scheme types to represent objects, persons, or abstract concepts, or even connections between schemes. One scheme may include another scheme, like the russian dolls. They may even include complex structures involving other schemes.

The schemes are not mere copies of the world, but the output of some elaboration process concerning informations we have about the world. They are dynamical and adjustable structure. They may even include, when needed, some new informations.

As a summary, the schemes are elementary cognitive bricks, which enable us to interpret the world in order to make it intelligible and therefore to memorize it. They are the basis of any mental explanation of our own part about the world. They are the reasons for an intelligible world. They are the means for memorizing. They are present, in addition, in the composition of the operational tools we build in order to act on the world.

We have underlined hereabove the limits of our working memory. We have indicated that it can't operate with more than 5 to 7 maximum different elements. Now let's precise that these elements are mostly schemes and that, by using more and more sophisticated schemes, we could exceed beyond this restriction.

The building and the initial memorization of one scheme are difficult tasks which occupy the working memory in an intense activity. Later on, the repeated implementation of this scheme progressively turns it in an automatic use : the scheme becomes therefore an operational tool encapsulating a complex knowledge which may be manipulated, thus sparing the use of the working memory. The schemes automation can not only structure the long-term memory but also operate with complex informations without effort though the limited working memory.

As our knowledge about one domain sharpens, we become capable to enrich the schemes we are using to solve the problems we are facing. We yet can manipulate in the same time only a small number, but their individual potentiality is improving.

So what is the greatest distinction between an expert and a beginner ? It is not his intelligence or working capacity, but the degree of elaboration and sophistication of the schemes at his disposal to approach his field of competences. The expert has organized his long-term memory by storing in it numerous schemes he may recall according to the encountered difficulties. Thus, an expert can base on a very wide and highly organized knowledge to solve the problems which are submitted to him.

References

Miller, G. A. (1956). The magical number seven plus or minus two : Some limits on our capacity for,processing information. Psychological Review, 63 , 81-97.

Atkinson, R.C., & Shiffrin, R. M. (August 1971). The control of short-term memory. Scientific American, 82-90

Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. perceptual learning, automatic attending, and a general theory. Psychological Review , 84 , 127 - 190.

Cognitive Mechanisms and Learning

The application field of this theory is very rich, so we shall focus its presentation on its use in the field of engineering sciences.

Assumptions About Learning

During the process of learning (coming to know), the knower constructs a reality or at least interprets it based upon his/her apperceptions. Traditional, objectivistic conceptions of learning focus on the object of our knowing rather than the process of coming to know or what is actually learned. Constructivists assume that learners construct knowledge by interpreting our perceptual experiences in terms of our prior knowledge, current mental structures, and existing beliefs. Some of these assumptions are considered in more detail, because these assumptions form the conceptual basis for a new domain of construction environments and tools.

So, the training requires to link the new informations to the previous ones in order that the existing cognitive structures (schemas) may be sharpened.

Ill-structured knowledge results from ill-defined categories of objects, which have variable attributes or ambiguous criteria. Ill-structured knowledge domains have few general rules or principles for class inclusion that describe most of the cases, no defining characteristics to determine appropriate action, and inconsistencies between cases.

Real World, Case-Based Tasks and Environments

The most effective learning, that which is most meaningful and therefore transfers best, is case-based and involves meaningful real-world tasks.

Therefore the constructivists contest the learning not based on a realistic and well appropriate context. Without this particular context, says the constructivism theory, the information is less significant.

The more efficient contexts for studying are those based on problems or on particular case studies, which put the student in a quite realistic situation. Those contexts require from the student that he improves his skills or gets some new knowledge in order to solve a realistic problem.

A situated learning

The characteristics of cognitive learning include the articulation (the explicitation of a tacit knowledge) and the exploration (learning how to develop and test hypothesis).

Using a metacognitive reflection, the learners can better monitor their training, learn from other students, and develop their thoughts, as well as their reflections on the acting.

Jonassen D., Mayes T., McAleese R., A Manifesto for a Constructivist Approach to Technology in Higher Education, In Duffy T., Jonassen D., & Lowyck J. (Eds), Designing constructivist learning environments. Heidelberg, FRG: Springer-Verlag

It may be useful too to refer to the very interesting synthesis done by B. Wilson about the late various pedagogic theories, which are available on-line.

Distinction between knowledge and know-how

It is extremely important for the teacher to both clearly express the learning goals, i.e., what he wants the students to have learned by the end of their training, and to have an appropriate typology to describe the knowledge to be transmitted as it is to state the learning objectives.

In didactics, the well-established distinction between knowledge and know-how is emphasized. Develay says that " declarative knowledge falls under the section of discourse and knowledge, whereas procedural knowledge falls under the section of action and know-how. This distinction is obviously vital, but it is too broad for our purposes.

In addition, didacticians have shown that knowledge as it is taught differs from so-called scholarly knowledge, i.e. as it exists in the world of research. They insist on the necessary didactic transposition of scholarly knowledge, which must be done taking into account referential social practices, , which define the context in which the teaching takes places ("research, production, engineering activities, as well as domestic and cultural activities)").

Didactic transposition highlights the difference between scholarly knowledge and taught knowledge. This difference is naturally more marked in primary or junior high school than in high school or university.

Develay, M. , De l'apprentissage à l'enseignement, ESF, 1992

Toussaint, J. (Coord.) Didactique appliquée de la physique-chimie, Nathan Pédagogie, Paris, 1996.

Articulations of scientific knowledge: The RTM(E) Model

Concepts introduced by didactics are basic, but they are not sufficient for defining the teaching content.

Unable as we were to find in the literature a model that fully met our needs, we are proposing one here, called RTM(E), in which the knowledge to be transmitted is grouped into four broad interrelated categories, namely Reality, Theory, Methods (and Examples).

The study of Reality (, i.e., nature and technology, observed facts, matter…) by observation, analysis and experimentation, makes it possible to develop or refine Theory , i.e., an explanatory diagram highlighting the similarities between the different observations of Reality and explaining them in a way that is both coherent and as simple and generic as possible. Theory on the one hand constitutes an interpretive graph for Reality, and on the other hand serves as a guide for the development of Methods (and/or operational tools) for solving problems, making use of specific concepts if necessary.

We believe that this typology is a good way to categorize knowledge relative to a scientific discipline, especially if it is rounded out by the principal applied Examples, which concretely illustrate how to resolve a class of problems (using Methods in the context of a Theory) relative to a particular aspect (of Reality).

Learning a scientific discipline requires the acquisition of both declarative knowledge for Reality and Theory, and procedural knowledge for Methods, which in essence correspond to know-how.

During learning, the instances are a key element. Therefore, the instances must be realistic. If not, the perception the students will have of the question field will be wrong. They will have the idea or the feeling that it doesn't solve the real problems. This last remarks is a counter-argument against the classical method of teaching thermodynamics : if we too much stress the perfect gases models (supposedly in order to simplify), then the students conclude on this thought that the discipline solving capacity is very limited.

Reality, Theory, Methods and Examples are an essential part of what Kuhn, in the afterward to the 2nd French edition of La Structure des Révolutions Scientifiques, calls the disciplinary matrix, which represents what a group of scientists have in common (instead of the term Reality, he speaks of nature, instead of Theory, he uses symbolic generalizations, but the meaning is really the same, and he strongly stresses the objective of "normal science" which is to solve enigmas , which requires the development of Methods, and also the key role played by Examples). The disciplinary matrix is important in that it is typical of the identity of the group and in that its content is an integral part of the training of students, due to the fundamental role it plays in structuring schemas. This is in line with a comment by Develay, who says that "the knowledge to be taught constitutes the legacy that one generation wants to leave to the next".

Kuhn, T.S. , postface to the " Structure of the scientific revolutions ", Flammarion, 1972

Gicquel, R., Utilisation pédagogique des simulateurs : Volet 1 : éclairages de la didactique , Bulletin de l'Union des Professeurs de Physique-Chimie, n° 868, novembre 2004.

Gicquel, R., Utilisation pédagogique des simulateurs : Volet 2 : application à l'enseignement de la thermodynamique , Bulletin de l'Union des Professeurs de Physique-Chimie, n° 869, décembre 2004.

SRK Model: three levels of cognitive control depending on the degree of expertise

According to Rasmussen, human cognitive control is structured, hierarchized and adaptable, and capable of processing information on three levels: Skills-based, Rule-based and Knowledge-based.

To simplify, when an individual undertakes a task that he knows perfectly, he uses his skills (Skill-based behaviour). His reactions are very fast and he goes from activation to execution using unconscious sensory-motor processes. These "auto-pilot" patterns can be performed at the same time as other routine behaviors.

If he is confronted with a new situation, he will first ask himself whether he has already solved similar problems. Now his reaction is not automatic, but it is based on the rules, (Rule-based behavior) or procedures derived from prior experiences. For example, a process operator will refer to a set of instructions to find out how to handle a non-routine situation.

Finally, if he has to handle a problem that he is totally unfamiliar with, which in no way resembles a known situation, the individual has no suitable schema for solving the problem. To find a solution, he tries to put together a reasoning process enabling him to attain his objectives, on the basis of his knowledge, without having any rules to go by. He may try several different methods. This is called Knowledge-based behaviour.

This very interesting and useful theory was initially developed in response to a specific problem, namely training process operators on complex technical systems, such as air traffic control towers or nuclear power plants. It is aimed at individuals on a specific learning track corresponding to a long-term career, the goal being to acquire operational expertise.

This models can explain how an operator operates (i.e. through which means), but it doesn’t explain why it operates and how he constructs his knowledge. It is based on a syntactical interpretation rather than on a semantic one.

Nothing proves that it also applies to beginning students taking general courses, who are learning diverse disciplines, without needing to acquire specific expertise in any one, at least at this stage in their education.

RASMUSSEN, J. Skills, Rules and Knowledge : Signals, Signs and Symbols and Other Distinctions in Human Performance Models. IEEE trans. on Systems, Man and Cybernetics, 13, 257-266 1983. back

Cognitive Load Theory: Promote good knowledge structuring

The SRK model stresses the differences between the cognitive mechanisms used by beginners, learners or experts, but it does not explain the origins of these mechanisms. The cognitive load theory provides a very interesting conceptual framework at this level.

Based on a detailed analysis of the human ability to process information, Sweller shows that since working memory is limited (we can only process a few elements at a time and we have to assign a meaning very quickly), human learning capacity is limited.

To get around this difficulty, two mechanisms are used:

  • The schema acquisition, i.e., the construction of appropriate mental models (see above), makes it possible to break down the information into meaningful elements that can be memorized;

  • Once the schemas are structured, the tasks (procedural rules) become automatic with practice, which enables the individual to memorize the correct working methods.

The cognitive load represents the total amount of mental activity imposed at a given time onto the working memory. It depends essentially on the number of elements it has to process in the same time and on their interactiveness : it is far much easier to learn elements when they can be considered separately one from another than to consider all of them in the same time. Besides, it can be demonstrated that the cognitive load may be greatly reduced if these elements and their relations can be graphically represented.

The cognitive load is composed of one intrinsic part, related to the difficulty of the studied question, and of one extraneous part, related to the manner this question is presented. Considering the limits of our working memory, the heavier the intrinsic cognitive load is, the more necessary it is to reduce the external load by being careful to the elaboration of the presentation media addressed to students.

Moreover, cognitive overload occurs when too many different types of information have to be processed at once. Consequently, it is important to simplify educational content and avoid presenting elements separately that can only be understood together.

Based on this theory, the students must begin by structuring their knowledge. We must make every effort to help them do so, especially because it is not completely intuitive and they are unlikely to discover the appropriate schemas on their own. The schemas must be clearly explained and we have to make sure that they are relevant to students, and not just to experts. Note that there is a small dialectical problem here: it’s best if the students build their own schemas, but they cannot be left completely on their own. We need to find learner-based instructional methods that are sufficiently open.

This theory explains why the balance between rules-based and skills-based behavior according to Rasmussen changes over time, with the learner’s level of practice and expertise. The theory also coincides with the RTM(E) model, in that documented examples play an essential role in learning, since they enable students to organize knowledge in a concrete and realistic manner. Memorization of key Examples of the discipline, through immersion in personal practice enables them to cement their knowledge for future use.

Cooper, G. (1990). Cognitive load theory as an aid for instructional design, Australian Journal of Educational Technology, 6(2), 108-113.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning, Cognitive Science, 12, 257-285.

Sweller, J. Cognitive technology: Some procedures for facilitating learning and problem solving in mathematics and science. Journal of Educational Psychology, 81 (4), 457-466, 1989

Develay, M. , De l'apprentissage à l'enseignement, ESF, 1992

Sweller, J., & Cooper, G. A. The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59-89, 1985

Sweller, J. (1994). Cognitive load theory, learning difficulty and instructional design. Learning and Instruction, 4 , 295-312.

Chandler P., Cooper G., Pollock E. & Tindall-Ford S., Applying Cognitive Psychology Principles to Education and Training back

Allosteric Learning Model: a complex process of questioning prior conceptions

The cognitive load theory explains the importance of a good schema structure because of the limits of working memory, but it does not look at how learning happens. Giordan’s allosteric learning model does so by analyzing the different phases in the deconstruction of the learner’s initial conceptions, then the construction / elaboration of new knowledge, based on the affective, the cognitive, and meaning, in a given socio-cultural context.

The practical implications of this theory for teaching are particularly interesting for identifying conditions and situations conducive to learning . This can very useful when designing multimedia instructional products.

As A. Giordan says, "although the individual cannot learn alone – no one can do it for him, and that shows us how important the role of the learner is, as the only true "author" of his education – he has little chance of "discovering" on his own all of the elements that can transform his questions, his references or his relationship to knowledge." Well-designed instructional software can help a great deal in this respect, by bringing together these elements in an easy to manipulate form, to create a genuine instructional environment.

Giordan correctly emphasizes two types of knowledge complementary to declarative and procedural knowledge: the attitudes that the learners must acquire, and metacognition, which is knowledge of one's own thinking processes and strategies.

Giordan, A., Platteaux, H. Le multimédia va-t-il remplacer l'école , Colloque National Le multimédia dans l'Education, les enjeux d'une mutation culturelle, Grenoble, 1996

Giordan, A., Apprendre : une alchimie complexe, Apprendre autrement aujourd’hui ? , 10e Entretiens de la Villette, Cité des Sciences et de l'Industrie, 1999

The Redish approach

In his paper : « The implications of cognitive studies for teaching Physics », Redish develops some ideas about the contribution of cognitive studies to the teaching of physics.

He insists particularly on the importance of the mental models, which may not be complete and even not consistent with other elements already memorized by the students. It is not enough to pass on good mental models, it is also necessary to ensure that they can be present in mind in time. This requires a lot of practice. These models have to be constructed by the student, whose mind is not a « tabula rasa », but who arrives into course with his own preconceptions.

Redish insists too on the importance of the « touchstone problems », which are a leading themes for learning about a defined subject. Then Redish concludes on the importance for a teacher to be truly attentive to the difficulties encountered by the students.

REDISH, E. F., The Implications of Cognitive Studies for Teaching Physics , American Journal of Physics, 62(6), 796-803 (1994) back

  Rudiments of EPFL practical pedagogy

The CRAFT ( Research and Support Centre for Training and its relevant Technologies ) proposes a list of pages with the following title : « The rudiments of practical pedagogy . » which is full of pertinent and adequate advices for the teachers who wish to improve their pedagogy, by using for instance ICT in education.

ICT-use importance in education (ICT : Information Communication &Technology)

The hypertext is one of the best instances of constructivist training environments. The educational environments using the ICT favor too the knowledge construction by providing them cognitive learning tools.

The ICT-based environments, such as hypertext, may represent the world to students, in its natural complexity. Rather than simplifying reality to get it more understandable, the interpretations of reality, and the tasks they lead to, need to be situated in a significant and realistic context which reflects the natural and badly structured complexity of our real world.

Rather than trying to represent the world to the student, the ICT-use in education tools should give toolboxes which could enable the student to establish more significant but personal interpretations and representations of the world.

It is the opinion of constructivists that the students at a high level of learning knowledge should face open studying environments, which would reflect the complexity of the real world. Moreover these environments would be based on some case studies, in contact with the reality of the external world.

Collaborative environments for a knowledge construction

The social negotiation for the understanding suggests to emphasize on the cooperative learning in which the student has to face other points of view casting doubts on his initial understanding. It is the opinion of constructivists that the learning environments should favour the collaborative construction of the knowledge by involving professors and students.

As the active learning is the more efficient, so the students may learn a lot from observations of other students who are facing and solving similar difficulties.

Assesment

The constructivist learning is not supposed to reflect the reality, but may rather build significant interpretations. The constructivist appreciation have to reflect this mode of construction.

The assesments of the constructivist environments should measure the progress made at the level of superior mental processes.

Conclusion

The higher teaching is particularly appropriate to integrate constructivist approaches because its target is to favour the advanced knowledge acquisition and to turn out self-sufficient adults.

References

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