A multi-component model for assessing learning objects: The learning object evaluation metric (LOEM)
Bibliographic record
Abstract
While discussion of the criteria needed to assess learning objects has been extensive, a formal, systematic model for evaluation has yet to be thoroughly tested. The purpose of the following study was to develop and assess a multi-component model for evaluating learning objects. The Learning Object Evaluation Metric (LOEM) was developed from a detailed list of criteria gathered from a comprehensive review of the literature. A sample of 1113 middle and secondary students, 33 teachers, and 44 learning objects was used to test this model. A principal components analysis revealed four distinct constructs: interactivity, design, engagement, and usability. These four constructs showed acceptable internal and inter-rater reliability. They also correlated significantly with student and teacher perceptions of learning, quality, and engagement. Finally, all four constructs were significantly and positively correlated with student learning performance. It is reasonable to conclude that the LOEM is reliable, valid, and effective approach to evaluating the effectiveness of learning objects in middle and secondary schools.
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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 | 0.022 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".