Defining Varied Learning Environments: An International DKG Perspective
Notice bibliographique
Résumé
This article continues a series initiated by members of the Bulletin's Editorial Board. The goal of the series is to feature interviews conducted with Delta Kappa Gamma members or other educational leaders on a topic related to the theme of the issue. Here, board member Trybus presents the results of a unique approach to such an interview by reporting comments made by international state organization presidents and leaders in response to a survey seeking their perspectives on learning in their countries.In considering whom to interview for this issue of the Bulletin on the theme Varied Learning Environments, I quickly focused on the importance of context, wondering what this topic might mean to educators in different countries. The resources seemed quite evident, because The Delta Kappa Gamma Society International has a global network of state organizations worldwide. Beyond the United States, countries include Canada, Denmark, Estonia, Finland, Germany, Great Britain, Iceland, Japan, The Netherlands, Norway, Sweden, Puebla, San Luis Potosi, Costa Rica, El Salvador, and Nuevo Leon. My belief was that, although the settings might be diverse, the changing roles and competencies of teachers working in the context of varied learning environments constitute a shared constant throughout our global educational systems. Accordingly, the more that members can find opportunities to learn from each other in different settings, the more innovative we, as educators, can be in creating learning that are successful.Today's students are complex, exhibiting wide-ranging needs from content knowledge in traditional subjects to acquisition of twenty-first-century skills. In order to meet these needs, learning need to be fluid, innovative, and changing. Educators can no longer accept the premise that a one size fits teaching and learning environment will be sufficient to prepare students in schools around the world. The majority of learning have been traditional face-to-face classrooms; however, the movement to online learning is growing in the educational community at all levels. With this shift comes an increased need to support teachers to expand their competencies in planning and designing curriculum, providing instruction, communicating through different modalities, and finding diverse ways to engage students in all types of environments.Looking for an international perspective on these issues and the overall concept of learning environments, I created and sent a survey to state organization presidents and leaders outside of the United States. Ten questions were designed to elicit participants' responses to (a) identify problems in schools that affect student learning; (b) determine teaching methods that define the learning environment; (c) indicate the use of technology in the classroom; and (d) determine factors that influence learning environments. Responses from 10 participants (41% return rate) are provided verbatim and present an interesting perspective on these topics.What are the most difficult problems schools in your country face to help students learn?* Class sizes are big and make it difficult to help the individual student. Not enough classrooms.* Students' ability levels are mixed, including students with disabilities in regular classes.* Multicultural issues and the mental health needs of our students.* Building a bridge between school and home. Teachers are often overstretched to meet students' needs. Students miss classes and refuse help.* School laws keep changing every few years, and not enough money to buy technology.* Infrastructure in rural areas differs from urban areas.* Majority of teachers don't know how to use technology, and at the same time students are overusing technology and losing the ability to speak face-to-face, use a calculator, and write. …
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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.
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».