Bibliographic record
Abstract
An approach to course planning in educational programs was presented in Madan and Gnanendran (2020). There, each student's journey through a degree program was viewed as a "project" requiring completion of a set of tasks (courses) with each task having a specific duration (semester) and, possibly, precedence requirements (prerequisites). The institution is expected to offer courses in an appropriate sequence and timing so that students may progress toward graduation efficiently. Given the large number of students that could matriculate every semester, the institution needs to manage myriad individual degree pathways. To make this problem tractable, Madan and Gnanendran (2020) considered cohorts of students, rather than individuals, according to when each entered the program. The institution then only needs to manage a limited number of simultaneous projects with outcomes that are measured on the typical criteria of time and cost. Recently, researchers (e.g., Rigby et. al., 2016) have espoused the "agile" approach over the traditional "waterfall" approach to managing projects in environments where changes to requirements are to be expected, the work can be modularized, and there are avenues to collaborate with end-users. Since degree programs possess all of these characteristics, we propose heuristics based on agile methodology to address course planning and demonstrate their application via numerical examples.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".