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Enregistrement W7135416579 · doi:10.1108/dl-09-2007-0013

CASE STUDY: York University Improves the Educational Experience using Mediasite

2007· article· en· W7135416579 sur OpenAlexaffabout
Kelly Parke

Notice bibliographique

RevueDistance Learning · 2007
Typearticle
Langueen
DomaineComputer Science
ThématiqueMobile Learning in Education
Établissements canadiensYork University
Organismes subventionnairesnon disponible
Mots-clésPresentation (obstetrics)PopularityPerspective (graphical)Set (abstract data type)Higher educationAcademic yearFront (military)

Résumé

récupéré en direct d'OpenAlex

Founded in 1959, York University is now Canada’s third largest university and world-renowned for attracting students who forge their own unique paths. York’s top-ranked programs set international standards. The faculty expands the horizons of its students, providing them with a broad perspective of the world that opens up new ways of thinking. York offers a full range of programs and degrees and is setting the contemporary standard in academic excellence, pioneering research and innovative thinking.As one of the premier educational facilities in North America, York attracts an increasing number of students worldwide. However, its well-earned respect and popularity came with a price: a swelling of the university’s classrooms. In addition, a growing percentage of its students are adults trying to balance career, family, and education. A few years ago, the university began delivering online lectures through audio players, and later added video, but it was dissatisfied with the two-dimensional experience this provided its students. The school was desperate for a system that could combine the audio, video, and graphic components of a typical classroom lecture.“We sought a solution which would allow us to deliver students a richer academic learning experience than just having a professor stand up in front of the classroom and present the same lecture,” said Kelly Parke, senior multimedia designer at York University. “We also wanted to make the presentation as user-friendly as possible, allowing our adult learners to view the content at their convenience.”York’s professors and directors believed a multimedia online learning solution could address some of its growing pains while improving the quality of students’ education, but they needed a system that would not create additional work for the faculty. In fact, that caveat was stipulated in the faculty union contract. As a result, York assembled a research team to find an e-learning solution that: required little or no technical expertise or training; offered live and on-demand rich media via the internet; eliminated the need for costly and time-consuming postproduction, and delivered the highest return on investment in the shortest amount of time.The research team evaluated dozens of technologies and found many that met one or more of the criteria, but none that met all four requirements. Finally, after 2 years of searching, York University found one that did: Sonic Foundry’s Mediasite.Educators instantly began envisioning how the versatile Web communication solution could be incorporated into its teaching curriculum to most effectively reach its rapidly growing student body.“We uncovered immediately upon our purchase of Mediasite that having the right tools in place allowed educators to be more effective and reach a greater number of students, while at the same time relieving some of the pressure on our classrooms and parking lots,” said Parke.Mediasite’s presenter-friendly design meant that educators could continue to focus on teaching, instead of having to learn new multimedia technology or Webcasting software. They simply plug their notebook PCs into the system. There was no new software to load. No new skills to learn. No extra time required. No need to submit their slides ahead of time for encoding. Mediasite automates all the necessary processes—capturing, encoding, integration, streaming, and archiving of all the audio, video, and graphic content in real-time. Unlike other Web presentation systems that limit users to PowerPoint, Mediasite gives York professors the ability to use any teaching tool, such as document cameras, graphics tablets or smart boards, and maintain the high-resolution of their original instructional materials.For York University students, Mediasite means the convenience of easily accessing courses remotely anywhere, anytime using their Web browser. Now, more students can continue their education online as their schedules permit, reducing the problems associated with overcrowded classrooms and the headaches of commuting. Furthermore, Mediasite’s unique navigation capability lets students quickly preview the content of archived lectures by simply selecting a thumbnail.The university has seen spikes in on-demand usage just before exams, indicating students are using the archives to review course material. Both professors and students are giving rave reviews about their experience with Mediasite, illustrating the important role Mediasite has played in enhancing the educational experience at York.Given the results to date, York plans to expand its online learning program to a broader student population by offering audio and video podcasting to engage the mobile learner. The university is in the process of outfitting most of its new classrooms with robotically controlled cameras and state-of-the-art computer systems to make them more Web-enabled. “We have learned through our experience with Mediasite that a well-designed, content-rich presentation is vital to a successful distance education experience,” said Parke.

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)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Étude de cas · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,045
Score d'incertitude au seuil0,090

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0130,005
Communication savante0,0060,005
Science ouverte0,0020,006
Intégrité de la recherche0,0040,004
Charge utile insuffisante (le modèle a refusé de juger)0,0170,002

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 Opus0,023
Tête enseignante GPT0,293
Écart entre enseignants0,270 · 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'étudeÉtude de cas
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

Citations0
Publié2007
Routes d'admission2
Résumé présentoui

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