Using empirical data to clarify the meaning of various prescriptions for designing a web‐based course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Design prescriptions to create web‐based courses and sites that are dynamic, easy‐to‐use, interactive and data‐driven, emerge from a “how to do it” approach. Unfortunately, the theory behind these methods, prescriptions, procedures or tools, is rarely provided and the important terms, such as “easy‐to‐use”, to which these prescriptions refer are not defined. The empirical results reported here bring lighting on the meaning of several design prescriptions that contain qualitative attributes. This paper aims at clarifying the meaning of several web‐based course design prescriptions found in the literature in the context of two music web‐based courses. Two examples are presented and the results of the students’ assessment regarding several design prescriptions are given. First, what we learned while producing the first release of the web part of an undergraduate music course entitled Teaching and Music Technology is presented. Then, what else we learned when the second release was assessed by students is detailed. The next part concerns what we used while developing the undergraduate music course French‐Canadian folk which gives access to several music files and scores. Again the results of the students’ assessment are presented. The list of the various technologies that must be highly mastered to produce such musical content is given.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it