Identifying student group profiles for diagnostic feedback using snap-drift modal learning neural network
Why this work is in the frame
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Bibliographic record
Abstract
The aim of this paper is to propose a novel method for identifying student group profiles based on student responses to a set of multiple choice questions for the purpose of constructing diagnostic feedback using snap-drift modal learning neural network. The proposed method is capable of supporting tutors without the knowledge of machine learning in identifying useful student groups and constructing diagnostic feedback. Trials were conducted and analysis of the result showed that the snap-drift modal learning neural network was able to identify distinct student groups and represented student group profiles were helpful in revealing gaps of understanding and misconceptions that facilitate construction of diagnostic feedback. Moreover, the result showed that all student responses gathered were assigned to their appropriate student group profiles and the diagnostic feedback constructed based on the identified student group profiles had a positive impact on improving the learning performance of the students.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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