From Dissimilar to Similar: Reverse Fading Assistance Improves Learning
Why this work is in the frame
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Bibliographic record
Abstract
This thesis investigated how worked examples could be used to fade assistance in the domain of algebra. The method of fading assistance was novel. It used similarity as the mode of assistance, with similar problem-example pairs providing high assistance and dissimilar pairs providing low assistance. Learning, performance, and gaze behaviours captured by an eye tracker were analyzed across three conditions -a faded assistance condition, a constant assistance condition, and a reverse faded assistance condition. Participant's personality traits and attitudes and behaviours regarding math were also collected and correlated with eye gaze sequences. We found that, contrary to our hypothesis, the reverse faded assistance condition resulted in the greatest learning gains. We analyzed the gaze behaviours to shed light on this finding and found that participants in this condition focused significantly more on the problem solution, suggesting more cognitive processing during problem solving than in the other conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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