On the Concepts of Usability and Reusability of Learning Objects
Bibliographic record
Abstract
“Reusable learning objects” oriented towards increasing their potential reusability are required to satisfy concerns about their granularity and their independence of concrete contexts of use. Such requirements also entail that the definition of learning object “usability,” and the techniques required to carry out their “usability evaluation” must be substantially different from those commonly used to characterize and evaluate the usability of conventional educational applications. In this article, a specific characterization of the concept of learning object usability is discussed, which places emphasis on “reusability,” the key property of learning objects residing in repositories. The concept of learning object reusability is described as the possibility and adequacy for the object to be usable in prospective educational settings, so that usability and reusability are considered two interrelated – and in many cases conflicting – properties of learning objects. Following the proposed characterization of two characteristics or properties of learning objects, a method to evaluate usability of specific learning objects will be presented. Key Terms: Learning objects, reusability, usability evaluation, learning technology standards
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".