The Academic Preparation of Transnational Students: An Analysis of Curriculum and Teaching Methods from Four International Systems
Notice bibliographique
Résumé
General note to editors—we do NOT use the word international students. The reasons are explained in the body of the article but international—as defined in higher education–is actually more limited than the focus of this article. The term is being removed throughout. There has been a considerable increase in the number of international students over the past decades. Although much of the research on international students focuses on academic skills and the broader student experience beyond the classroom, less is known about how subject-matter preparation and the experience of student-centered teaching methods increasingly promoted in North American universities differ among students whose prior education was gained outside of North America. To build a better sense of the subject-matter preparation and teaching methods experienced in their prior education by students with prior education outside of North America, this article poses the following questions: What curriculum did students follow in their general education? What teaching methods were common? This paper presents the results of a scoping literature review, which aims to “map” key themes in a field of research to clarify complex topics and orient future inquiries. This review looked at the subject-matter preparation and teaching methods in four regions that send many students to universities in the English-speaking world: China, India, the Middle East and North Africa, and Latin America. The history of educational reforms in the 20th century informs the findings. Reforms brought much consistency in science and mathematics curricula across regions. However, there is less consistency in the coverage of other subjects, such as the humanities and social sciences. In terms of teaching methods, traditional instructor-centered teaching methods remain prevalent despite reforms calling for student-centered methods. Instructors benefit from awareness of the prior knowledge of their students so they can adjust their teaching plans to students’ knowledge base, something that varies by subject area. Instructors also benefit from recognizing that students may require time to adjust to participatory approaches to teaching.
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 | 0,000 |
| 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 | 0,000 |
| 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 ».