A Design-Based Research Approach to Bridge Teacher Aspirations and Goal-Setting
Bibliographic record
Abstract
ICTD research has often faced challenges in transferring findings across different projects or general HCI due to its specificity to local communities. Theory-driven research, such as aspirations, can bridge work across communities. However, designing technology for emerging theories like aspirations is inherently complex. This study employs a Design-Based Research (DBR) methodology to explore designing digital tools for teacher aspirations in rural Côte d’Ivoire. First, we interviewed teachers and found the important role of digital literacy in their aspirations. Then, we conducted four focus groups with 16 teachers to understand their weekly planning practices and challenges. We found that teachers rely on collaboration to help set goals and classroom activities, but this collaboration was inconsistent and often inaccessible. Based on this context, we designed a prototype in Google Forms to scaffold planning for teachers and evaluated it through four additional teacher focus groups. Completing a DBR iterative cycle, we reflect on design principles to further develop similar technological supports for teachers in low-infrastructure contexts. The study contributes by demonstrating the value of DBR in designing for teacher aspirations and highlighting the importance of digital literacy, collaboration, and goal-setting in achieving teacher aspirations.
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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 | 0.116 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".