Teacher Emotional Support and Academic Outcomes of Migrant Children in Urban China: A Qualitative Study
Bibliographic record
Abstract
As China undergoes rapid urbanization, millions of children from migrant families face challenges integrating into urban education systems. Teacher emotional support plays a potentially significant role in the academic development of these children, yet its specific effects remain underexplored. This study employs grounded theory methodology to examine the perceptions of 5 fifth-grade migrant children in a public primary school in Guangzhou, China. Data were collected through semi-structured interviews and analyzed via open and axial coding. We find that academic-oriented emotional support from teachers, including classroom participation, academic expectations, and engaging teaching strategies, positively influenced children’s motivation and academic performance. Conversely, insufficient emotional availability, uneven attention, and negative emotional expression from teachers hindered student engagement. Many children relied on peers, parents, or technology for support due to limited access to their teachers. The study reveals an imbalance in teacher support, with academic performance prioritized over emotional well-being. Holistic teacher training and educational policy reforms are needed to better support the dual academic and emotional needs of migrant children.
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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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".