Graduate Students’ Emotions and Achievement in Statistics
Why this work is in the frame
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Bibliographic record
Abstract
Despite the importance of statistics courses for social science students, many find these courses formidable obstacles to the completion of their degrees and report high levels of anxiety about these courses. Understanding the attitudinal factors among students can help instructors improve both students' attitudes toward statistics and their achievement in statistics courses. In this paper, using Pekrun's control-value theory of achievement emotions, we treat emotions as a central component of students' attitudes toward statistics. To investigate the relations between students' attitude components and achievement in statistics, twenty-nine graduate students in a required statistics class completed questionnaires concerning their academic emotions, which were used to determine which emotions had effects on their academic achievement. Results indicate that students' emotions regarding statistics are more complex than simply feeling anxious, and that these graduate students differed in significant ways from undergraduates in how their academic emotions affected their academic engagement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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 it