Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This qualitative research examines the perceptions of e-learning stakeholders within the Canadian Department of Defence and makes strategy recommendations that may support e-learning adoption.A review of the literature describes the diffusion of educational technology as a slow and evolutionary process that may take twenty-five years or more to be realized in educational settings.Adoption is more successful if the technologies are easily integrated, not too complex and offer obvious advantage over existing practices.A review of distance education systems suggests a return to the basics.Large distance education systems thrive using print as the media of choice to support learning.Leading theories of distance education inform the reader of the essential requirements to support learning at a distance including the requirement for interaction and communication.The Canadian Forces (CF) are aligned with the Advanced Distributed Learning (ADL) and the Shareable Content Object Reference Model (SCORM).As one of only two ADL colabs located outside the United States, learning objects, contrary perspectives to the learning object paradigm, and notions about the SCORM standard are explored.Moreover, many complex notions embedded in the learning object concept have led some to ask where is the learning in learning objects and complex standards.Two related themes that have recently gained momentum are the convergence of knowledge management with e-learning and the rapid development of e-learning.These notions seem to support a shift from course-based learning to just-in-time and informal learning constructs.Elements of a strategic plan including the requirement for vision and leadership are examined as critical components to adoption.There is no shortage of educational technology.However, vision, leadership, and pedagogical practices have not kept pace with technological development.Hence, strategy and vision must be able to withstand the constant barrage and challenge of implementing new technologies.The v Chapter Four, "findings," provides a rich description of the challenges of implementing advanced technology applications, in the words of the candidates who were interviewed.The Chapter Five, "conclusion," provides strategic recommendations that may be considered for implementation.vi Chapter II -Review of Related LiteratureThe introduction of new technology can be both exciting and alienating.It may create or destroy jobs, and it can both enhance the quality of our lives and seriously undermine it.It poses challenges for all aspects of our society, including the ways in which we teach and learn (Paul, 1995, p. 127).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it