Impact of Data-Driven Feedback and Coaching on Preservice Teachers’ Questioning Skills for Higher-Order Thinking within a Mixed-Reality Simulation Environment
Bibliographic record
Abstract
In this exploratory study, constructs related to self-efficacy theory were embedded in a treatment to improve the use of higher-order thinking (HOT) questioning skills in candidates enrolled in a teaching-methods course enhanced by mixed-reality simulations (MRS). The problem of designing an effective feedback and coaching model to improve the delivery of HOT questions in 10 min lessons was addressed. Thirty undergraduates were asked to incorporate HOT questions into each of the three lessons presented during a 15-week semester. Treatment candidates received individual data-driven feedback and coaching that included tailored guidance provided at regular intervals throughout the term. Quantitative analyses indicated that there was no significant difference in self-efficacy between conditions and that treatment group members posed significantly more HOT questions in their lessons (effect size = 1.26) than their non-treatment peers. An optimal ratio of two knowledge/comprehension to one HOT question in a 10 min period was proposed and three criteria for high-quality HOT questions are presented. Interviews revealed that those who participated in the treatment were more likely to recognize improvements in their self-efficacy, lesson planning, and performance than comparison group members. Data-driven feedback and coaching also provided candidates with opportunities for reflection.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".