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Enregistrement W2592324990 · doi:10.18260/1-2--19343

Connecting Cognitive Domains of Bloom’s Taxonomy and Robotics to Promote Learning in K-12 Environment

2020· article· en· W2592324990 sur OpenAlexfundno aff
James Muldoon, Paul Phamduy, Raymond Le Grand, Vikram Kapila, Magued Iskander

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineComputer Science
ThématiqueTeaching and Learning Programming
Établissements canadiensnon disponible
Organismes subventionnairesYork UniversityXerox FoundationDirectorate for STEM EducationNew York Space Grant ConsortiumNational Science Foundation
Mots-clésConceptualizationTaxonomy (biology)Bloom's taxonomyCognitionComprehensionCognitive scienceDomain (mathematical analysis)Mathematics educationComputer scienceArtificial intelligenceCognitive skillPsychologyMathematics

Résumé

récupéré en direct d'OpenAlex

Abstract Connecting Cognitive Domains of Bloom’s Taxonomy and Robotics to Promote Learning in K-12 EnvironmentLearning as currently represented in our K-12 educational system doesn’t lend itself to effectiveclassroom environments that stimulate the growth of students’ cognitive domain. Instead, manycurrent classroom practices rely on rigid theory, disconnected facts, and computational recipes.Such an approach fails to relate to students’ everyday experiences in life outside the classroom.Consequently, when classroom instruction fails to connect theory/facts/procedures with students’conceptualization of ideas, it results in a loss of significance, i.e., the students can neither recallnor appreciate the significance of their classroom learning. Alternatively, the ability to recalltheory/facts/procedures and their significance allow students to apply ideas more effectively anddevelop higher-order thinking to synthesize new concepts. In Bloom’s taxonomy, learning incognitive domain is categorized from simple to complex behaviors. Specifically, knowledge,comprehension, application, analysis, synthesis, and evaluation are the behaviors that aretypically mastered sequentially due to the nature of their increasing difficulty. Bloom’s methodallows accurate measurement of students’ learning progression through each level of behavior.As behavior at each level is learned sequentially, with each new step in the chain building on itspredecessor, this approach allows the development of a deeper level of understanding andhigher-order thinking. Designing and conducting classroom activities that support the cognitivelearning domains of Bloom’s taxonomy can allow students to develop their fundamental andhigher-order skill sets. Unfortunately, the current educational system exposes students to onlysome but not all of the core cognitive learning categories of Bloom’s taxonomy.In this paper, three concrete illustrations will demonstrate integration of the entire cognitivelearning domain with robotics lessons. The example lessons will address typical educationalobjectives of K-12 science and math disciplines and strengthen students’ ability to learn thesubject material. Three lessons, based on Lego NXT robotics, will be used to transcend agegroups from elementary school to high school levels. For example, one lesson will use a mobilerobot with an ultrasonic sensor to navigate around obstacles. First, to allow students to developknowledge and ability to recall, verbal and visual connections will be drawn between the robot’sultrasonic sensor and a bat’s echolocation. Second, to develop their comprehension, the studentswill perform experiments to establish how the ultrasonic sensor interacts with various objects inthe environment and its effect on measurements. Third, to develop their cognitive domain ofapplication, the students will construct a robot that is capable of movement and uses theultrasonic sensor to interact with its environment. Having addressed the fundamental cognitivelearning domains, the robotics lesson will be used to address students’ higher-order cognitiveskills. First, to allow the development of analysis skills, students will conduct an experimentinvolving the measurement of reaction time and robot behavior when the ultrasonic sensor is setto several different distance thresholds. Second, to develop their synthesis skills, student willemploy the data collected from the previous step and make inferences about rebuilding theirrobot to optimize its abilities to maneuver around obstacles. Finally, to develop their evaluationskills, students will obtain qualitative data on their newly synthesized robot design to determinethe results of their decisions. Such an approach will guide students through the entire cycle ofcognitive domains of Bloom’s taxonomy to ensure that all levels of learning are captured. Thefull version of the paper will include classroom assessment of the aforementioned activities, inelementary, middle, and high school grades, and recommendations for future work.

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)0,004
score de la tête « metaresearch » (Gemma)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,051

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,012
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0030,001
Études des sciences et des technologies0,0030,004
Communication savante0,0050,007
Science ouverte0,0010,006
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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 Opus0,033
Tête enseignante GPT0,234
Écart entre enseignants0,201 · 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'étudeQualitatif
Domainenon disponible
GenreEmpirique

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

En bref

Citations7
Publié2020
Routes d'admission1
Résumé présentoui

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