Implementing EDIA: Building belonging into the laboratory learning environment
Notice bibliographique
Résumé
Actively building a sense of belonging in students has been shown to increase engagement, retention, and enjoyment of students in courses, programs, and higher education in science.A recent meta-analysis of an early intervention tested in 45 American colleges and universities showed the power of creating a sense of normalcy that difficulties will arise when moving and making a new home to produce a growth mindset that these are common perceptions that can be resolved by persisting and trying again.Setting expectations of challenge and sharing the experiences of previous students helps to prepare a growth and resilience mindset in freshmen before they begin college or university (Walton et al. 2023).The journal Cell Biology Education has published a clear guide to evidence-based pedagogies that develop a sense of belonging in classes: https://lse.ascb.org/evidence-based-teaching-guides/inclusive-teaching/pedagogical-choices/#belonging.Groups of students with diverse perspectives have been shown to be better at problem solving than students who are only high AbstractLaboratories are natural active learning environments where students are immersed in learning the cognitive and physical skills to apply the conceptual knowledge of the course.To create an inclusive environment that makes every student feel welcome and respected, we can use many methods to build a sense of belonging to a supportive learning environment that fosters their development as scientists.Introductory surveys based on the student's values and barriers they face gives students the message that they are considered individuals and their opinions are encouraged.A classroom culture that encourages group work, generating big questions that they want answered by the course, flipped classroom activities that generate collaborative problem solving, and a lab culture that encourages peer teaching all contribute to a supportive learning experience.Organization of students in diverse assigned pods of four students, group in-lab assignments, peer review of draft student research papers before assessment by markers improve every student's understanding and achievement.Additional benefits are the development of the student's ability to critically evaluate their own work, respect viewpoints and abilities outside of their usual peer group and sometimes make new friends.Learning management systems designed with Universal Design for Learning principles provides choices for student's pre-lab preparation and automatic marking of pre-lab quizzes frees TA time for assignment feedback and focusing on struggling students during laboratory sessions.Transformation of courses by small changes each term is possible with a phased-in approach of inclusive initiatives.Discussion and suggestions from participants were encouraged during the conference workshop and are included here.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».