The Effectiveness of Pictured Letters Mnemonics Strategy in Learning Similar English Language Letters among Students with Learning Disabilities
Bibliographic record
Abstract
The present study aims to investigate the effectiveness of pictured letters mnemonics strategy in learning similarEnglish language letters among students with learning disabilities in Saudi Arabia according to experimental group(1) and (2), control group, gender, and interaction between them. The study sample comprised (90) students withlearning disabilities who were randomly distributed to three equal groups. While the first experimental group wastaught using the modified letter method, the second experimental group was taught using the method of bothmodified and abstract letters, and the control group was taught in the traditional way. To measure the effectiveness ofthe strategy, the authors applied (pre, post, and follow-up) tests. As a result, two-way ANOVA indicated that therewere significant differences attributed to the teaching method, in favor of the method of both modified and abstractletters, while there were no statistically significant differences due to gender or interaction between the teachingmethod and gender. The same results were obtained from the follow-up test. Accordingly, a set of recommendationswere made.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".