Technology-Mediated Collaborative Learning Environments for Young Culturally and Linguistically Diverse Children: Vygotsky Revisited
Bibliographic record
Abstract
Given the instructional challenges posed by the influx of minority-language children in North America, this article attempts to examine early childhood bi- or multilingualism in one of the fastest growing ethnic minority groups in Canada, Korean-Canadians. By drawing on a Vygotskian perspective, the article focuses on the affective and social aspects of learning for culturally and linguistically diverse (CLD) children and their families. With an emphasis on the integration of language and thought, this article first identifies the instructional applications of Vygotskian perspectives and then describes iterative phases of design-based research aimed at developing technology-mediated collaborative learning environments for trilingual Korean-Canadian children. The technology-mediated collaborative learning environment supported young CLD children's affective, social and cognitive needs and created meaning-centered collaborative learning environments. The paper concludes with a consideration of implications of this technology-mediated collaborative learning environment so as to assist teachers who might be confronted with the educational needs of the growing population of CLD children.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".