Pedagogical content knowledge in an educational context (PCK-EC)
Bibliographic record
Abstract
The Pedagogical Content Knowledge in an Educational Context (PCK-EC) model is proposed as a framework to support teachers, coaches and researchers in the examination of teacher knowledge within a specific context and with a particular focus. This framework combines the theoretical and practical aspects represented by five dimensions of teachers’ attitudes and teachers’ knowledge (technology, learners’ cognition, subject matter, pedagogy) within an educational context that includes curricular, technological, social, cultural, and teaching - learning contexts. Two case studies were used to examine the utility of the proposed PCK-EC model. Data collected included: semi-structured initial and final interviews; teacher’s journals of reflection (completed after teaching each lesson); direct observations during lessons; observations from video recordings of lessons; transcripts from initial and final interviews; and other collected documents in regards to the educational context. The interpretive repertoires method allowed us to identify and characterize groups of themes in each dimension of teachers’ attitudes and knowledge, and supported inter-relationships between themes. The PCK-EC was useful to support a deep description of a collection of themes by using different sources of data. Analysis of each one of these collections of themes allowed us to understand teachers’ PCK-EC and provided insights about how different technological tools might affect teachers’ attitudes and their knowledge. The dimensions of teachers’ attitudes and knowledge are not isolated, but rather they are inter-related during teaching practice. It is possible to recognize inter-relationships (outgoing and incoming) between themes (within and across dimensions). It is suggested that the frequency of the outgoing and incoming inter-relationships found between themes might give us an average weight for each of the dimensions of the PCK-EC and this could represent teachers’ attitudes and knowledge used during teaching practice. The collection of themes identified might be useful as a tool to support teachers as they explore their attitudes and their knowledge needed for teaching a specific topic with the use of technological tools, and may provide coaches with an effective mechanism to support the identification of an individuals’ PCK and development needs.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".