Making sense of converging technologies and new media: a study of distance education course developers
Bibliographic record
Abstract
Distance Education, as it is practiced today in the Western world, is undergoing rapid change. This is no less true within the British Columbia distance education community. This change is due, in part, to the increasing speed of technological innovation, specifically in the convergence between broadcast, telecommunications, and data communications fields (Bates, 1994). Therefore, a research study was conducted to look at the phenomena of converging technologies and new media (CT/NM) and their impact on course development decisions in distance education for adult learners. Specifically, the purpose was to describe how selected distance education course developers were conceptualizing, or making sense, of CT/NM. This study was conducted with eight distance education course developers from two education institutions in the Lower Mainland of British Columbia, Canada between May and June 1999. Five participants were from the Open Learning Agency and three were from University of British Columbia's Distance Education and Technology Unit (DE&T). A qualitative research methodology, based on an interpretive understanding and using participant interviews and document analysis, was applied. Three conclusions emerged from the study. First, the course developers' practices were being affected by CT/NM and as a result, some new planning considerations and four new course development practices were emerging. The four practices were media and technologies replacement, hybrid course development, resource-based course development and structured information. Secondly, the course developers were applying an enhanced systems-based course development model and moving towards a new course development paradigm, based on networked multimedia and using post-fordist production processes. The final conclusion was that there were six specific organizational issues that could enable or impede the success of CT/NM for the course developers in this study. These issues were roles, training and professional development, delivery systems, funding arrangements, intellectual property policies, and new opportunities. Overall, the course developers in this study were making sense of CT/NM within their practices pragmatically, in an incremental and evolutionary way. Based on the conclusions from the study, three suggestions for further study were also provided.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".