Clustering and Visualizing Study State Sequences
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
This paper investigates means to visualize and classify patterns of study of a college math learning environment. We gathered logs of learner interactions with a drill and practice learning environment in college mathematics. Detailed logs of student usage was gathered for four months. Student activity sessions are extracted from the logs and clustered in three categories. Visualization of clusters allows a clear and intuitive interpretation of the activities within the clustered sessions. The three clusters are further used to visualize the global activity of the 69 participating students, which would otherwise be difficult to grasp without such means to extract patterns of use. The results reveal highly distinct patterns. In particular, they reveal an unexpected and substantial amount of navigation through exercises and notes without students actually trying the exercises themselves. This combination of clustering and visualization can prove useful to learning environments designers who need to better understand how their application software are used in practice by learners. 1.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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