Inclusion of Students with Borderline Cognitive Impairment in Secondary Schools: Challenges and Coping Strategies
Bibliographic record
Abstract
Background: The challenges of managing students with borderline cognitive impairment in an inclusive classroom are enormous. Therefore, there is a need for appropriate coping strategies to foster the successful inclusion of students with borderline cognitive impairment in the day-to-day classroom activities. Aim: This study examines the challenges facing the inclusion of students with Borderline Cognitive Impairment (BCI) and the coping strategies often adopted by these students in the Ogoja Education Zone of Cross River State, Nigeria. Method: The study adopted a descriptive survey design. One hundred and sixty-nine students with BCI in twenty (2) regular secondary schools were selected, using the purposive sampling technique. The instrument for the study was a questionnaire titled "Academic Challenges and Coping Mechanism of Students with Borderline Cognitive Impairment Questionnaire (ACCMSBCIQ 0.67). Results: The study revealed that inclusive education for students with BCI at the secondary school level in the Ogoja Education Zone of Cross River State is hindered by a complex web of interconnected challenges. The study also revealed that students with BCI adopt several coping strategies to remain included in the secondary education program in the study area. Recommendation: Based on the findings of the study, the researchers recommend that the government and other stakeholders should organize adequate training on the inclusion of students with BCI for all secondary school teachers in Cross River State
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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 | 0.003 |
| 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) | 0.001 | 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".