Modeling student teachers’ self-regulated learning of complex professional knowledge: A sequential and clustering analysis with think-aloud protocols
Bibliographic record
Abstract
There has been much discussion regarding the positive relationship between self-regulated learning (SRL) and technological pedagogical content knowledge (TPACK) development for student teachers. This study continued this claim and adopted advanced analytical methods to explain how SRL influences TPACK learning. Think-aloud protocols from 39 participants were collected and transcribed when they were learning TPACK by designing technology-infused lessons with nBrowser, a computer-based learning environment. Based on models, nine critical SRL events were retrieved from participants‘ think-aloud protocols and analyzed through sequential clustering analysis. The results show two SRL groups indicating distinct self-regulatory sequential patterns. One group had a shorter sequence length and dominantly enacted elaboration activities (Low-SRL group), while the other had longer sequence lengths and engaged in diverse SRL activities (High-regulation group). Relating to TPACK performance indicated by the quality of lesson plans, the results reveal that the participants in the High-SRL group outperformed their counterparts in the Low-SRL group. The findings are consistent with previous evidence and provide implications for practitioners about the importance of student teachers’ self-regulation trajectories.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".