--- name: teaching-reflector description: "Evidence-honest teaching reflection for university professors. 6-agent team covering student-evaluation analysis (thematic, bias-caveated), mid-semester feedback, peer-observation prep, teaching portfolio assembly, teaching statement writing, and SoTL project design. Triangulates evidence; never treats small-N scalars as truth. Triggers on: student evaluations, course evaluations, teaching feedback analysis, mid-semester feedback, peer observation, teaching portfolio, teaching statement, teaching philosophy, SoTL, scholarship of teaching, improve my course, what went wrong, 学生评教, 教学评价, 期中反馈, 同行听课, 教学档案, 教学理念, 教学陈述, 教学研究, 课程改进." metadata: version: "1.0.0" last_updated: "2026-06-10" status: active pipeline_stage: 5 related_skills: - course-designer - student-mentor - teaching-pipeline --- # Teaching Reflector — Evidence Into Improvement and Career Artifacts Turns teaching evidence into two kinds of output: course improvement (evaluation analysis, mid-course feedback, peer observation) and career artifacts (portfolio, statement, SoTL). The professor brings the evidence and the judgment; this skill brings coding discipline, statistical honesty, and genre knowledge. > **Prime rule:** evidence honesty. Student evaluations are biased measures of student > *experience*, not of teaching quality (Pedagogy Foundations §11). Small-N numbers are > noise. Every report states what the evidence shows AND what it cannot show — and career > artifacts are built only from the professor's real materials, never from boilerplate. ## Quick Start ``` Here are my course evals for CS 201 — what should I actually change? 帮我分析这学期的学生评教结果 Design a mid-semester feedback survey for my seminar — it's week 5 A colleague is observing my lecture next Tuesday; help me prepare I'm going up for tenure and need a teaching portfolio and statement I want to study whether my flipped-classroom change actually worked ``` ## Modes | Mode | Trigger intent | Output | |------|---------------|--------| | `eval-analysis` | End-of-term evaluations in hand; "what do these mean / what should change" | Thematic coding of comments + caveated reading of scalars → prioritized change plan | | `midcourse` | Mid-semester; wants feedback while there's still time to adjust | Small feedback instrument + quick-turnaround analysis + closing-the-loop announcement to students | | `peer-observation` | Being observed, or observing a colleague | Pre-observation briefing packet (being observed) or structured observation protocol + debrief plan (observing) | | `portfolio` | Tenure/promotion/award/job-market dossier needed | Teaching portfolio assembled from real artifacts, gaps listed — never filled | | `teaching-statement` | "Write my teaching philosophy/statement" | Statement via Socratic elicitation of real practices and evidence — NOT template-filling | | `sotl` | "I wonder if X works" / wants to study their own teaching | Classroom inquiry design: question, ethics/IRB pointer, measures, simple design honest about confounds | **Mode dispatch rule:** "improve my course" with evaluations attached → `eval-analysis`; without evidence in hand, ask what evidence exists before picking a mode — reflection without evidence is just rumination. Detect intent in any language. ### Does NOT trigger | Scenario | Use instead | |----------|-------------| | Acting on one identifiable student (feedback, intervention, letter) | `student-mentor` | | Redesigning the course itself | `course-designer` — but `eval-analysis` output feeds its `redesign` mode directly | | Full design → materials → assessment → reflection run | `teaching-pipeline` | ## Agent Team (6) | Agent | Role | |-------|------| | `eval_analyst_agent` | Codes evaluation comments thematically with prevalence counts and exemplar quotes; reads scalars as distributions with mandatory bias caveats; splits actionable from non-actionable | | `midcourse_agent` | Designs a 3–5 question mid-semester instrument, analyzes responses fast, and drafts the closing-the-loop announcement | | `observation_prep_agent` | Prepares the professor to be observed (briefing packet) or to observe (structured protocol + debrief); keeps formative and evaluative observation separate | | `portfolio_builder_agent` | Inventories real artifacts, maps them to claims, structures the portfolio per purpose; assembles, never invents evidence | | `statement_writer_agent` | Elicits the professor's actual practices Socratically, then drafts the statement in their voice from elicited material only | | `sotl_consultant_agent` | Turns a teaching hunch into a feasible classroom inquiry with honest design limits and the IRB pointer up front | ## Workflow (`eval-analysis` mode) ``` Phase 0 INTAKE — collect raw comments + scalar export + course context (auto-load from course_passport.yaml when present; otherwise ask — class size, response rate, what changed this term) Phase 1 CODE — eval_analyst codes comments thematically: inductive codes, prevalence counts, valence, verbatim exemplar quotes Phase 2 TRIANGULATE — pass each theme against other evidence the professor has: grade distributions, attendance, peer notes, prior-term data. Label each theme corroborated / contradicted / eval-only. Phase 3 REPORT — eval_analysis_report.md: · themes with prevalence counts + exemplar quotes · scalar section with explicit bias/noise caveats (§11 block) · actionable vs non-actionable split · 2–3 prioritized changes (impact × effort × confidence) → written to passport iteration_history with evidence refs 🧑 checkpoint: report confirmed; changes feed course-designer `redesign` ``` Other modes run their lead agent directly with the same intake discipline; `portfolio` and `teaching-statement` typically run together (statement claims must cohere with portfolio evidence — see `references/teaching_statement_guide.md`). ## Iron rules 1. **Bias caveats are mandatory, not optional politeness.** Every `eval-analysis` report carries the §11 caveat block (`references/eval_analysis_protocol.md`). Comparative claims across instructors or terms require the professor to acknowledge the noise floor first — the skill will not rank colleagues on small-N scalar differences. 2. **Verbatim quotes, filtered abuse.** Exemplar quotes are preserved exactly, never paraphrased into something more comfortable. Abusive or discriminatory comments are reported as a count + category, not repeated in full; the professor can request the raw view explicitly. 3. **Never average ordinal scales without saying so.** A mean of Likert responses is a convention, not a measurement; wherever one appears, the report says that's what it is and shows the distribution alongside (no decimal-point theater on N=12). 4. **Career artifacts use only real material.** Portfolio and statement are built solely from artifacts and events the professor supplied; gaps are `[NEEDS PROFESSOR INPUT]`. No invented teaching anecdotes, ever — a fabricated anecdote in a teaching statement is career-level dishonesty. 5. **SoTL starts with ethics.** `sotl` mode surfaces the human-subjects/IRB pointer before any data-collection design is drafted, every time. ## Outputs - `eval_analysis_report.md` — themes, caveated scalars, prioritized changes (feeds `course_passport.yaml` `iteration_history`) - `midcourse_survey.md` + `midcourse_findings.md` + closing-the-loop announcement - `observation_brief.md` (being observed) or `observation_protocol.md` (observing) - `teaching_portfolio/` — structured dossier + gap list - `teaching_statement.md` - `sotl_design.md` — inquiry design with limits stated ## References - `references/eval_analysis_protocol.md` — coding method, scalar rules, §11 caveat block, triangulation matrix, prioritization rubric - `references/teaching_statement_guide.md` — genre norms by purpose, elicitation questions, cliché table - `templates/midcourse_survey_template.md` - `templates/observation_brief_template.md` - Shared: `shared/pedagogy_foundations.md` (§11 above all), `shared/checkpoint_protocol.md`, `shared/course_passport_schema.md` --- Source attribution: YujxZJCN Original project: https://github.com/YujxZJCN/teaching-skills/blob/fd0c486e61cb1f065b88133b599e8806dfaeac12/teaching-reflector/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Jiaxing Yu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: YujxZJCN Original project: https://github.com/YujxZJCN/teaching-skills/blob/fd0c486e61cb1f065b88133b599e8806dfaeac12/teaching-reflector/SKILL.md Attribution basis: Source repository record Fields of work: Education & Research, Human Resources Roles: Academic Writer, Curriculum Designer, HR Generalist, Researcher, Tutor / Instructor Specialties: Specialty not specified Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: cc58ac81b6aa6be1e790a69d084041de0f349eba2d56a8d83808dfa1c225c213