Reducing Isolation through Peer-Modeled Posts
Bibliographic record
Abstract
Creating a supportive community in introductory programming courses is vital to student success, yet forums meant to facilitate this can cause stress due to social comparison. According to social identity theory, students are more likely to engage and feel a sense of belonging when they perceive connections with their peers. This study investigates whether peer-modeled posts that simulate students exhibiting desirable engagement behavior can reduce feelings of isolation and foster social connection among students. We introduced curated posts modeling expected student behavior -- covering content, providing emotional support, and offering study tips -- into Q&A forums for two introductory computing courses. These posts were inserted using different student accounts. Surveys and forum data were analyzed to measure the impact on students' feelings of isolation. Students responded positively to the seeded posts, reporting a significant reduction in feelings of isolation. Notably, women reported feeling less isolated after seeing the posts more than men, and many students reported feeling relieved that other students had the same worries and concerns as them. Seeding peer-modeled posts can significantly reduce student isolation and foster a greater sense of belonging in competitive academic contexts. However, future work may explore alternative delivery mechanisms, such as instructor posts framed as ''questions from last year,'' to determine if they can achieve similar effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".