A Co-Design Process Microanalysis: Stages and Facilitators of an Inquiry-Based and Technology-Enhanced Learning Scenario
Bibliographic record
Abstract
This paper presents a case study of the co-design process for an online course on Sustainable Development (Degree in Tourism) involving the teacher, two students, and the project researchers. The co-design process was founded on an inquiry-based and technology-enhanced model that takes shape in a set of design principles. The research had two main objectives. Firstly, to identify the moments of change that occur during the co-design process and secondly, to describe the causes and agents that motivate them. This study applied design-based research methodology and used a qualitative approach to collect, analyse, and interpret data. The results show that the co-design methodology led to moderate changes consisting of the progressive construction and refinement of the activities, seeking a sense of continuum throughout by including students’ collaboration. The findings also reveal the main role of each of the actors involved. Thus, the teacher focused on basic instructional design related aspects while students focused on improving the use of resources and learning aids. Researchers helped them to embody and achieve the proposed changes and also acted as mediators of pedagogical concepts and vocabulary. CompendiumLD software was a helpful tool to graphically represent and share the prototyping of the activities and to help analyse the design process.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".