Using empirical data to clarify the meaning of various prescriptions for designing a web‐based course
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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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 | 0.072 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".