Exploring the Issues in Simulating a Semi-Structured Learning Environment: the SimGrad Doctoral Program Design
Bibliographic record
Abstract
The help seeking and social integration needs of learners in a semi- structured learning environment require specific support. The design and use of educational technology has the potential to meet these needs. One difficulty in the development of such support systems is in their validation because of the length of time required for adequate testing. This paper explores the use of a simulated learning environment and simulated learners as a way of studying de- sign validation issues of such support systems. The semi-structured learning environment we are investigating is a graduate school, with a focus on the doc- toral program. We present a description of the steps we have taken in develop- ing a simulation of a doctoral program. In the process, we illustrate some of the challenges in the design and development of simulated learning environments. Lastly, the expected contributions and our research plans going forward are de- scribed.
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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 | 0.005 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".