For the Love of Teaching: The Chronicles of Teachers Handling Students with Special Educational Needs
Bibliographic record
Abstract
Teaching learners with special needs is a very different reality in the profession. This study aimed to determine the lived experiences of teachers who handle learners with special needs. Qualitative-phenomenological design was employed in order to explore their lived experiences. The participants were purposefully chosen based on pre-determined criteria and were interviewed in order to gather the needed data. Results showed that they encountered difficulty in communication and performing adaptive skills, denial of parents on their children’s condition, learners having delayed motor skills, lack of parental support, and the most recurring challenge that emerges is learners’ behavioral problems. With the educational practices that teachers employed that are suitable for the children’s needs, several outcomes were identified specifically increased participation in different activities, improvement of verbal and non-verbal skills, improvement of behavior through simple instructions, increased socialization with their classmates, and learners improved their skills in reading and writing. Furthermore, compassion, enthusiasm, good communication and leadership skills, patience, and resourcefulness and creativity were the skills necessary for teachers to possess in providing the most suitable learning experience to their learners.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".