Date
Publisher
arXiv
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.
Study at a glance
- Question: Can a three-phase detective framework using staged polling guide students' reasoning and transfer across educational levels?
- Setting and data: 80 Year 3 undergraduates in a UK–China engineering program and 26 Year 12 UK secondary students in February 2026.
- Method: Mixed methods evaluating staged in-class polls, pre/post surveys, participant reflection, and student-created artifacts.
Why it matters
Active learning evaluations often rely on endpoint grades, hiding how student reasoning develops during learning. Additionally, investigation activities rarely scale to large lectures or test whether the teaching structure transfers across different age groups.
Key findings
- Undergraduate teams completely shifted from an initial misconception (71% selecting temperature) to 100% identifying humidity as a contributing factor across four staged polls.
- All pre-university teams with no engineering background successfully identified the correct root cause across two adapted investigation scenarios.
- Pre-university participants reported significant increases in their confidence to explain AI and their comfort with data and patterns, each rising by 1.1 Likert points.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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