Undergraduate Students Develop Questioning, Creativity, and Collaboration Skills by Using the Question Formulation Technique
Bibliographic record
Abstract
Asking questions can be one of the most difficult, yet important, steps in driving student inquiry. As post-secondary instructors work to integrate inquiry into classrooms, very few concrete strategies exist for developing and promoting student questioning. The Question Formulation Technique (QFT) is a structured process used widely in K-12 settings that guides students through generating, evaluating, and reflecting on questions. In these contexts, the QFT has been shown to develop important skills, including building creative capacity (divergent and convergent thinking), metacognition, and collaborative teamwork. While we have used QFT in many contexts with undergraduate students, there is very limited evidence of effectiveness and impact beyond K-12. In this study, we explored how students collaboratively developed questions using the Question Formulation Technique (QFT) in an undergraduate upper-division biology class. We documented and explored themes in question generation and collected student group reflections on their experiences. We show that undergraduate students are productive in generating questions using QFT, and asking questions broadly across the course themes, with the number of questions increasing by the end of the term. Students enjoyed the QFT process, were excited by the questions generated, and found the collaborative brainstorming effective. We also reflect on our experiences and provide ideas for other ways QFT can be incorporated into courses to support undergraduate inquiry and research. We found that QFT helps biology undergraduate students ask their own questions and use inquiry to explore concepts creatively and collaboratively.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".