Is Self-Rated Confidence a Predictor for Performance in Programming Comprehension Tasks?
Why this work is in the frame
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Bibliographic record
Abstract
Studies on programming comprehension have focused largely on the type of reading strategies individuals employ. However, quite few programming comprehension studies have focused on the relationship between the self-rated confidence levels and the performance levels of the participants. In this study, our aim was to identify the effect of confidence levels among the participants as they attempt familiar programming questions. Our results indicate that due to familiarity, all participants generally show high confidence levels. High performers demonstrated self-rated high confidence levels as compared to low performers. However, the difference in confidence levels of high and low performers was found non significant. Furthermore, the confidence levels and the performance levels are weakly correlated indicating that confidence levels do not affect the performance levels of this set of participants on the types of questions tested. Moreover, the machine learning algorithms utilized to classify the participants in this study showed potential based on their performance and confidence levels.
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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 | 0.000 | 0.000 |
| 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 | 0.003 |
| 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