Latent profiles of self‐regulated learning and their impacts on teachers’ technology integration
Bibliographic record
Abstract
Abstract Past research shows that both teachers’ technological pedagogical content knowledge (TPACK) and their engagement in metacognitive activities are essential to technology integration in the classroom. However, the interplay between teachers’ TPACK ability and their metacognitive skills is still underexplored, especially in the context of developing technology‐infused lesson plans. This study examined how the interrelations among metacognitive activities and TPACK constructs affected preservice teachers’ technology integration in instructional design. Sixty‐four preservice teachers designed a lesson with nBrowser, a computer‐based learning environment (CBLE) that helps teachers incorporate technology into instruction by promoting self‐regulated learning (SRL). Drawing on the lesson plans, we extracted six types of metacognitive processes preservice teachers exhibited while solving the task and generated two distinct SRL profiles according to the identified latent profile of metacognitive patterns. The competent self‐regulated learners demonstrated more efforts in metacognitive monitoring activities than the less competent self‐regulated learners in regulating their task solving processes. When comparing TPACK comprehension and design performance between the two profiles, the competent self‐regulated learners outperformed the less competent self‐regulated learners on comprehension and design outcomes. This study provides deep insights into teachers’ self‐regulation in CBLEs and emphasizes the pivotal role of metacognition and SRL in teachers’ TPACK development. Practitioner Notes What is already known about this topic Success in technology integration calls for teachers’ conceptual understanding of technological pedagogical content knowledge (TPACK). Teachers’ self‐regulated learning (SRL) ability mediates their TPACK development since metacognitive activities in the SRL process enable teachers to monitor and evaluate TPACK learning towards the sophisticated levels of TPACK. What this paper adds nBrowser, the computer‐based learning environment fosters teachers’ engagement in regulated TPACK development. Analysis of teachers’ lesson plans affords opportunities to identify teachers’ specific metacognitive processes in self‐regulated TPACK development. Latent profile analysis helps to understand the heterogeneity of teachers’ metacognitive processes and establish distinctive profiles regarding teachers’ self‐regulation. Teachers’ TPACK development differed significantly across distinctive SRL profiles. Implications for practice and/or policy Teacher educators should find ways to support teachers’ SRL ability in technology education. The identification of SRL profiles could contribute to designing metacognitive scaffolds in the specific domain of teachers’ technology education.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".