Teachers' KSAs in Handling LSENs: Basis for Localized Inclusive Education Program
Bibliographic record
Abstract
The Department of Education has intensified its focus on Learners with Disabilities (LWD) or Learners with Special Education Needs (LSENs) since the introduction of the K-12 curriculum. This study aimed to assess the knowledge, skills, and attitude of teachers in Tanay (LSENs).The study used a descriptive – quantitative method of research using the survey- questionnaire. Conducted during the first quarter of the First Semester, SY 2022-2023, research samples were selected through purposive sampling, the 21 teaching personnel of Tanay Senior High School, served as the respondents. Frequency and percentage distribution were used. Findings reveal that in terms of Knowledge, teachers are Less Knowledgeable on indicators related to the concept of inclusive education; in terms of Skills, teachers’ skills on handling LSENs are verbally interpreted as Less Competent; lastly, with respect to Attitude, teachers find the attitudes stated as Moderately Manifested. Further, there is no significant difference on the knowledge and skills of teachers in handling LSENs in terms of the number of years in service. Likewise, there is a significant difference on the knowledge and attitude of teachers in handling LSENs in terms of the special needs courses/ trainings attended. It is recommended that program on inclusive education be made, likewise be allocated with budget.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".