Lazy or Dyslexic: A Multisensory Approach to Face English Language Learning Difficulties
Bibliographic record
Abstract
An investigation was conducted to help weak academic English learners in a public high school in Colombia, as they seemed to be facing a learning specific difficulty called dyslexia. A focus group of ten students from ninth and tenth grade was the beneficiaries of the design, implementation, and assessment of five multisensory activities to help students decrease their struggles while learning the foreign language (English). For the present action research, five activities were applied during two academic terms (six months) where students were taught verbs, grammar rules, question words, and minimal pairs to help them do better while reading. Outcomes showed that low academic students tend to have a better performance when teachers target multisensory activities to assist them in their learning process related to grammar within the English sessions. Color-coded activities help low achieving students to exercise and remember more easily as senses are engaged while learning, reading exercises are better approached if their workload is split into smaller quantities compare to regular learners.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| 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".