Neuroscience, Linguistics and Psycholinguistics Advances Applied to Early Literacy Teaching
Bibliographic record
Abstract
I discuss the lack of linguistic and psycholinguistic fundamentals compromising the teaching-learning models of early literacy, as well as the ignorance of reading neuroscience most recent contributions, arguing with linguistic and neuroscience theories about perceptual invariant units, like phonemes and graphemes. I also explain the difference between phoneme and sound and between grapheme and letter as well as the existence of hierarchical linguistic levels. All those fundamentals pave the Scliar Early Literacy System (SSA), applied on an experiment run at Lagarto, Sergipe State, on the Brazilian Northeastern, that showed the lowest scores in the 2016 National Literacy Assessment (ANA). METHOD: José Humberto dos Santos Santana, distance SSA Course student, belonging to Lagarto municipal staff, organized the five researchers group to implement the SSA in two Lagarto schools. Teachers Patrícia Vieira Barbosa Faria and Jaqueline da Silva Nascimento were 75 children teaching pioneers, in February, 2017, using SSA, Module 1, method and materials, focusing on reading learning at the municipal schools Raimunda Reis, RR (two classes) and Manoel de Paula Menezes Lima, MPML (one class). On 2018, the same teachers followed the same children in the 2nd grade, applying SSA, Module 2, method and materials, focusing on writing learning. Educators received continuous distance training, first, fortnightly and, starting in 2018, twice every week: Tuesdays, for educators who worked with 2nd grade children and, on Wednesdays, for 1st grade educators, from Elementary School. Distant classes last one hour and a half each. RESULTS: The 2018 More Early Literacy Program assessment describes the lowest level 1, as the one where children barely identify one word or the other. In this level two Lagarto schools dropped to 8.7 (RR) and 9.1% (MPML), while at the highest level, dealing with children who have a desirable reading performance, they reached the percentages of 34.8 (RR) and 31.8 (MPML). Compare such results with the 2016 National Literacy Assessment (ANA) performance in the State of Sergipe: level 1, 45.28; highest level, 3.02. In 2018, Lagarto Municipal Education Secretariat expanded its adhesion to SSA, reaching an average of 490 children from the 1st (18 classes) and 2nd (3 classes) grades of Elementary School, taking into account reading and writing, respectively. The Secretariat guaranteed the continuous training of 18 teachers who attend the 1st year and the 5 who attend the 2nd year for applying the SSA. In 2019, given the proposal success, more than 1000 children benefited from the project.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".