Understanding Teachers’ Cognitive Processes during Online Professional Learning: A Methodological Comparison
Bibliographic record
Abstract
This study examined the effectiveness of three types of think aloud methods for understanding elementary teachers’ cognitive processes as they used a professional development website. A methodology combining a retrospective think aloud procedure with screen capture technology (referred to as the virtual revisit) was compared with concurrent and retrospective think aloud procedures. Elementary teachers from a large metropolitan area were assigned to one of the three think aloud conditions (N = 45). Participants in the concurrent condition verbalized their thoughts while simultaneously navigating a professional development website for 20 minutes. Participants in the retrospective condition verbalized their thoughts following their 20-minute website navigation without any aids. Finally, participants in the virtual revisit condition verbalized their thoughts while viewing a screen recording of their website navigation. Think aloud protocols were analyzed to determine the frequency of cognitive processes verbalized by participants in each condition. The findings of this study indicated significant differences in the types of verbalizations produced by participants across the three think aloud conditions. In addition, findings reveal benefits and limitations of employing each type of think aloud method in the context of a professional development website.
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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 | 0.034 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".