Student-Generated Questions Fostering Sustainable and Productive Knowledge Building Discourse
Bibliographic record
Abstract
The role of questions in student learning is well recognized. However, the controversial issue of who should pose questions that direct inquiry continues: teachers or students? One perspective advocates that teachers generate questions as it assumes that students cannot generate high-quality questions. In contrast, Knowledge Building, a pedagogical approach that advocates transforming schools into knowledge-creation organizations, emphasizes student agency in generating authentic questions as they try to understand the world around them. This study examined the extent to which elementary students could generate questions and explore how student-generated questions help knowledge-building discourse progress. Comparing question threads (i.e., a series of online notes started with questions) and non-question threads (i.e., a series of online notes not started with questions), we noticed that questions posted by students engaged them in sustainable and progressive discourses, which is central to Knowledge Building. Moreover, the content analysis of the data revealed that the threads starting with questions were more likely to end up with productive threads than the non-question threads.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".