MétaCan
Menu
Retour à la cohorte
Enregistrement W2508089488 · doi:10.18260/1-2--20445

Evolution of Student Attitudes Toward Teamwork in a Project-based, Team-based First-Year Introductory Engineering Course

2020· article· en· W2508089488 sur OpenAlexaboutno aff
Laura Alford, Robin Fowler, Stephanie Sheffield

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueBiomedical and Engineering Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTeamworkContext (archaeology)PsychologyPresentation (obstetrics)Team effectivenessScale (ratio)Engineering educationProject-based learningMedical educationTest (biology)EngineeringKnowledge managementMathematics educationComputer scienceEngineering managementManagementMedicine

Résumé

récupéré en direct d'OpenAlex

Abstract Evolution of Student Attitudes Toward Teamwork in a Project- based, Team-based First Year Introductory Engineering CourseFor the past decade, engineering schools have developed a variety of models for introducingfirst-year students to their chosen field. One common model is a project-based course, in whichstudents work together on teams to design (and often build and test) something. Through thisprocess, students develop professional and teamwork skills and often also practice technicalcommunication in the context of a real design project. Such courses have been found to increasestudent persistence1 and self-efficacy, though in the latter case project-based learning appears tobe more beneficial for male students than for females2. More research is needed, however, intostudent attitudes toward teamwork and the characteristics of team experiences that lead toimprovements in student attitudes toward working in teams.This study is an exploratory investigation of student attitudes toward teamwork at three timepoints during a first-year team-based design course: before students have begun working inteams, after they have completed an initial small-scale design project in a 4- or 5-person team,and after they have completed a larger-scale design project with a different, similarly-sized team.In this presentation, the authors will explain how teamwork is managed in this first-yearengineering course through our methods of grouping students into teams of 4-5 as well as ourefforts to facilitate successful teams. These efforts include: ● a fun, engaging “ice breaking” exercise to encourage students to consider their team’s communication style as well as to get to know each other ● class discussion of the characteristics of effective teams ● peer mentors (alumni of the course) paired with each student team ● a skit about teamwork prepared by the Educational Theatre Company at our institution ● incorporation of real-stakes team member evaluations in which team members evaluate each other and receive peer feedbackWe will then report the results of a survey, administered this fall at the three time pointsdescribed above (n= about 55). The survey includes quantitative information regarding studentperspectives of the teamwork experience and the fun, frustration, and learning that occurred andqualitative information on the students’ perceived positive and negative characteristics ofteamwork. By administering the same survey at three time points during the semester, we canchart the evolution of those perspectives and assess our teaching methods. 1 Knight, D. W., Carlson, L. E., & Sullivan, J. F. (2007, June). Improving engineering student retentionthrough hands-on, team based, first-year design projects. 31st International Conference on Research inEngineering Education, Honolulu, HI.2 Kilgore, D., Sheppard, S., Atman, C. J., & Chachra, D. (2011, June). Motivation makes a difference, butare there differences in motivation? What inspires women and men to study engineering? 118th ASEEAnnual Conference & Exposition, Vancouver, BC, Canada. The largest contribution of this project may be the qualitative information gathered via thesurveys, which will help us better understand what aspects of teamwork experiences studentsbelieve most impact their perceptions of teamwork. For example, we may learn that groupcharacteristics such as gender balance or group size are most important in student attitude towardteamwork. This qualitative data will be used to further develop research questions that could beinvestigated quantitatively with a follow-up survey in future semesters.The data will be collected in September, October, and early December 2013, and is not availablefor this initial abstract. If accepted, a paper written up on this project will of course include thedata and an analysis of it. Our teaching schedule would allow us to conduct a new survey with anew cohort of students in the semester beginning in January 2014, and that data would beavailable for presentation at the conference (but not for inclusion in the paper).

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)0,002
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,013
Tête enseignante GPT0,236
Écart entre enseignants0,223 · 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'étudeObservationnel
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

Citations11
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même sujetBiomedical and Engineering EducationTravaux en français237 207