Interoperability Gap Challenges for Learning Object Repositories & Learning Management Systems
Bibliographic record
Abstract
An interoperability gap exists between Learning Management Systems (LMS) and Learning Ob ject Repositories (LOR). LORs are responsible for the storage and management of Learning Objects and the associated Learning Object Metadata (LOM). LOR(s) adhere to various LOM standards depending up the requirements established by user groups and LOR administrators. Two common LOM standards found in LORs are CanCore (Canadian LOM standard) and the Sharable Content Object Reference Model (SCORM) Content Aggregation Model (CAM). In contrast, LMSs are independent computer systems that manage and deliver course content to students via a web interface. This research addresses three important issues related to this problem domain: (a) a lack of metadata standards that define the format of how assessment data should be communicated from Learning Management Systems to Learning Object Repositories, (b) a lack of Information Engineering (IE) architectural standards for the transfer of data from Learning Management Systems to Learning Object Repositories, and (c) a lack of middleware that facilitates the movement of the assessment data from the Learning Management Systems to Learning Object Repositories. Thus, the three goals of this research are: (a) make recommendations for extending the CanCore and SCORM CAM LOM standards to facilitate the storage of assessment and summary assessment data, (b) define the foundation for an IE architectural standard based on an Access Control Policy (ACP) and Data Validation Policy (DVP) using a reliable consensus of experts with the Delphi technique, and (c) develop a middleware prototype that transfers learning assessment data from multiple Learning Management Systems into the Learning Object Metadata of Learning Objects that are stored within a CanCore or SCORM compliant Learning Object Repository.
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 | 0.096 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.033 | 0.059 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".