Educational Rationale Metadata for Learning Objects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Instructors searching for learning objects in online repositories will be guided in their choices by the content of the object, the characteristics of the learners addressed, and the learning process embodied in the object. We report here on a feasibility study for metadata to record process-oriented information about instructional approaches for learning objects, though a set of Educational Rationale [ER] tags which would allow authors to describe the critical elements in their design intent. The prototype ER tags describe activities which have been demonstrated to be of value in learning, and authors select the activities whose support was critical in their design decisions. The prototype ER tag set consists descriptors of the instructional approach used in the design, plus optional sub-elements for Comments, Importance and Features which implement the design intent. The tag set was tested by creators of four learning object modules, three intended for post-secondary learners and one for K-12 students and their families. In each case the creators reported that the ER tag set allowed them to express succinctly the key instructional approaches embedded in their designs. These results confirmed the overall feasibility of the ER tag approach as a means of capturing design intent from creators of learning objects. Much work remains to be done before a usable ER tag set could be specified, including evaluating the impact of ER tags during design to improve instructional quality of learning objects.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it