Gaining instructional design expertise through self-designing, using, and evaluationg a performance support system
Bibliographic record
Abstract
In order to gain instructional design (ID) expertise, the author self-designs and uses an instructional design performance support system (IDPSS). The recursive, dynamical, and systematic process of designing, using, and evaluating the IDPSS has effectively engaged the author in learning ID knowledge and skills. Three tools are designed and used: the consilience of learning theory tool, the ID competency tool, and the design-based research (DBR) tool. The media format includes not only computer software, such as Excel and OneNote, but also traditional pen and paper. The author evaluates and synthesizes relevant ID knowledge, and creates specific models guiding her practice. She tries to create her own instructional theory model through systematically drawing useful implications from various learning theories. This learning process is characterized by five modes of thinking: enactive, iconic, story-telling, mathematical-thinking, and formal academic writing. The author self-reflects on her learning experience by discussing a few misconceptions she has encountered, and how she has tried to correct the misconceptions. The author designs a framework for her proposed approach to gain ID expertise, and hopes that it can be applicable to other instructional designers. Two other instructional designers have tried this approach in a small scope, and have contributed to building the framework. As the current project tries to initiate a DBR project, a real DBR project will rely on the efforts of many instructional designers who can try this approach over a long period of time. The author discusses the potential for creating a standard DBR documenting instrument.
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 | 0.050 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".