Exploring the Effects of Grouping by Programming Experience in Q&A Forums
Bibliographic record
Abstract
Motivation: Q&A forums are a critical resource for supporting students in large educational environments, yet students often perceive these forums as stressful and report discomfort in participating visibly, especially in classes that are large and have students with varying levels of prior programming experience (PE). Method: We divided students in a CS1 Q&A forum into smaller, homogenous groups based on their PE. We use a mixed-methods approach to compare data from this experience to data from a setting where all students shared a single, large Q&A forum (a “mixed” setting). We quantitatively analyze measures of student engagement and use an open-ended qualitative approach to examine responses about student experience on the forums. This approach helps us identify the motivation behind student decisions to participate in visible or non-visible ways and to evaluate their alignment with theoretical frameworks. Results: In the mixed setting, students frequently use anonymity, with students without PE using anonymity more than students with PE and women using anonymity more than men. In contrast, in the homogenous groups, novices used anonymity less than novices in the mixed setting, while the students in higher-experience groups tended to use it more. We also observe a reduced anonymity usage among women in the homogenous experience groups, suggesting that PE plays a critical role in the observed gender disparities in forum participation. The qualitative analysis provides additional evidence that social status issues and confidence may explain these behavioral patterns. Conclusion: This study highlights the potential benefits and consequences of grouping students by experience. Homogenous PE groups foster increased student comfort and engagement within the Q&A forum for students with less experience, but students with more experience are exposed to more perceived status threats. We discuss how these results align with the theories we used to design the homogenous group setting. This exploration contributes to a deeper understanding of the underlying dynamics shaping student behavior in online learning communities. Educators and platform designers can use these lessons to more effectively create inclusive environments that accommodate diverse student needs and preferences.
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".