Experiencing the needs and challenges of ELLs: Improving knowledge and efficacy of pre-service teachers through the use of a language immersion simulation
Bibliographic record
Abstract
Pre-service teachers need to understand how to support ELLs in their future classrooms, yet evidence suggests that pre-service ELL training may not be as effective as we need it to be. One promising strategy for increasing pre-service teachers’ efficacy and knowledge around teaching ELLs is through a shock-and-show simulation. This strategy incorporates a Swedish-language immersion experience that simulates what it may like to be an ELL and the strategies that can help support these students. There were two phases: a lesson with limited scaffolding (shock) and an extensively scaffolded lesson (show). Our participants included 87 pre-service teachers who filled out pre- and post-surveys, including closed- and open-ended questions. t-Tests were used to determine whether differences in the scores from the two surveys were significant. We analyzed qualitative data using an interpretive approach to the development of codes, categories, and themes, which we triangulated with descriptive statistics to describe the frequency of the emergent codes. Our findings suggest that shock-and-show experiences may benefit pre-service teachers’ knowledge and efficacy around ELL instruction. We theorize that the emotional component of the experience connected to the cognitive aspects may help foster greater learning among pre-service teachers concerning the difficulties and needs of ELLs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".