Pre-Service Teachers’ Technological Pedagogical Content Knowledge (TPCK) Related to Calculator-Based Laboratory and Contextual Factors Influencing Their TPCK
Bibliographic record
Abstract
The purposes of this study were to determine pre-service physics teachers’ TPCK related to Calculator-Based Laboratory and to examine influences of some contextual factors on their TPCK. This research was based on the transformative model of TPCK that conceptualizes TPCK as a unique body of knowledge. Multiple case study design was used. Both qualitative and quantitative research methods were implemented to collect data. Correlations between TPCK and contextual factors were calculated to seek statistical relationships. The participants of the study were senior pre-service physics teachers. Their knowledge, ability, and practice of TPCK were measured by using various methods including observations, lesson plans, and interviews. More data were collected associated with the participants teaching philosophies and their attitudes towards CBL technology by using individual interviews, reflective journals, and surveys to focus on context related factors. Results of this study conclude that pre-service physics teachers can reflect CBL technology integration skills into their practices more successfully than to their lesson plans. They can behave like an expert while using CBL technology in their teaching. In addition, pre-service physics teachers have high level TPCK related to CBL; hence, they have tendency to use CBL technology as a learning tool and have a coherent knowledge about this technology, pedagogy and content. This study also concludes that instructional philosophy and awareness of CBL technology usage have significant impacts on their TPCK related to CBL. Having student-centered instructional philosophy and awareness of the specific technology integrated into instruction would contribute performing sophisticated TPCK.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".