Discovering Process in Curriculum Data to Provide Recommendation.
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
Process mining is an emerging technique that can discover the real sequence of various activities from an event log, compare different processes and ultimately find the bottleneck of an existing process and hence improve it. Curriculum data is the history of the courses effectively taken by students. It is essentially process-centric. Applying process mining on curriculum data provides a means to compare cohorts of students, successful and less successful, and presents an opportunity to adjust the requirements for the curriculum by applying enhancement of process mining. This can lead to building recommenders for courses to students based on expected outcome. In this paper we first discover a process model of students taking courses, then, compare the paths that successful and less successful students tend to take and highlight discrepancies between them. The conclusion we reached is that process mining indeed has a great potential to assist teachers and administrators to understand students behavior, to recommend the correct path to students, and at last to enhance the design of a curriculum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| 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