A System of Indicators for the Quality Assessment of Didactic Materials in Online Education
Bibliographic record
Abstract
The quality of didactic materials is a source of concern for teachers, users, and educational institutions that offer online education. There is a lack of indicators to help assess the quality of three key types of didactic materials commonly used in online education: didactic units (i.e., materials that contain program contents), didactic guides (i.e., materials that provide information), and additional didactic materials (materials to deepen knowledge). The objective of this article is to present a system of indicators designed to assess the quality of these types of didactic materials and guide their creation process. The system was developed based on a critical analysis of existing models designed to assess the quality of digital didactic materials. The system was validated by 16 international experts in online education, and a trial application of the system assessed five didactic guides and didactic units used by online universities in three different countries. Results of the validation process were triangulated with relevant literature, allowing the authors to make decisions regarding changes to the system in terms of maintaining, reformulating, or removing indicators. The resulting system comprises 43 assessment indicators and serves as a guide for designers, teachers, and users in the creation and selection of didactic materials for use in online education and in the assessment of their quality.
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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 | 0.100 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".