Supporting the Encouragement of Forum Participation
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
We analyze the forum contribution rates during forty offerings of 12 college courses reaching back to 2011. The goal is to identify moments during the quarter when encouragement for passive students might be timely. We started with a social graph model of each forum archive. From those models we computed weighted out degrees, and page ranks for each student. The out degrees reflect the number of posts a student contributes. We performed change point analyses through bootstrap procedures over the CUSUM data of these post contributions through each quarter. We thereby identified significant week by week changes in the rate at which the top ten percent of forum contributors post messages. We hypothesize that such change points might be appropriate encouragement opportunities, and we find that sudden rate shifts do occur along regular patterns, primarily in science and engineering courses. We propose and demonstrate the use of control charts to monitor forum traffic, and show how the historic data can be used to provide personalized encouragement messages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it