Navigating Inclusion: Understanding the Experiences of Non-Special Education Teachers in Teaching Learners with Special Needs
Bibliographic record
Abstract
Promoting inclusive education is on top of the trends in the academe, wherein teachers with or without specialization in special education have to teach or accommodate learners with special needs. This is because of the vigorous proclamation of the Department of Education (DepEd) and Republic Act 11650, which seeks to promote the rights of learners with special needs to acquire the same quality of education as other regular students. This study will primarily use qualitative-phenomenological research to investigate the lived experiences of non-special education teachers who handle and teach learners with special needs. A purposive sampling technique was used to identify the qualified participants for this study. Using the thematic analysis in analyzing the data that have been gathered from in-depth interviews of the participants, several themes and core ideas have been generated from participants responses, which answers the questions regarding experiences, coping mechanisms, and insights that can be shared from non-sped teachers who handle and teaches learners with special needs. Research findings provide significant implications for teachers regarding teaching and dealing with learners with special needs and create an inclusive and supportive learning environment that shows collaboration among stakeholders.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| 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".