education-qualitative-coding-analysis

# Education Qualitative Coding Analysis ## Goal Turn qualitative education data into a transparent coding process, evidence-backed themes, and a findings section. ## Use After Use after data sources and qualitative design are defined: - `education-qualitative-study-design` - `education-mixed-methods-design` - `education-action-research-design` - `education-design-based-research` - `education-policy-comparative-analysis` - `education-program-evaluation` Do not expose the skill name to users. Present it as "质性材料编码分析" or "访谈/观察/文本分析". ## Inputs - Research questions - Data type: interview, focus group, observation, document, policy, student work, AI feedback text, open-ended survey - Corpus description and file list - Chosen qualitative tradition if any: thematic analysis, grounded theory, case study, content analysis, discourse analysis - Whether multiple coders are involved - Language and anonymization requirements ## Workflow 1. Prepare corpus: - transcribe audio/video - clean obvious transcription errors - anonymize names, schools, locations, accounts - assign document and participant IDs 2. Choose analysis approach: - thematic analysis for patterns of meaning - grounded theory for model/theory building - content analysis for categories/frequency - case analysis for bounded cases - discourse-oriented analysis for language/interaction patterns 3. Create initial coding unit: - sentence - paragraph - speaking turn - classroom episode - document section - artifact segment 4. First-cycle coding: - open coding - descriptive coding - in vivo coding - process coding - values/emotion coding where relevant 5. Build codebook: - code name - definition - inclusion/exclusion rules - example quote 6. Second-cycle coding: - axial coding - pattern coding - focused coding - category aggregation 7. Theme/model development: - theme names - theme definitions - relationships among themes - negative cases/contradictions 8. Write analytic memos throughout. 9. Build evidence table linking claims to excerpts. 10. Draft findings structure and discussion implications. ## Tool Calls Dependency policy: inspect the current environment first. Do not install packages, clone repositories, or change the user's environment without explicit approval. Prefer a project-local Python virtual environment or R library, and record package versions used for reproducibility. ### Transcription ```text Whisper: https://github.com/openai/whisper Otter: https://otter.ai/ 讯飞听见: https://www.iflyrec.com/ ``` ### Qualitative Coding Software ```text NVivo: https://lumivero.com/products/nvivo/ MAXQDA: https://www.maxqda.com/ ATLAS.ti: https://atlasti.com/ Dedoose: https://www.dedoose.com/ Taguette: https://www.taguette.org/ QualCoder: https://github.com/ccbogel/QualCoder ``` ### Lightweight Text Analysis R: ```r install.packages(c("tidyverse", "tidytext", "quanteda", "readtext")) ``` Python: ```bash pip install pandas nltk spacy scikit-learn ``` Use NLP only as support. Human interpretation remains required for qualitative analysis. ## Output Format ### 1. Corpus Inventory | Document ID | Data Type | Participant/Source ID | Date | Length | Anonymized | Notes | |---|---|---|---|---|---|---| ### 2. Coding Plan | Decision | Choice | |---|---| | Analysis approach | | | Coding unit | | | First-cycle coding | | | Second-cycle coding | | | Software/tool | | | Multiple coders | Yes/No | | Reliability check | Yes/No/Not applicable | ### 3. Codebook | Code | Definition | Include When | Exclude When | Example Excerpt | Notes | |---|---|---|---|---|---| ### 4. Category / Theme Table | Theme | Related Codes | Meaning | Evidence Count | Representative Excerpts | Negative Cases | |---|---|---|---|---|---| ### 5. Evidence Table | Finding Claim | Supporting Excerpt ID | Excerpt Summary | Data Source | Strength | Caveat | |---|---|---|---|---|---| ### 6. Findings Section Outline ```text Finding 1: [Theme name] - Main claim - Representative evidence - Variation across participants/cases - Negative or boundary case Finding 2: [Theme name] ... ``` ## Approach-Specific Guidance ### Thematic Analysis Use when the goal is to identify patterns of experience, perception, practice, or meaning. Steps: 1. Familiarize with data. 2. Generate initial codes. 3. Search for themes. 4. Review themes against data. 5. Define and name themes. 6. Write findings with evidence. ### Grounded Theory Use when the goal is to build a process model or explanatory theory from data. Steps: 1. Open coding. 2. Constant comparison. 3. Axial coding. 4. Theoretical sampling if possible. 5. Selective coding. 6. Theoretical saturation judgment. 7. Model construction. Output model: ```text Conditions -> Actions/Interactions -> Consequences ``` ### Content Analysis Use when categories and frequencies matter, such as policy texts, textbooks, AI feedback comments, classroom discourse moves, or open-ended survey responses. Steps: 1. Define coding categories. 2. Define coding unit. 3. Code sample and refine categories. 4. Code full corpus. 5. Count and interpret category patterns. 6. Combine frequency with qualitative examples. ## Quality Rules - Do not treat AI-generated codes as final findings. - Every major theme needs multiple pieces of evidence unless it is explicitly a rare/negative case. - Keep raw excerpt, code, category, and theme traceable. - Distinguish participant claim from researcher interpretation. - Include negative cases or contradictions when they matter. - For classroom data, preserve context: lesson phase, activity, teacher/student role, task. - For minor students, anonymize names and identifiable writing details. ## User-Facing Closure End with the next practical decision: ```text 现在可以进入正式编码。你希望我先帮你做一版初始代码本，还是先根据 2-3 份材料做试编码，再修订代码本？ ```