🤖 AI Summary
This study addresses the challenge of effectively guiding students to transition from misconceptions to accurate causal reasoning within a single classroom session, particularly in engineering fault-diagnosis contexts. It proposes a “detective-style scaffolding instructional framework” that reconfigures classroom voting systems into evidence-centered reasoning probes—rather than mere engagement tools—through three stages: hypothesis activation, evidence structuring, and causal integration. The approach exposes how conventional scoring often misjudges reasoning quality and instead employs dual-precision reasoning analysis to assess learning outcomes. In experiments, 80 third-year polymer engineering students increased their correct identification rate of humidity as the root cause from 29% to 100% within 90 minutes; additionally, 26 high school students without engineering backgrounds achieved 100% accuracy on transfer tasks and demonstrated significantly enhanced confidence in data analysis and ability to interpret AI-generated explanations.
📝 Abstract
This paper presents a detective scaffolding framework -- a three-phase instructional sequence (Hypothesis Activation -> Evidence Structuring -> Causal Integration) in which engineering students investigate a realistic industrial defect scenario using staged in-class polls as designed evidence probes. Unlike conventional uses of student response systems for engagement, the framework positions each poll as an Evidence-Centred Design instrument targeting a specific reasoning capability. In the primary implementation, 80 Year~3 polymer engineering students progressed from prior-knowledge-driven misconception (71% attributing defects to temperature) to complete root-cause convergence (100\% identifying humidity; Fisher's exact test, $p < .001$) across four sequenced prompts within a single 90-minute lecture slot. A dual-accuracy analysis revealed that at one intermediate stage, textbook-correct and analytically valid responses diverged, illustrating why conventional scoring can misrepresent reasoning quality. In a transferability study, 26 Year~12 students with no engineering background achieved identical root-cause identification rates across two adapted scenarios, with significant gains in data-analysis confidence and AI explanation ability. The results suggest that the pedagogical structure, rather than disciplinary content, drives the convergence effect, implying portability across disciplines and educational levels.