The use of instructional technology in poultry science curricula in the United States and Canada: 1. Demographics of technology and software use
Bibliographic record
Abstract
This paper describes a study conducted in recognition of the increasingly widespread use of computers and the importance of exposure to instructional technologies in all aspects of poultry science curriculum. The study consisted of the distribution and analyses of two cross-sectional surveys to gather detailed information on the use of instructional technology (IT) in poultry science curricula in the US and Canada. One survey was sent to departments to obtain profiles of poultry science degree programs and the availability of IT and general support for its use. A second survey was designed to obtain individual profiles of faculty use of IT and attitudes toward the use of such technologies. Information presented in this paper includes basic demographics, estimates of survey validity, and a cross-section of instructional technologies used in poultry science education. The survey found that poultry science faculty reported higher levels of use for some instructional technologies than was expected from recent reports in the literature for higher education in general. Traditional technologies were widely used for instruction, but computers and the Internet were almost as popular. Reasons for the high levels of use may be due to an increasing user-friendliness of equipment and software applications, as well as the rapid acceptance over the past 2 yr of computers and Internet technologies among the general public. Involvement with IT projects appears to be changing from passive to active, consistent with faculty reports of high interest levels and active experimentation with technology and software.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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 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".