Bibliographic record
Abstract
This study shared unique design experiences by examining the process of transferring residential courses to the Web, and proposed a design model for individuals who want to transfer their courses into this environment. The formative research method was used in the study, and two project teams’ processes of putting courses, which were being taught in classrooms at the time, on the Web were examined in depth to reveal and confirm the components of the design model. The participants were 13 instructional designers. In addition to the logbooks kept by the designers, individual and focus group interview techniques were employed in the data-gathering process. Two researchers analyzed the data concurrently using content analysis. The logbooks and the focus-group interviews were used for model formation, and the individual interviews to confirm the components of the model. Based on the findings from the two design cases, the experience-based e-course design model consisting of seven basic stages including forming design team, preliminary search, analysis, instructional and technical design, integration, tests, and improvements was proposed. It is considered that sustaining Web-based course design efforts within this model will enable both implementing the design process more effectively, and the Web-based course obtained at the end of the process to have higher quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".