Bibliographic record
Abstract
Learning to thrive in a new culture is a cognitively and emotionally demanding task that requires a lifetime of exploration, goal setting, and intentional effort. Lifelong learning is the reality for millions of immigrants in Canada, young and old, who leave behind the familiar to pursue social, economic, and education opportunities in unpaved landscapes. Learning independently in non-learning settings, such as the home, is particularly challenging because learning moments are sporadic, unstructured, and tacit. As a result, immigrants are at heightened risk of experiencing adverse consequences when they do not meet their learning goals, such as missing out on employment opportunities or facing relationship stress. The ill-defined pathways for supporting lifelong learning for immigrants creates barriers to full participation in their new society. In the absence of structural support, alternate possibilities for fostering the informal learning process must be identified. Toward this goal, I investigated two lifelong learning contexts and created tools that serve three generations of immigrants. In my first project I uncovered the dynamics of immigrant grandparent-grandchild storytelling practices, and I designed and evaluated a digital crafting tool that prompts two-way language and culture learning. In my second project I built and deployed a peer-feedback platform for adult immigrants learning to write in English. In my dissertation I integrate these two projects to answer three research questions. First, I have mapped the unique multifaceted learning landscapes faced by immigrants regarding (1) language and culture, (2) age, (3) attitudes, and (4) external structures. Second, I evaluated digital interventions informed by my mapping and showed how manipulating (1) flexibility and structure, and (2) shared spaces can springboard learning moments into meaningful engagement. Third, I reflect on the participatory approaches I adapted to better serve marginalized users. I advance knowledge in Human-Computer Interaction by validating design mechanisms that enrich social learning experiences, and the learning sciences by demonstrating how the interplay of internal psychosocial constructs and external sociocultural context influence the learning process for immigrant populations. Broadly, I contribute a body of interdisciplinary evidence for designing digital tools to capitalize on learning moments that arise from daily routine.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".