MétaCan
Menu
Retour à la cohorte
Enregistrement W3027504733 · doi:10.32674/jis.v10i1.1851

21st Century International Higher Education Hotspots

2020· article· en· W3027504733 sur OpenAlexaboutno aff
Karin A. C. Johnson

Notice bibliographique

RevueJournal of International Students · 2020
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueHigher Education Governance and Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHigher educationMathematics educationSociologyPedagogyGeographyPsychologyPolitical scienceEconomic geographyEconomic growthEconomics

Résumé

récupéré en direct d'OpenAlex

The Institute of International Education (IIE) 2018 Open Doors report highlighted that the United States is the leading international education destination, having hosted about 1.1 million international students in 2017 (IIE, 2018a). Despite year over year increases, U.S. Department of State (USDOS, 2018) data show that for a third year in a row, international student visa issuance is down. This is not the first decline. Student visa issuance for long-term academic students on F visas also significantly dropped following the 9/11 attacks (Johnson, 2018). The fall in issuances recovered within 5 years of 2001 and continued to steadily increase until the drop in 2016. Taken together, the drops in international student numbers indicate a softening of the U.S. international education market. In 2001, the United States hosted one out of every three globally mobile students, but by 2018 it hosted just one of five (IIE, 2018b). This suggests that over the past 20 years, the United States has lost a share of mobile students in the international education market because they’re enrolled elsewhere. The Rise of Nontraditional Education Destination Countries Unlike the United States, the percentage of inbound students to other traditional destinations such as Canada, the United Kingdom, France, and Germany, has remained stable since the turn of the 21st century. Meanwhile, nontraditional countries like the United Arab Emirates (UAE) and Russia are garnering more students and rising as educational hotspots (Knight, 2013). The UAE and Russia annually welcome thousands of foreign students, respectively hosting over 53,000 and 194,000 inbound international university students in 2017 (UNESCO Institute for Statistics, 2019). This is not happenstance. In the past 5 years, these two countries, among others, have adopted higher education internationalization policies, immigration reforms, and academic excellence initiatives to attract foreign students from around the world. The UAE is one of six self-identified international education hubs in the world (Knight, 2013) and with 42 international universities located across the emirates, it has the most international branch campuses (IBCs) worldwide (Cross-Border Education Research Team, 2017). Being a country composed of nearly 90% immigrants, IBCs allow the UAE to offer quality higher education to its non-Emirati population and to attract students from across the Arab region and broader Muslim world. National policy and open regulations not only encourage foreign universities to establish IBCs, they alsoattract international student mobility (Ilieva, 2017). For example, on November 24, 2018, the national government updated immigration policy to allow foreign students to apply for 5-year visas (Government.ae, 2018). The Centennial 2071 strategic development plan aims for the UAE to become a regional and world leader in innovation, research, and education (Government.ae, 2019), with the long-term goal of creating the conditions necessary to attract foreign talent. Russia’s strategic agenda also intends to gain a greater competitive advantage in the world economy by improving its higher education and research capacity. Russia currently has two higher education internationalization policies: “5-100-2020” and “Export Education.” The academic excellence project, known as “5-100-2020,” funds leading institutions with the goal to advance five Russian universities into the top 100 globally by 2020 (Ministry of Science and Higher Education of the Russian Federation, 2018). The “Export Education” initiative mandates that all universities double or triple the number of enrolled foreign students to over half a million by 2025 (Government.ru, 2017). These policies are explicitly motivated by boosting the Russian higher education system and making it more open to foreigners. Another growing area is international cooperation. Unlike the UAE, Russia has few IBCs, but at present, Russian universities partner with European and Asian administrators and government delegates to create dual degree and short-term programs. Historically, Russia has been a leading destination for work and education migrants from soviet republics in the region, but new internationalization policies are meant to propel the country into the international education market and to attract international students beyond Asia and Europe. Future Trends in 21st Century International Education Emerging destination hotspots like the UAE and Russia are vying to become more competitive in the global international higher education market by offering quality education at lower tuition rates in safe, welcoming locations closer to home. As suggested by the softening of the U.S. higher education market, international students may find these points attractive when considering where to study. Sociopolitical shifts that result from events such as 9/11 or the election of Donald Trump in combination with student mobility recruitment initiatives in emerging destinations may disrupt the status quo for traditional countries by rerouting international student enrollment to burgeoning educational hotspots over the coming decades.

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)0,003
score de la tête « metaresearch » (Gemma)0,003
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,194
Score d'incertitude au seuil0,648

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,007
Études des sciences et des technologies0,0050,002
Communication savante0,0160,009
Science ouverte0,0010,015
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,1940,043

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 Opus0,022
Tête enseignante GPT0,371
Écart entre enseignants0,349 · 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

Citations12
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of International StudentsMême sujetHigher Education Governance and DevelopmentTravaux en français237 207