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Enregistrement W4391955108 · doi:10.18260/1-2--37401

Investigating Team Roles Within Long-Term Project-Based Learning Experiences

2024· article· en· W4391955108 sur OpenAlexaff
Amy Dunford, Edwing Medina, Jack Bringardner

Notice bibliographique

Revue2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024
Typearticle
Langueen
DomainePsychology
ThématiqueInnovative Teaching and Learning Methods
Établissements canadiensYork University
Organismes subventionnairesAmerican Society for Engineering Education
Mots-clésAccreditationComputer scienceKey (lock)Term (time)Discussion boardKnowledge managementHuman–computer interactionMultimediaMedical education

Résumé

récupéré en direct d'OpenAlex

Abstract This evidence-based practice paper investigates students' perceptions of and discourse surrounding their team roles on a multidisciplinary project-based learning (PBL) team and if/how those views and enactments change over time. This study also examines how designated student leaders' self-perceptions of their team role compare to their peers' assessment of their leadership. Analysis of the study's perception and discourse data will consider participants' discipline, gender, ethnicity/race, and academic year. From this, we aim to understand how leadership on PBL student teams is established, enacted, and evolves over time, and what factors may influence such development. While many engineering education programs have first-year cornerstone and final-year capstone project experiences, the middle years tend to lack similar multidisciplinary and long-term team project experiences. The Vertically Integrated Projects (VIP) Program at a large urban university encompasses teams of students – from various academic years and disciplines – who are advised by faculty to engage on long-term and large-scale projects. The VIP Model is aimed directly at engaging students during the middle years of engineering education and maintaining their engagement on a project of their choice for at least 3 consecutive semesters, honing technical and professional skills. The VIP Model is an evidence-based approach for multidisciplinary project-based learning that is active at 40 institutions around the world. Long-term student engagement affords each VIP Team the time and space to develop an organizational structure. Over this extended period of time, students also have the opportunity to establish, enact, and change leadership roles. This study focuses on the subset of VIP Teams categorized as Design Competition VIP Teams. These VIP Teams participate in annual intercollegiate competitions that are hosted by, sponsored by, and/or affiliated with professional organizations or societies (e.g., SAE, NASA, ASCE). These VIP Teams are generally the largest teams that have formal team roles and student leadership structures (in contrast to VIP Teams with faculty-guided leadership). BelbinⓇ defined (and provided discourse examples of) a team role as a particular behavioral preference while performing tasks with other team members, distinguishing it from a functional role (the operational knowledge and technical skills relevant to performing a task). This distinction allows for the possibility that a group may be composed of several team members with the same functional role and different team role(s). This study employs the BelbinⓇ Team Role Self-Perception Inventory (TRSPI), the Observers' Assessment Sheets (OAS), and discourse analysis of video-recorded Design Competition VIP Team meetings at several time points to investigate students' perceptions and enactments of their and their peers' designated and explicit team role(s) and how these may change over time. Comparing collected survey and discourse data over time to participants' demographic survey data will make possible a greater understanding of how these perceptions and discourse vary with respect to discipline, academic year, race/ethnicity, and gender. The goal of this study is to understand how students develop and enact their team roles, how peers view designated leaders' team roles, and whether these observations are related to demographic and academic backgrounds.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,270
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,131
Tête enseignante GPT0,403
Écart entre enseignants0,272 · 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 tête enseignante, pas un consensus.

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

Citations4
Publié2024
Routes d'admission1
Résumé présentoui

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