Developing Learning Objects for Secondary School Students: A Multi-Component Model
Bibliographic record
Abstract
Previous research on the development of learning objects have three notable characteristics: a focus on either technical or learning features, but not both, a target audience consisting exclusively of higher education students, and the absence of formal evaluation.This study provides a detailed description and formal evaluation of a multi-component model used to develop five learning objects for secondary school students.Overall, two thirds of the students reported that the learning objects were beneficial, citing a motivating theme, interactivity, and visual qualities as the most important features.However, almost 60% of all students were critical about the learning object quality.Clarity of instructions, help functions and organization/layout presented the most problems.While the development model used in the study emphasized both technical and learning components, the latter was far more important to teachers and students.Key steps that appeared to be beneficial in the development process included a collaborative team approach, understanding the learner, a focus on clear instruction and organization, and using a comprehensive, theoretically supported evaluation metric to examine the quality and benefits of learning objects.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".