Integrating the Key Competencies of International Large-Scale Assessment (ILSA) into the Teacher-Education Curriculum in the Philippines
Bibliographic record
Abstract
This study proposes a framework to integrate competencies from various international large-scale assessments (ILSAs) into the program design of a teacher educational institution (TEI) in the Philippines. Using a descriptive-developmental research design, the study examined how ILSA key competencies can be incorporated into the curriculum, along with practices of outcomes-based educational programs at the TEI. Inductive thematic analysis of focus group interviews revealed one overarching program-level theme: using ILSA as a benchmark for curriculum development. At the micro level, four core themes emerged: (1) strengthened content teaching, (2) increased pedagogical content knowledge (PCK) courses, (3) explicit targeting and teaching of ILSA competencies through course-intended learning outcomes (CILOs), and (4) ILSA-like assessments. The study recommends that the Philippine Department of Education (DepEd) and TEIs collaborate to align pre-service teacher training with global literacy standards, ensuring that pre-service teachers are equipped to teach ILSA-recommended skills.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".