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Enregistrement W2915659358

The 21st Century Classroom: Technology as a Transformative Tool in Educational Routines, Rules, and Rituals

2018· dissertation· en· W2915659358 sur OpenAlexfundaboutno aff
Jessica Rizk

Notice bibliographique

RevueMacSphere (McMaster University) · 2018
Typedissertation
Langueen
DomaineArts and Humanities
ThématiqueLiteracy, Media, and Education
Établissements canadiensnon disponible
Organismes subventionnairesSocial Sciences and Humanities Research Council of Canada
Mots-clésTransformative learningPedagogyMathematics educationSociologyEngineering ethicsEngineeringPsychology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This dissertation discusses a current niche in sociological literature: technology and interaction rituals in elementary schools. In particular, it examines the relationship between classroom interactions and the increasing available new forms of technologies (i.e. iPads, robotics kits, Smart boards) that are finding their way into schools. In doing so, I consider what new interactions and digital tools might mean for student engagement in what has now become known as the “21st century classroom”. Two pivotal sociological theories are utilized in this project: 1) Collins (2004) interaction ritual (IR) theory and 2) Bourdieu’s (1974; 1986) concept of cultural capital. Both are valuable in understanding how the introduction of digital tools in mainstream schools can influence or change interactions between and among students and teachers in classrooms, how they may impact student engagement gaps. Traditionally speaking, schools have long valued and rewarded certain types of interactions—student obedience alongside teacher authority, an orderly and compliant classroom, emphasis on more traditional teaching and so forth. Student engagement was not necessarily a point of interest, as was having a systematic classroom. However, perhaps technology is beginning to change those valuations, and create new types of classroom interactions that are unique to the 21st century—classrooms that have a more student-centered pedagogy, whereby teachers work in tandem with students to engross them in the learning process, and where student engagement is more much valued. If this is true, this may be a sign of some new emerging types of IRs that are beginning to surface in the presence of technology. Collins' (2004) theory of IR focuses on the emotional input and feedback of individuals that transpire in interactions among actors, which in the case of classrooms, consist of teachers and students. The theory holds that interactions produce or deplete “emotional energy” of participants depending on many key factors (physical co-presence, exclusivity of group, mutual focus/mood, bodily synchronization). A successful ritual is one in which participants have a mutual focus on a particular “symbol” or “emblem” unique to that group. Through this research, I propose that technology can serve as that “emblem” to group membership, and as a result, can facilitate new kinds of IR. “Cultural capital”, in comparison, is usually considered to be a collection of symbolic elements such as skills, tastes, clothing, materials, credentials and so on that one acquires by being a member of a particular social class. In education, cultural capital can refer to having valued sets of skills and knowledge that are aligned with school rewards. Traditionally, this usually meant a middle-upper class advantage in schooling, as students of more affluent families were able to learn valued kinds of skill sets to help them achieve better in school. However, with the advent of new technologies, I question whether notions of cultural capital have changed as a result, and whether possessing a digital skillset is in and of itself, a new type of valued capital. Can new technologies produce more equalizing experiences for students of varying SES backgrounds? To explore the possibility of digital tools in classrooms creating new sets of rituals with new kinds of valued cultural capital, this study adopts a qualitative methodology, consisting of elementary classroom observations, interviews, and focus groups with teachers and students in ten school boards across Ontario, Canada. My research discusses three integrated themes. I begin by asking first, how have technologies transformed the ways in which students and teachers interact with, and amongst each other? By providing a new medium for both teacher pedagogy and student learning, this has major implications for classroom engagement. Secondly, I explore the possibility that one unintended consequence of using digital resources (compared to more traditional print media), has been a reduction in home-based inequalities, and a more “even playing field” for students of varying SES. With the ease, accessibility, and affordability of technology today, students in vary capacities are exposed to new valued skillsets. Lastly, I consider how technology can be a type of “leveler” for different kinds of students, which can allow them to participate and facilitate new types of ritual inclusions. I focus both on gendered interactions and exchanges between students with special needs as examples. The exploration of these three themes guides my research on the use of educational technologies across classrooms. These have important implications for sociologists, educational researchers, and policy-makers alike.

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)0,003
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: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0050,029
Communication savante0,0130,011
Science ouverte0,0010,007
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,010
Tête enseignante GPT0,210
Écart entre enseignants0,200 · 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

Citations5
Publié2018
Routes d'admission2
Résumé présentoui

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