Evaluating a Program to Increase Faculty Use of Technology in Teaching and Learning
Bibliographic record
Abstract
Higher education institutions are trying various methods to encourage faculty to use technology in their teaching and students ’ learning. However, it is difficult to measure the effectiveness of these methods. This article describes both what one does not want to measure and what one should be measuring in this evaluation process. In addition, we give the evaluation results of our intervention program. A growing body of literature is showing the potential of technology-enhanced courses to facilitate the learning process of undergraduate students (Berger, 1992; Cummings, 1996; Lee and Johnson, 1998). However, most undergraduate institutions are finding it difficult to enlist large numbers of faculty members in adopting technology-based solutions in their classrooms (Lee and Johnson, 1998). There are deficiencies in the technology skills of the faculty (Molenda and Sullivan, 2000) among other considerations, and unless support of faculty is provided, it is unlikely these deficiencies will be addressed (Danielson and Burton, 1999). Our institution was not an exception to this. In 1997, 282 out of our 351 faculty responded to an internal survey with only 12 % (34 of 282) of faculty stating they used web-based course materials in their teaching. About a quarter said they used the web "sometimes " while well
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".