Symposium 3: Learning Design Family Tree to Back Reuse and Cooperation
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
The types of artefacts or solutions used towards the creation learning designs (Learning design Solutions, LdS) are diverse (patterns, course maps, activities, etc.) and have varied or multiple lives. Sometimes designs are created by an individual teacher for a single use with their students. But often, they are reused the following years or by other teachers with minor adaptations. Other times, designs are co-outlined by networks of teacher and later refined by each teacher for their particular group of students, or they are co-designed involving students. These scenarios can imply the creation of multiple replicas of the same design, which in turn may be duplicated and refined as new LdS. In this paper we state that supporting the management and visualization of interrelated LdS can back scenarios of cooperation and reuse in the context of design communities. In particular, we propose an LdS branching model visualized following a family-tree metaphor. We define a "learning designs’ family" as a collection of learning designs which weren't started from scratch but by replicating (or duplicating) a particular existing learning design. The model, and its visualization, has been implemented as a new feature in the LdShake teacher-community platform, as part of the Metis Integrated Learning Design Environment (ILDE). The development of the feature consists of two main modules: one devoted to the management of the family-related LdS and another focused on their visualization. On the one hand, the management module is in charge of storing LdS replicas’ data, managing their interrelations, and retrieving a learning design family corresponding to a given LdS. On the other hand, the visualization module displays a learning design family as square-shaped icons representing LdSs, and its family-relations using arrows. This first implementation of both the model and its visualization has enabled the collection of the first feedback from learning technology experts. The evaluation was carried out online. 11 experts responded to our invitation to try the feature completing a set of tasks and an on-line questionnaire. Their opinions indicate that the feature is interesting and could significantly address relevant learning design and co-design situations. They used the feature satisfactorily but also pointed out several suggestions to improve its usability and enhance its potential utility. The suggestions are being considered in a second iteration of the model and its implementation, which will be used by teachers in the Metis workshops.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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