When Faculty Use Instructional Technologies: Using Clark's Delivery Model to Understand Gender Differences
Bibliographic record
Abstract
Instructional and learning technologies are playing an increasingly important role in postsecondary education, but there is evidence that a number of differences exist in how females and males approach, perceive, and implement these technologies. As faculty start to offer more of their courses using flexible delivery methods such as Web-based conferencing, it is important to understand what gender differences may exist in faculty members' approaches to instructional and communications technologies so that this process may be better facilitated. This paper has three purposes: to highlight some of the major gender-related differences noted in the literature, including some from a feminist perspective; to present and discuss related findings found in an exploratory, post-hoc analysis of survey data collected from our institution; and finally, to suggest areas for future research. Richard Clark's (1994) model distinguishing between Instructional and Delivery Technologies provides a framework for this discussion.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".