Innovative Digital Tools for Enhancing Phonemic Awareness among Learners with Mild Cognitive Impairment in Calabar Education Zone of Cross River State, Nigeria
Bibliographic record
Abstract
Learners with mild cognitive impairment (MCI) often struggle with reading comprehension, especially auditory discrimination, memory retention, and linguistic processing, which hinders their ability to acquire foundational reading skills through conventional instructional approaches. This pretest-posttest quasi-experimental study investigated the effect of graphic organizers as an instruction strategy on reading comprehension among pupils with MCI in public primary schools within Calabar Education Zone, Cross River State. 80 pupils with MCI were purposively selected. Analysis of Covariance (ANCOVA) and Multiple Classification Analysis (MCA) were used to test the three hypotheses. Results indicated that graphic organizers significantly improved reading comprehension for pupils with MCI (F-ratio = 85.329, p < 0.05), while gender had no significant effect on the outcomes (F-ratio = 0.344, p > 0.05). A significant interaction effect of graphic organizers on reading comprehension improvements was also observed (F-ratio = 0.305, p < 0.05). The findings underscore the necessity of training workshops for educators on the use of graphic organizers and advocate for gender balance in grouping pupils with MCI for such interventions, highlighting the effectiveness of graphic organizers as tools for enhancing reading comprehension in pupils with MCI.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".