Bibliographic record
Abstract
There are several dimensions to the time factor in e-learning: 1. The time of the learner. 2. The time of the learning activity. 3. The time affordances of different media and technologies. I will discuss each briefly. THE TIME OF THE LEARNER This is perhaps the best understood factor in e-learning. One reason why e-learning is increasing rapidly, at least in North America, is because of the flexibility, particularly regarding time of study, that e-learning affords. Because of increases in tuition fees (inevitable given the increased access to higher education and reluctance to increase taxes to pay for this), more and more students are working at least part-time to pay for their initial undergraduate and graduate education. Furthermore, because of the demands of knowledge-based occupations such as health, telecommunications and computer software engineering, there is increasing demand from lifelong learners to return for postgraduate studies and continuing education. Thus increasingly students are combining work, family and study. Online learning is clearly providing the flexibility that such students need. It does this by allowing them to shift studying to times that are most convenient for them. A recent study by Statistics Canada (2009) found as many students over 24 years of age taking education or training programs as those under 24 in Canada, which probably accounts for the increasing demand for e-learning. However, such data needs more close examination and breaking down by type of program and institution. Thus while there is plenty of research to support the argument that e-learning provides increased flexibility for especially adult learners (e.g. the Sloan Commission studies) there is still room for more research on exactly what demographics are best served by e-learning, in terms of flexibility, and the implications of this for course and program design.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".