Bibliographic record
Abstract
Reusable learning objects are an approach that is receiving a significant amount of attention in distance-based and online education (see Reports # 11, 40, and 46 in this series).They have the potential to provide cost-effective, personalised instruction with a short development time.Instructional design principles, however, must play an important part in any such development effort, within a design process that occurs on two levels.At the higher level, instruction must be designed to deliver material efficiently to students at the modular/ course/ programme level.Design principles should be applied at the secondary level, at which the unique characteristics of learning objects are determined.Various instructional design (ID) methodologies are capable of dealing with these issues.The current report discusses a sle of these methodologies, and compares the ID adequacy of objects in four major learning object repositories: Merlot, CLOE, EOE, and Wisconsin Online.At the time of writing, each of these repositories contains objects that are inadequate from the ID point of view. Instructional Design Requirements of Learning ObjectsFor a learning object (LO) to have instructional impact, it must embody explicit planning for learning, intentional instructional design (ID).Solid ID is a critical part of reusable LO design (Longmire, 2000;Wiley, 2000;Douglas, 2001;and Sosteric and Hesemeier, 2002).For the purposes of the current review, it will be assumed that the term LO refers to a digital entity intended to further the achievement of a specific learning objective.This working definition discounts those LOs that generate learning serendipitously, and could restrict the review to LOs in computer-based environments.Digital entities, whose primary purpose in a given context are to provide information, will be referred to as content objects (CO).Depending upon the context, a CO may become a LO or may serve as a LO component.LOs typically comprise two different major components that may, or may not be, co-resident on the same computer -the learning content and the metadata.Both of these LO aspects must be considered during the ID process for the object to be effective.The metadata provide the learning context for the LO, and are the key to its reusability.The prime requirement of a LO is that it is reusable in different contexts (Sicilia and Garcia, 2003), and specifically in each of its target contexts as defined in the metadata.The granularity of an LO is defined as its instructional size, a characteristic hotly debated among LO advocates (Wiley, 2000).As learning objectives may be N.B.Owing to the speed with which Web addresses are changed, the online references cited in this report may be outdated.They can be checked at the Athabasca University software evaluation site: http
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".