Evaluating Student Anxiety as a Predictor of Stem Performance Using Storytelling and Machine Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Anxiety can be a formidable barrier in STEM. Incorporating anxiety-informed pedagogy presents a potential approach to overcome this challenge, such as ‘Breadboardia,’ a graphical storybook to learn electronics. In this paper, machine learning (ML) was employed to predict students’ STEM performance based on their self-scored anxiety levels. Throughout, ML concepts are introduced to encourage their adoption in educational research. The instrument was administered to 200 high-school girls prior, immediately thereafter, and two weeks following exposure to ‘Breadboardia.’ A sentiment analysis was also performed to evaluate student perceptions of the storybook. Preliminary results suggest that the participants remained largely neutral towards the storybook, independent of their performance on technical questions. Initial prediction model results are promising, with average errors of 1.5% on training data and 23.1% on testing data. These models elucidate how ML techniques can be leveraged in engineering pedagogy and inform the development of targeted interventions that enhance 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 | 0.004 | 0.002 |
| 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 | 0.000 |
| 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