Learner-centered instructional design and development: Two examples of success.
Bibliographic record
Abstract
An environmental scan of the demand for and varied levels of success of online learning products and services suggests that dropout numbers are higher in online learning. One response is to enhance strategies for supporting learners who are engaged in online distributed learning environments. These strategies are examined within the ADDIE framework. A comparative analysis of learner evaluations of two online learning projects illustrates the benefits of learne-centered development and delivery of online instruction. A professional development course for employees of the United Nations High Commissioner for Refugees written by Maree Bentley, designed by David Murphy, and delivered by the Commonwealth of Learning provides data from the area of non-credit continuing education. An instructional design course by Richard Schwier for the University of Saskatchewan provides data for a credited, graduate-level course. This article resulted in the author being an invited speaker at the Association of Pacific Rim Universities (APRU) Distance Learning and Internet Conference 2003 in Singapore. This article has been cited as a source in the report, Megatrends in e-learning provision - Literature review (Norway).
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 | 0.034 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".