The Struggle is Real: An Intervention to Regulate and Resolve Confusion During Complex Statistics Problem Solving
Bibliographic record
Abstract
The purpose of this study was to develop a cognitive-emotive strategy training intervention (CEST) to help university students regulate and resolve confusion during complex statistics problem solving. One hundred sixty-eight university students from Canada, the United States, and England participated. Measures of academic control, epistemic emotions, and confusion regulation strategies were collected. Audio-recordings of the sessions were transcribed and coded to investigate learning and confusion regulation strategies used. Results revealed that the intervention was not effective in helping students better regulate their confusion during problem solving. Students in the intervention group did not increase their perception of control after problem solving, did not increase their learning or confusion regulation strategy use, and did not experience more positive and less negative emotions. Although the intervention had no positive effect, this was the first study to consider emotion regulation skills that are particular to the self-regulated learning processes students must engage to regulate and resolve confusion. To develop more effective interventions, future research should provide more opportunities for students to practice confusion regulation skills over longer periods of time, and ideally to conduct research in natural learning environments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".