Using optimized clustering to identify students' science learning paths to knowledge integration
Bibliographic record
Abstract
Background: This study captured students' repertoire of science ideas and determined the varied paths students take to integrate their disconnected ideas as they studied a web-based Genetic Inheritance unit. Method: We analyzed 6th graders' responses to embedded items and activities to establish progress in knowledge integration in two different learning conditions: revisiting and critiquing. Learning paths were established by measuring students' idea dissimilarities using Levenshtein edit distance, clustering using silhouette coefficient and K-means, and determining the most representative path via generalized median method. Results: Four learning paths emerged from the revisit condition (isolated links, partial links, valid links, integrated links) and three learning paths emerged from the critique condition (isolated links, partial links, and integrated links). Conclusion: We found that by providing opportunities for students to revisit or critique ideas, the curriculum supported them to follow multiple paths in building their repertoire of ideas and integrating initial and new information.
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How this classification was reachedexpand
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 | 0.005 |
| 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 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".