Promoting Student Teachers’ Content Related Knowledge in Teaching Systems Thinking: Measuring Effects of an Intervention through Evaluating a Videotaped Lesson
Bibliographic record
Abstract
<p>The understanding of complex, dynamic and animate systems has a special standing in education for sustainable development and biology. Thus one important role of science teacher education is to promote student teachers’ Content Related Knowledge (CRK) for teaching systems thinking, consisting of extensive Content Knowledge (CK) and well formed Pedagogical Content Knowledge (PCK). But the effective development of this knowledge is empirically uncertain. This article reports the effects of three different interventions (technical course, didactic course, mixed course) in promoting student teachers’ reflective Content Related Knowledge for teaching systems thinking. Participants analyzed a videotaped lesson of 6th graders learning about the ecosystem lake and identified weak aspects in the teacher’s CK and PCK. The results revealed that student teachers’ reflective Content Related Knowledge for teaching systems thinking can be promoted in teacher education. The conclusion to be drawn from our findings is that a mixed course with equal technical and didactical input seems to be most effective in fostering student teachers’ reflective Content Related Knowledge for teaching systems thinking.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".