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인터넷방송 실무교육 커리큘럼 개발을 위한 선험적 연구

2001· article· ko· W1931654334 sur OpenAlexaboutno aff
이인희

Notice bibliographique

Revue한국방송학보 · 2001
Typearticle
Langueko
DomaineComputer Science
ThématiqueHigher Education and Teaching Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFacilitatorCurriculumProcess (computing)Job analysisMedical educationKnowledge managementPsychologyComputer sciencePedagogyMedicineJob satisfaction
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This study was conducted to develop a curriculum for internet broadcasting(webcasting) education at a preliminary level by using DACUM, a well-known job analysis process. The curriculum presented in this study may serve as an exemplary model which can be adopted by either four-year colleges or two-year junior colleges for the instruction of webcasting production. Developed more than 30 years ago by a Canadian university team as a fast and reliable way to identify job tasks for training curricula, DACUM has become one of the best-known and most-used job analysis techniques, both in educational and work settings. DACUM has become the basis for human resource and training functions in many industry education-related fields. The DACUM process brings together a qualified DACUM facilitator and a panel of five to nine workers in the occupation being analyzed. The panel members must be articulate, considered outstanding in their occupation and possess highly developed technical knowledge and skills. The facilitator, specifically trained in the DACUM process, is essential for valid and usable outcomes. Within a few days, the team compiles a comprehensive list, or chart, of all duties and tasks associated with the position. The validity of DACUM is based on three premises: 1) expert workers can describe their jobs better than anyone else; 2) any job can be described in terms of the competencies or tasks that successful workers in that occupation perform; and 3) the specific knowledge, skills and attitudes required by workers to perform their tasks correctly can be defined. The DACUM process calls for verification of the charts by other workers in the same job and by their supervisors or managers. By applying the DACUM process, this study identified webcasting industry as four job categories: web producer, site manager, contents manager, and webcasting engineer. Results of this study indicate the following: First, a web producer is responsible for needs analysis of customers, planning, marketing, promotion, human resource and production management, and so on. To provide a curriculum for these job requirements, the DACUM charts suggested five subjects such as Research Methods, Introduction to Web Business, Contents Planning, System Analysis, and Web Marketing. Second, site manager's job primarily includes server and network management, hardware and software management; and the DACUM charts presented four subjects such as System Management, Web Design, Web Programming, and Site Management. Third, a contents manager deals with all contents to be included on the web site, e.g., the production of texts, graphics, audio, video, and other types of information. Content manager's job is similar to that of a director in the television broadcasting industry. While television directors handle only videos, contents managers in the webcasting are responsible for all forms of communication performed via the Internet. Thus, the following subjects were drawn by the DACUM charts: Web Site Planning, Webvideo Programming, Digital Video Directing, Nonlinear Video Editing, Shooting, Video Aesthetics, and Audio Editing. Fourth, webcasting engineers handle streaming servers and encode video outputs into digital files. The following subjects were drawn by the DACUM charts: Streaming Contents Production, Streaming Server Management, Encoding, and Digital Video Processing. A total of 20 subjects, identified as necessary for the training of webcasting practitioners, suggest that the curriculum should be offered from a multidisciplinary perspective including communications, media arts, engineering, and computer graphics. As a preliminary curriculum-developing step, results of this. study present many useful findings and ideas for webcasting educators and institutions.

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)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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.

Tête enseignante Opus0,051
Tête enseignante GPT0,367
Écart entre enseignants0,316 · 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'étudeQualitatif
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
Publié2001
Routes d'admission1
Résumé présentoui

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