Early Experiences in Broadening the Use of Web-Based Learning to Mainstream Faculty
Bibliographic record
Abstract
The history of instructional technology is littered with discarded technological innovations that promised to revolutionize the way teaching and learning occurred. This paper will describe the efforts of the staff of the Digital Media Projects Office at Ryerson Polytechnic University in introducing an integrated web-based course delivery package called WebCT to Ryerson faculty and students. The goal was to provide and support a software package that would enable faculty considered to be non-technical to use the web for instruction, extending the reach of this instructional technology beyond that of the typical innovators and early adopters. In this paper we will report on our efforts to ensure that these web-based tools do not ultimately contribute to the list of discarded instructional technologies. Specifically we will reflect on the techniques used to promote and support WebCT, our experiences on the successes to-date and suggestions on how to continue to reach out to faculty not already using web-based tools for instruction
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".