Examining the Interactions among Second Language Teachers’ Pedagogical Content Knowledge, Knowledge of Metacognition, and Their Influence on Teaching Metacognition
Bibliographic record
Abstract
Three critical constructs in relation to teachers’ instruction were examined in this study. They are pedagogical content knowledge, knowledge of metacognition, and the teaching of metacognition. Pedagogical content knowledge is domain specific, which includes teachers’ knowledge about the subject and the translation of this knowledge into classroom activities. Knowledge of metacognition, on the other hand, is domain general, refers to the knowledge of identifying and delineating effective usages of general strategies in learning and self-knowledge. The teaching of metacognition refers to creating conditions where learning and fostering of metacognitive skills becomes plausible. This study examined the interactions between these two knowledge domains and their influence on teaching metacognition to second language adult learners in Language Instruction for Newcomers in Canada (LINC) classrooms. Based on Shulman’s (1987) teachers’ knowledge base categories, studies by Gatbonton (2008), Tarnanen (2015), Edwards (2014) explored teachers’ pedagogical content knowledge domain. Following Paris et al.’s (1983) framework, the metacognitive knowledge of teachers was examined in Wilson and Bai’s (2010) study that showed pre-service teachers lack of instructional knowledge in this domain. However, the interaction between these two domains and whether the interaction influence teaching metacognition to adult learners has not been discovered in second language context (i.e., LINC classrooms). To address this research gap, this study examined the three constructs using a qualitative approach. Eight LINC teacher participants answered 15 open-ended questions in 60-minute interviews. The questions were based on deductive categories related to the constructs. The inductive analysis of the data resulted in themes in all three constructs. The results suggest that the distinct knowledge areas (pedagogical knowledge, content knowledge, knowledge of curriculum and knowledge of learners) within LINC teachers’ pedagogical content knowledge are interconnected. The findings also suggest that teachers have better understanding in the declarative knowledge category of metacognition rather than in procedural and conditional categories. This understanding of metacognition is rooted more in practice rather than theory, which amalgamates with pedagogical content knowledge and influences their teaching of metacognition. The conclusion suggests future research in constructing LINC teachers’ explicit knowledge of metacognition (in all knowledge categories) through preservice and in-service teacher education programs.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".