The Integration of Teacher’s Pedagogical Content Knowledge Components in Teaching Linear Equation
Bibliographic record
Abstract
This qualitative research aimed to explore the integration of the components of pedagogical content knowledge (PCK) in teaching Linear Equation with one unknown. For the purpose of the study, a single local case study with multiple participants was used. The selection of the participants was made based on various criteria: having more than 5 years of experience teaching mathematics and possessing high and low level of knowledge in algebra and high general PCK in mathematics. Six teachers were selected to be the respondents in this research. The data were collected using i) stimulated recall interview, ii) observation, and iii) analysis of documents. Nvivo 8 software was used to help the researcher organize and analyze the data. Kohen Kappa reliability values obtained from three experts were very good, exceeding 0.8. The findings showed that the integration of content knowledge component of pedagogical implications (CàP) is the most frequently used component in the teaching of Linear Equations with one unknown. This indicates that teachers focus mostly on the components of content knowledge and knowledge about students. The teachers were least likely to integrate content knowledge with pedagogical knowledge, which has implications on the knowledge of the students (C&PàS). The study suggests the need to improve teacher’s knowledge through collaborative partnership with colleagues and through courses.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".