Community platform management mechanisms to support integrated Learning Design
Bibliographic record
Abstract
This PhD Thesis contributes to the domain of Educational Technologies, and more specifically to the Learning Design (LD) research field, which focuses on supporting teachers in the creation of effective computer-supported learning activities considering the needs of their educational contexts. Research in LD has provided a myriad of tools and methods. Yet, existing tools lack collaboration support for communities of teachers engaged in learning (co-)design. Moreover, scope of tools is varied in terms of representations used, pedagogical approaches supported, and design phases targeted (from conceptualization to authoring and implementation). This diversity of tools contrasts with lack of articulation of their synergies to offer meaningful, manageable and integrated LD ecosystems for teachers and communities of teachers. This Thesis is framed in this problem area. Its guiding research question is: How can community platform management mechanisms support teachers in integrated learning design ecosystems? This question is addressed by more specific investigation towards addressing four specific research objectives. The first objective is explorative, focused on understanding needs for management mechanisms in LD community platforms. The resulting contribution includes participation in building and evaluating LD community platforms (LdShake, Learning design Sharing and co-edition, and ILDE, Integrated Learning Design Environment) in the context of Spanish and European projects, and the identification of needs tackled in the following three research objectives. The second objective deals with enabling flexible management of learning (co-)design processes that involve use of several LD tools. The associated contribution is a model and implementation for LD Workflows, which shape orchestrated uses of selected LD tools that can be applied to LD Projects. The third objective focuses on supporting management of multiple learning design versions in scenarios of reuse and co-design. The contribution is a model and visualization strategy based on a family tree metaphor. The fourth objective concerns the need for interoperability between co-(design) tools and platforms, and in particular focuses on design patterns as structured LD representations of special interest because they collect repeatable good teaching practices. The contribution is a pattern ontology for computationally representing a pattern language (working case of design patterns in Computer-Supported Collaborative Learning) and a derived model together with an architecture for interoperable management of patterns across LD tooling. Contributions have been implemented in LdShake and ILDE community platforms, showing feasibility, enabling proofof-concept in significant scenarios and user studies involving teachers.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".