Structured Controversy: Inquiry-Based Learning in Place of Traditional Group Presentations
Bibliographic record
Abstract
Knowledge is constructed through active and deep learning (Brew, 2003; Fougner, 2012). Inquiry-based learning (IBL) can facilitate active and deep learning, as it is “a self-directed, question-driven search for understanding” that affords students the opportunity to explore a subject and develop central questions through their exploration (Hudspith & Jenkins, 2007, p.9). The purpose of inquiry is to “develop the skills needed to bring research to bear on the understanding of a central question” (p. 10). To this end, Hudspith and Jenkins (2007) have used this teaching method to incorporate group work into the classroom in the Faculties of Social Science and Humanities and the Faculty of Science at Western University in both core courses and special topic interdisciplinary ones. Furthermore, Justice et al. (2007) describe IBL as a process “about discovery and systematically moving from one level of understanding to another, higher level” (p.202).\nStructured controversy is an active learning activity that helps to prepare students for inquiry-based learning. This occurs when students are encouraged to explore a theme (through research) as a member of a group/team who then present or argue against an opposing team’s arguments. Structured controversy works well in a community practice or macro course as a teaching strategy that fosters social action. This active and deep learning activity goes beyond the achievement of learning outcomes from traditional group presentations and “help the student get some background in a particular area, become familiar with disputed issues, and to spark starting points for inquiry” (Hudspith & Jenkins, 2007, p.27). This workshop will provide the instructor with activities used to facilitate a structured controversy and an opportunity to experience this teaching method in order to appreciate the power of this exercise for student learning.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".