Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".