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Enregistrement W562664693 · doi:10.5860/choice.50-1234

Encyclopedia of the Sciences of Learning

2012· article· en· W562664693 sur OpenAlexfundno aff

Notice bibliographique

RevueChoice Reviews Online · 2012
Typearticle
Langueen
DomaineNeuroscience
ThématiqueNeuroscience, Education and Cognitive Function
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of Illinois at ChicagoUniversity of California, IrvineLingnan UniversityStockholms UniversitetUniversidade do MinhoNational Taiwan Normal UniversitySyddansk UniversitetUniversità Cattolica del Sacro CuoreUniversity of HaifaCentre National de la Recherche ScientifiqueUniversiti Putra MalaysiaOrta Doğu Teknik ÜniversitesiVrije Universiteit AmsterdamLomonosov Moscow State UniversityRadboud UniversiteitHarvard Graduate School of EducationUniversity of AucklandInstitut National de la Santé et de la Recherche MédicaleGriffith UniversityCenter for Advanced Study, University of Illinois at Urbana-ChampaignKU LeuvenPomona CollegeUniversity of New South WalesAnadolu ÜniversitesiMonash UniversityUniversity College CorkMassey UniversityAustralian Catholic UniversityCardiff UniversityUniversity of SussexUniversity of California, Los AngelesUniversità degli Studi di MilanoSaint Louis UniversityManchester Metropolitan UniversityUniversity College LondonUniversity of SouthamptonMax Planck Instituut voor PsycholinguïstiekKent State UniversityWellcome TrustUniversity of OxfordMcMaster UniversityNorges Teknisk-Naturvitenskapelige UniversitetIndiana University BloomingtonUniversidade Estadual de CampinasUniversity of BernCarnegie Mellon UniversityRobert Gordon UniversityiMindsImperial College LondonUniversity of MemphisUniversity of OklahomaYork UniversityQueen's UniversityUniversity of MiamiUniversity of Central FloridaNorthwestern UniversityUniversity of MissouriUniversiteit UtrechtTU Graz, Internationale Beziehungen und MobilitätsprogrammeUniversité de LyonUniversity of Technology SydneyIllinois State UniversityUniversity of ConnecticutMcGill UniversityUniversität SalzburgUniversität des SaarlandesUniversiteit GentWorcester Polytechnic InstituteBrown UniversityQueensland Brain InstituteUniversity of Wisconsin-MadisonDepartment of Epidemiology, Biostatistics and Occupational Health, McGill UniversityUtah State UniversityPrinceton UniversityUniversity of Illinois at Urbana-ChampaignUniversity of GeorgiaPennsylvania State UniversityWestern Michigan UniversityHarvard UniversityUniversity of Pennsylvania
Mots-clésEncyclopediaHistoryLibrary scienceComputer science

Résumé

récupéré en direct d'OpenAlex

Learning is existential, and so its study must be complex and interdisciplinary.Over the past centuries, researchers from different fields have posited many theories to explain how humans and animals learn and behave, i.e., how they acquire, organize, and deploy knowledge and skills.Basically, learning is defined as a relatively permanent change in behavior and/or in mental associations due to experience.Learning is a response to environmental requirements and different from biological maturation, which, however, is a fundamental basis for learning.From a historical point of view, learning had been an issue of epistemology and philosophy since ancient times.Nevertheless, the twentieth century may be considered as the century of psychology of learning and related fields of interest, such as motivation, cognition, and metacognition.It is really fascinating to see the various currents of the twentieth century research in learning, remembering, and forgetting.And it is interesting to see that many basic assumptions of early theories have survived the paradigm shifts of psychology and epistemology that occurred during the twentieth century.Beyond folk psychology and naı ¨ve theories of learning, psychological learning theories can be grouped into several basic categories, such as behaviorist and connectionist learning theories, cognitive learning theories, and social learning theories.However, learning theories are not limited to psychology and related fields but can be traced back to ancient philosophers, such as Socrates, Plato, and Aristotle.It is certainly true that the topic of learning also played a significant role in the philosophy of the Middle Ages (e.g., St. Thomas Aquinas), and in the modern era philosophers such as Descartes, Hume, Locke, Kant, and many others were interested in the topic.The same holds true for philosophers of the twentieth century, who were highly interested in learning.It is noteworthy that the so-called fathers of psychology as a discipline, Wilhelm Wundt (1832-1920) and William James (1842-1910), were both originally professors of philosophy.In the 1880s, Wundt began studying rote learning of lists of nonsense verbal items, and a short time later, James foreshadowed many aspects of modern neurobiology of learning and even connectionist theory.Whereas Wundt and James remained closely aligned with the field of philosophy and the application of introspective self-observation, Hermann Ebbinghaus (1850-1909) began studying human memory and higher cognitive processes (such as meaningful learning) by means of experimental methods.This transported the study of learning and remembering out of philosophy and into the realm of empirical research, providing valuable tools until today.Ebbinghaus' seminal work on learning and remembering can be considered as the beginning of systematic psychological research on learning and remembering for the twentieth century.Another strong influence was Pavlov's reflexology and his experiments with animals.This can be considered as the beginning of research on animal learning, which was also characteristic, to a large extent, of the emerging fields of associative psychology (e.g., Thorndike) and Gestalt psychology (e.g., Ko ¨hler).At the beginning of the twentieth century, these two sources -associative psychology and reflexologygave rise to connectionism and the idea of learning by trial and error, whose most prominent supporter became Thorndike .Clearly, the first half of this century was strongly influenced by connectionism (and behaviorism) and Gestalt psychology, whereas the second half can be considered as the period of the emergence of cognitive and constructivist conceptions of learning.Psychologists and biologists have studied learning in animals and humans within the realm of both paradigms.Nowadays, animal and human learning and cognition are separate but related fields of study within psychology and biology, each with an identifiable history that is often intertwined with the other.Beyond psychology and biology, disciplines such as anthropology, sociology, and education focused on the topic of human learning in the course of the past centuries.However, one of the most important innovations for research on learning resulted from the emerging computer sciences and their focus on machine learning.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,276
Score d'incertitude au seuil0,923

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0020,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0050,007
Études des sciences et des technologies0,0010,003
Communication savante0,0100,007
Science ouverte0,0020,003
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,2760,125

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,098
Tête enseignante GPT0,365
Écart entre enseignants0,267 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations241
Publié2012
Routes d'admission1
Résumé présentoui

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