Enhancing the Classroom Experience with Learning Technology Teams.
Bibliographic record
Abstract
19 The driving force behind adoption of educational technologies in universities is the belief that they improve the quality of teaching.1 Despite this assumption, faculty experimentation with technologies in the classroom is slow and focuses on a narrow range of tools such as e-mail, presentation handouts, Web pages, and Internet resources.2,3 This pattern suggests that weaving technologies into the learning experience poses challenges that go beyond mere adoption. The use of new tools in the classroom, however, does not ensure that teaching will improve or that students will learn. Rather, thoughtful pedagogical strategy matters most if educational technology is to succeed in building invigorating learning environments.4 How are faculty best supported in efforts to integrate technology in their courses? This question identifies the single most important technology issue for the next few years in U.S. public universities, according to the 1999 National Survey of Information Technology in U.S. Higher Education.5 In response to the need for faculty support, some campuses have developed comprehensive programs to reach this goal.6,7 Queen’s University, a midsize research university in Canada, provides a selection of activities to engage faculty in thinking about educational technologies. The Learning Technology Unit offers regular workshops on both the technical and pedagogical aspects of frequently used tools such as WebCT, PowerPoint, and HTML. Educational Technology Days showcase best practices Enhancing the Classroom Experience with
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".