Bibliographic record
Abstract
Due to the recent development of delivery and communication technology and the success of distance learning, educational organizations are starting to use distance teaching to reach students so that they can learn anytime and from anywhere (Daniel, 1997). At the same time, businesses and organizations are increasingly using distance learning technology to bring the training to employees rather than send the employees for training. As a result, faculty and trainers are required to make the transition from classroom face-to-face teaching to distance teaching. One of the drawbacks in making the transition to distance delivery is faculty and trainers may not be prepared to function in the new role which is a major challenge for administrators (Agee, Holisky & Muir, 2003). Also, distance teaching is seen as an add-on for faculty in dual mode institutions (Wolcott, 2003), and resources are not available to prepare staff to work in the distance learning setting. At the same time, the commitment to distance learning from senior officials tend not to be as strong when compared to traditional delivery especially in dual mode institutions where there are both face-to-face delivery and distance delivery, and faculty have to teach both classroom delivery and distance delivery (Betts, 1998; Hislop & Atwood, 2000). Hence, it is important that administrators support distance delivery for it to be successful. According to Betts (1998), administrators who show interest in distance learning and who have experience in distance learning will influence faculty to use distance learning methods. To make the transition to distance delivery, training of faculty is important to make sure they are prepared to perform effectively and efficiently so that they can be productive and meet the needs of learners when working in the distance learning environment. The faculty should experience the distance delivery method as a student, and the format of the training should model the distance delivery process (Moloney & Tello, 2003). This entry will describe why training of faculty is important and what type of training should be provided for distance teaching.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.094 | 0.057 |
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".