teaching-reflector

Teaching reflection triages student evaluation data using thematic inductive coding with prevalence counts to produce prioritized change ideas. It generates an eval_analysis_report.md that transforms raw feedback into actionable improvements while explicitly flagging potential biases.

Evidence-based teaching reflection triangulates mid-course feedback, peer observations, and evaluations to produce durable career artifacts. It aligns your teaching statement with real artifacts, ensuring portfolio claims are ethically supported by actual evidence rather than assertions.

Yes, you can triangulate mid-course feedback and peer observations together to cross-validate evidence. This Skill produces midcourse_findings.md and observation briefs, cross-referencing multiple data sources to identify consistent themes and satisfy triangulation requirements while mitigating biases.

Yes, this tool is explicitly suitable for Scholarship of Teaching and Learning inquiries across university courses. It processes evaluations and evidence to generate portfolio-ready artifacts, satisfying the references protocol required for credible SoTL research design and reporting.

Process your evaluation data through structured reflection to auto-generate a teaching statement aligned with real artifacts. This ensures your pedagogical claims are directly supported by triangulated evidence from mid-course feedback, peer observations, and end-of-term evaluations.

You need compiled student evaluation or feedback data to initiate a structured reflector session. The tool applies inductive coding with prevalence counts to this raw input, requiring you to supply recent evaluation text or mid-course feedback to generate improvement outputs and career artifacts.