The Effects of Implementing the Strategy of Semantic Feature Analysis (SFA) in Promoting Vocabulary in School-Aged Portuguese Children in Inclusive Schools
Bibliographic record
Abstract
Background: The purpose of this study was to apply and analyze the impact of the semantic feature analysis (SFA) strategy on vocabulary development and comprehension of texts and theoretical concepts in Portuguese school-age students with and without special educational needs (SEN) attending inclusive schools. Method: The research design was quasi-experimental. The SFA was administered in ten sessions of approximately 60 min each. The sample was a convenience sample and consisted of selecting three classes in each school: (i) in the first cycle of basic education, 65 students were divided into a control group, an experimental group and a structured teaching group; (ii) in the second cycle of basic education, 55 students were divided into an experimental group, an online virtual school and a control group. Results: (1) The SFA strategy is motivating, appealing, inexpensive, flexible and easy to implement; (2) learning the SFA strategy is easy and can be successfully taught in any classroom; (3) the performance of the students assigned to the experimental groups was significantly higher in both cycles compared to all the other groups; (4) the effect sizes were 0.87 in the first cycle and 0.88 in the second cycle. Conclusion: The SFA strategy effectively promotes the development of vocabulary, concept knowledge and text comprehension in school-age children, being more effective than regular teaching.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".