制定数据收集计划

以下数据收集计划旨在在真实教育情境中检验“课堂互动改革对学生参与度的影响”。计划基于多维参与度理论、混合方法设计与多源证据三角互证原则，以确保测量的信度、效度与可推断性（Fredricks, Blumenfeld, & Paris, 2004；Creswell & Plano Clark, 2017）。

一、研究目的与问题
- 目的：评估以互动为导向的课堂改革（如同伴互评、提问—同伴讨论、即时反馈等）对学生参与度（行为、情感、认知维度）的短期与中期影响。
- 研究问题：
  1) 干预是否显著提升学生的多维参与度（总体及分维度）？
  2) 干预效应是否因学科、年级或教师特征而异（跨层差异）？
  3) 课堂互动行为生态（师生—生生互动模式）与学生参与度变化之间的关系为何？

二、研究设计与总体策略
- 设计类型：优先推荐分层随机或阶梯楔形（stepped-wedge）集群设计（以班级/教师为单位），若随机化不可行，则采用匹配的准实验前测—后测对照设计。该设计控制选择偏差，允许分层比较与时点效应估计（Cohen, 1988）。
- 方法范式：收敛并行式混合方法。量化数据用于估计因果效应与大小；质性材料用于阐释机制与情境条件（Creswell & Plano Clark, 2017）。
- 理论框架：多维参与度（行为/情感/认知）与ICAP框架（互动>建构>活动>被动）（Fredricks et al., 2004；Chi & Wylie, 2014）。

三、样本与抽样
- 研究场域：K-12或本科通识/理工课程（依据实际场域选择）。为提高外部效度，跨学科抽样（如数学/语言/科学）并在年级上分层。
- 抽样策略：按学校/年级/学科分层，班级为集群，随机分配至“互动改革先行组/延期组”或与匹配对照班级并行。
- 样本量与功效（简述）：以多层模型为基础估算。在典型校内班级内相关性（ICC≈0.05–0.15）条件下，目标检测中等效应（d≈0.30）时，需≥24个班级，每班≥25名学生以达0.80功效（具体以基线ICC进行事前功效计算；Cohen, 1988）。

四、干预界定与实施保真度
- 干预核心要素（举例）：思—对—议（Think–Pair–Share）、同伴教学/及时投票（点击器或在线投票）、高质量提问与等待时间、结构化小组讨论、形成性评价与即时反馈、以学习任务为中心的互动脚本（对齐ICAP原则；Chi & Wylie, 2014）。
- 保真度（Fidelity of Implementation, FOI）指标：覆盖率（计划活动实施比例）、剂量（每节互动时长/次数）、质量（互动深度/追问质量）、一致性（与培训方案的吻合度）。采用教师日志、课堂观察核查表与访谈三角互证（Century, Rudnick, & Freeman, 2010）。

五、数据来源与测量工具（多源三角互证）
1) 学生自陈量表（多维参与度）
   - 学生参与度量表（SEI；Appleton, Christenson, Kim, & Reschly, 2006）：强调认知与心理参与（适合中学与高中）。
   - 课堂参与/疏离量表（Engagement vs. Disaffection；Skinner, Kindermann, & Furrer, 2009）：测量行为与情感参与/疏离。
   - 高教场域可补充NSSE相关分量表以捕捉课堂参与与协作学习（Kuh, 2009）。
   - 本地化与试测：采用翻译—回译程序，进行认知访谈与验证性因素分析（CFA），检验量表的结构效度与跨时测量等值性（目标α≥0.70；RMSEA≤0.08，CFI≥0.90）。

2) 课堂观察（课堂互动行为生态）
   - COPUS用于量化师生活动分布（适于本科STEM；Smith, Jones, Gilbert, & Wieman, 2013）。
   - CLASS用于师生互动质量与情感/组织/教学支持（K-3或相应版本；Pianta, La Paro, & Hamre, 2008）。
   - ICAP行为编码：对学习活动进行P/A/C/I层级标注（Chi & Wylie, 2014）。
   - 观察抽样：系统取样覆盖每班至少3次（基线/中期/后测），每次全节课；训练观察员并建立操作手册。目标观察者一致性：Cohen’s κ或ICC≥0.70（Shrout & Fleiss, 1979）。

3) 行为与过程数据（客观行为指标）
   - 出勤、作业按时提交率、课堂发言/提问次数（由观察记录或课程管理系统统计）。
   - 学习分析数据：LMS点击流、资源访问、帖子与回复数量、停留时长等（对齐隐私规范；Long & Siemens, 2011）。
   - 碎片化时间抽样（momentary time-sampling）记录学生在任务上的即时状态（Altmann, 1974）。

4) 瞬时体验抽样（ESM）
   - 通过课堂内简短推送（1–3题）测量当下投入、兴趣与认知努力（每周1–2次，低负担），用于捕捉波动态势（Hektner, Schmidt, & Csikszentmihalyi, 2007）。

5) 质性资料
   - 学生焦点小组与半结构访谈：探查互动体验、心理安全感、挑战—支持平衡与阻碍因素。
   - 教师访谈与反思日志：记录教学决策、实施张力与情境制约，辅助解释量化结果。

6) 背景协变量
   - 学生层面：基线学业成绩、性别/年龄、先前学习动机指标等。
   - 班级/教师层面：班级规模、学科、教师年资/专业发展经历。

六、测量时点与流程
- T0 基线（第0–2周）：量表（SEI/EvsD）、课堂观察（1次/班）、背景协变量、LMS初始数据抓取。
- 干预实施（第3–12周或一学期）：每班至少2次课堂观察；ESM每周1–2次；行为与LMS数据连续采集；教师日志每周一次；保真度核查每月一次。
- T1 后测（学期末）：量表复测、课堂观察（1次/班）、教师与学生访谈。
- T2 跟踪（+6–8周，可选）：量表短表复测与关键行为指标复取，用于检测持续性。
- 阶梯楔形设计下，按批次推进班级进入干预，保证每班均有前后对照期。

七、质量控制与信度/效度保障
- 工具试测与本地化：小样本先导研究（n≈60–100）检验条目理解与量表结构；根据CFA/IRT结果微调条目。
- 观察员培训：使用带标注视频进行校准，达到κ/ICC阈值后方可上岗；定期复训以防漂移。
- 三角互证：自陈—观察—行为/日志—ESM—访谈交叉验证，降低共同方法偏差。
- 数据完整性：建立缺失数据监测仪表板，课堂与在线收集并行以降低缺失；记录缺失原因以支持后续处理。

八、伦理与数据管理
- 伦理：遵循《贝尔蒙报告》与本地IRB/伦理委员会要求，获取知情同意/监护人同意与学生同意；未成年人数据最小可识别化处理；可随时退出且不影响学业（The Belmont Report, 1979）。
- 隐私与安全：数据去标识化、分级存取、加密存储；敏感数据单独密钥管理。
- 数据管理与共享：制定代码本、变量字典与版本控制；遵循FAIR原则进行元数据标注与合规共享（Wilkinson et al., 2016）；在OSF预注册研究假设、主要结果与数据收集方案，记录方案偏离。

九、偏差与威胁及缓解
- 选择偏差：随机/分层分配；若准实验则进行倾向评分匹配与协变量控制。
- 霍桑效应/观察者效应：延长观察期、掩蔽观察目的（在伦理许可范围内）、采用非侵入式日志。
- 教师效应/同伴污染：以班级为单位分配并最小化教师跨组教学；在模型中加入教师随机效应。
- 共同方法偏差：跨方法测量同一构念、错开收集时点。
- 实施异质性：引入FOI指标并进行亚组分析或剂量—反应探索（Century et al., 2010）。

十、时间表（一学期示例）
- 第-4至-1周：工具本地化与试测、观察员培训、功效与随机化实施、伦理审批与家校沟通。
- 第1–2周：基线收集（T0）。
- 第3–12周：干预与持续收集（观察/ESM/LMS/日志/FOI）。
- 第13–14周：后测（T1）与访谈。
- 第+6–8周：跟踪测量（T2，可选）。
- 全期：数据质控、缺失追踪、保真度核查与过程记录。

十一、可行性与负担控制
- 降低负担：量表采用短表与分次施测；ESM条目精简；观察安排与教学周历对齐。
- 反馈与激励：向教师与学校提供课堂互动画像与改进建议，以换取时间配合与数据接入。
- 设备与平台：采用现有LMS与手机端微问卷平台，减少额外部署。

参考文献（APA第7版）
- Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the Student Engagement Instrument. Journal of School Psychology, 44(5), 427–445.
- Century, J., Rudnick, M., & Freeman, C. (2010). A framework for measuring fidelity of implementation: A foundation for shared language and accumulation of knowledge. American Journal of Evaluation, 31(2), 199–218.
- Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
- Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE.
- Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109.
- Hektner, J. M., Schmidt, J. A., & Csikszentmihalyi, M. (2007). Experience sampling method: Measuring the quality of everyday life. SAGE.
- Kuh, G. D. (2009). The National Survey of Student Engagement: Conceptual and empirical foundations. New Directions for Institutional Research, 2009(141), 5–20.
- Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.
- Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom Assessment Scoring System (CLASS) manual, K–3. Paul H. Brookes.
- Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.
- Skinner, E. A., Kindermann, T. A., & Furrer, C. J. (2009). A motivational perspective on engagement and disaffection: Conceptualization and assessment of children’s behavioral and emotional participation in academic activities in the classroom. Journal of Educational Psychology, 101(4), 765–781.
- Smith, M. K., Jones, F. H. M., Gilbert, S. L., & Wieman, C. E. (2013). The Classroom Observation Protocol for Undergraduate STEM (COPUS): A new tool for characterizing university STEM classroom practices. CBE—Life Sciences Education, 12(4), 618–627.
- The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report.
- Wilkinson, M. D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.

该计划以多源数据、分层设计与实施保真度跟踪为核心，既能捕捉课堂互动改革对参与度的平均效应，也能揭示情境差异与机制路径，确保研究结论的可信度与可应用性。

Data Collection Plan: The Effect of Blended Learning on Academic Self-Efficacy

1. Purpose and Research Questions
- Purpose: To estimate the causal effect of blended learning (BL) on students’ academic self-efficacy (ASE) and to document implementation fidelity and contextual moderators.
- Primary question: Does participation in a well-specified BL course increase ASE relative to traditional face-to-face instruction?
- Secondary questions:
  - Do effects vary by baseline ASE, prior achievement, discipline, or gender?
  - Are BL effects mediated by mastery experiences and instructional presence?
  - How do implementation fidelity and engagement relate to ASE change?

Rationale: BL may enhance ASE by increasing mastery opportunities, feedback, and learner control (Bandura, 1997; Graham, 2006; Means et al., 2013).

2. Design Overview
- Preferred design: Cluster randomized controlled trial (cRCT) at the course-section level to minimize contamination (randomize sections to BL vs. business-as-usual face-to-face).
- Alternative (if randomization not feasible): Quasi-experimental matched comparison with propensity score methods and difference-in-differences using baseline ASE, prior achievement, demographics, and prior online experience as covariates (Murnane & Willett principles of causal inference; see also WWC standards).
- Mixed-methods concurrent design: Quantitative (surveys, LMS logs, administrative data) with qualitative interviews/focus groups for triangulation and explanatory depth (Creswell & Plano Clark, 2017).

3. Setting, Sampling, and Participants
- Setting: Multiple undergraduate gateway courses (e.g., introductory psychology, biology, statistics) across 2–4 institutions to enhance generalizability (Graham, 2006).
- Units:
  - Clusters: Course sections (target 40–80 sections total, balanced across arms and disciplines).
  - Students: All enrolled students in selected sections; anticipated n per section = 25–40.
  - Instructors: Faculty teaching participating sections.
- Inclusion criteria: Degree-seeking undergraduates enrolled at census; instructors willing to implement the specified BL model or the standard face-to-face format.
- Exclusion criteria: Sections taught by graduate assistants without training support; courses with existing extensive online components in the control arm.

4. Intervention and Comparison (for Fidelity Anchoring)
- BL treatment: Pre-specified blended model with defined dosage (e.g., 30–50% online), structured weekly online modules (readings, quizzes, discussion), and in-person active learning sessions. Alignment documented via a BL design template and a checklist derived from established blended/course quality frameworks (e.g., Community of Inquiry presence indicators; Arbaugh et al., 2008; Graham et al., 2013).
- Control: Business-as-usual face-to-face delivery without systematic online components beyond standard LMS posting.

5. Outcome Measures
Primary outcome: Academic self-efficacy
- Instrument: MSLQ Self-Efficacy for Learning and Performance subscale (8 items; 1–7 Likert) administered at baseline (T0), midterm (T1), and end of term (T2) (Pintrich et al., 1991; Pintrich et al., 1993).
- Evidence: Consistently high internal consistency for college samples (typically α ≈ .90) and demonstrated predictive validity for achievement (Pintrich et al., 1993).

Secondary/auxiliary measures (for mechanism, covariate adjustment, and sensitivity)
- Sources of self-efficacy (mastery experiences, vicarious experiences, social persuasion, physiological states), adapted from domain-appropriate scales following Bandura’s construction guidelines (Bandura, 2006; Usher & Pajares, 2009).
- Teaching, social, and cognitive presence (CoI survey; Arbaugh et al., 2008) to capture instructional/learning environment characteristics associated with BL.
- Prior achievement: Cumulative GPA or standardized placement scores; baseline course diagnostic if available.
- Engagement/effort: LMS activity metrics (time-on-task, resource views, assignment submission patterns) and brief self-report engagement scale.
- Demographics: Age, gender, major/discipline, first-generation status, prior online course experience.

6. Implementation Fidelity and Exposure
- Instructor-reported adherence: Weekly implementation logs aligned to the BL design template (dosage of online components, use of active learning).
- Independent observations: Structured observations of in-person sessions using a validated active-learning checklist.
- LMS analytics: Actual BL dosage (e.g., completion of online modules, discussion participation, quiz attempts).
- Fidelity rubric: Global fidelity scores synthesized from logs, observations, and analytics (O’Donnell, 2008).

7. Timing and Procedures
- Pre-semester (T−1): Instructor recruitment; randomization at section level; training for BL instructors; pilot testing of instruments; LMS instrumentation.
- Week 1–2 (T0): Baseline student survey (ASE, demographics, prior online experience), consent, and retrieval of prior GPA. Randomization concealed from analysts; treatment known to implementers.
- Midterm (T1): Short ASE assessment, CoI survey, and engagement check to examine trajectories and reduce common method bias via temporal separation (Podsakoff et al., 2003).
- End of term (T2): Posttest ASE, CoI, course grade collection; instructor fidelity summaries; LMS data export.
- Postterm (T3, optional): Follow-up ASE 6–8 weeks later to assess persistence.
- Qualitative sampling: Purposive subsample (e.g., n ≈ 20–30 students per condition across disciplines; 8–12 instructors) for semi-structured interviews near T2 to explain quantitative patterns (Creswell & Plano Clark, 2017).

8. Data Quality Assurance
- Pilot: Cognitive interviews with 8–12 students to ensure clarity and contextual fit of items; small pilot (n ≈ 60–80) to examine reliability and preliminary factor structure.
- Measurement equivalence: Test measurement invariance (configural, metric, scalar) for the MSLQ ASE subscale across conditions and time points before estimating effects (Putnick & Bornstein, 2016).
- Administration controls: Uniform survey windows, standardized reminders, proctoring in class where feasible to maximize response rates; incentives (e.g., small course credit or raffle) approved by IRB.
- Nonresponse management: Track response propensity; implement targeted reminders; document reasons for attrition.

9. Sample Size and Power Guidance
- Plan for a cRCT with students nested in sections. Use established software (e.g., Optimal Design) with plausible assumptions: small effect size (d = 0.20–0.25), intraclass correlation at section level ICC ≈ .03–.07 for psychosocial outcomes, covariate R2 ≈ .40 from baseline ASE and GPA. Aim for at least 30–40 sections per arm with 25–35 students each to achieve ≈ .80 power for small effects (Hedges & Rhoads, 2010; Raudenbush & Bryk, 2002). Final targets should be set via a formal power analysis with local ICC estimates.

10. Data Management and Security
- Unique study IDs linking surveys, LMS logs, and grades; no storage of direct identifiers with analytic datasets.
- Secure, encrypted storage on institutional servers; access controls; audit logs.
- Codebook, metadata, and version control (e.g., Git with restricted access).
- Pre-registration of hypotheses, primary outcomes, and analysis plan (e.g., OSF) before data collection.
- Data sharing: De-identified datasets and instruments archived following FAIR principles when permitted (Wilkinson et al., 2016).

11. Ethical Considerations
- IRB approval; informed consent specifying voluntary participation, data uses, and withdrawal rights.
- FERPA-compliant handling of educational records; minimal-risk classification anticipated.
- Mitigation of risks: Anonymized reporting; instructor training to ensure equitable learning opportunities in both arms.

12. Minimizing Bias and Ensuring Internal Validity
- Randomization by an independent coordinator; blocked by course and instructor to balance prior outcomes.
- Baseline measures to adjust for any residual imbalance.
- Analyst blinding to treatment during data cleaning and pre-specification of models.
- Multiple data sources (surveys, logs, grades) and temporal separation to reduce common method variance (Podsakoff et al., 2003).
- Monitoring protocol deviations and documenting contamination.

13. Analysis-Linked Data Needs (to inform collection)
- Primary impact: Change in ASE (T2 minus T0) estimated with multilevel modeling (students nested in sections), adjusting for baseline ASE and covariates (Raudenbush & Bryk, 2002).
- Moderation: Interaction terms for baseline ASE, discipline, gender.
- Mediation (exploratory): Mastery experiences and teaching presence measured at T1 and T2 (Bandura, 1997; Arbaugh et al., 2008).
- Sensitivity: Per-protocol and complier-average causal effect analyses using fidelity indices; missing data handled via FIML or multiple imputation under MAR assumptions (Enders, 2010).

14. Reporting and Documentation
- Document recruitment, participation flow, response rates, and attrition by arm (CONSORT for cRCTs adapted to education).
- Report reliability (Cronbach’s alpha, omega), CFA/invariance results, and fidelity statistics.
- Provide a transparent account of deviations from the pre-registered plan and robustness checks.

References
- Arbaugh, J. B., Cleveland-Innes, M., Diaz, S. R., Garrison, D. R., Ice, P., Richardson, J. C., & Swan, K. P. (2008). Developing a community of inquiry instrument: Testing a measure of the Community of Inquiry framework using a multi-institutional sample. The Internet and Higher Education, 11(3–4), 133–136.
- Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
- Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 307–337). Information Age.
- Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE.
- Enders, C. K. (2010). Applied missing data analysis. Guilford Press.
- Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk & C. R. Graham (Eds.), The handbook of blended learning (pp. 3–21). Pfeiffer.
- Graham, C. R., Woodfield, W., & Harrison, J. B. (2013). A framework for institutional adoption and implementation of blended learning in higher education. The Internet and Higher Education, 18, 4–14.
- Hedges, L. V., & Rhoads, C. (2010). Statistical power analysis in education research (NCEE 2010-4017). U.S. Department of Education.
- Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1–47.
- O’Donnell, C. L. (2008). Defining, conceptualizing, and measuring fidelity of implementation and its relationship to outcomes. Review of Educational Research, 78(1), 33–84.
- Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). University of Michigan.
- Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the MSLQ. Educational and Psychological Measurement, 53(3), 801–813.
- Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research. Journal of Applied Psychology, 88(5), 879–903.
- Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance in the social and behavioral sciences. Child Development Perspectives, 10(1), 29–34.
- Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models (2nd ed.). SAGE.
- Usher, E. L., & Pajares, F. (2009). Sources of self-efficacy in mathematics: A validation study. Contemporary Educational Psychology, 34(1), 89–101.
- Wilkinson, M. D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.

Note: If a quasi-experimental design is required, add a data element for teacher/course matching variables (e.g., historical grading distributions, class size, instructor experience) to strengthen the propensity model and include a clear overlap/diagnostics protocol before estimating effects.

以下为“教师培训对教学质量的前后测评估”之数据收集计划。方案以因果推断与测量严谨性为核心，强调多源数据、标准化工具、信度效度保障与实施忠实度监测，以确保对培训影响的估计准确、可复现与可推广。

一、研究目的与评估框架
- 研究目的：评估特定教师培训项目对课堂教学质量的影响，并探究影响机制（如培训实施忠实度、教师自我效能感变化）与异质性效应（年资、学科、学段）。
- 核心假设：与对照组相比，接受培训的教师在课堂教学质量观察评分、学生感知教学质量、与培训内容相关的教师知识/技能测验上表现更优，并在随访期保持或扩大效果；学生学习投入与学业成绩存在正向但相对较小的效应。
- 评估原则：三角互证（课堂观察+学生调查+客观作业/测评+教师自陈）、多时点测量（基线/后测/随访）、实施过程数据并行采集（忠实度与质量）。

二、研究设计与样本
- 设计类型：
  1) 优先方案：集群随机对照试验（学校或教研组为单位），T0基线测，培训后T1短期测（培训结束后2–4周）、T2中期随访（3–4个月）、T3远期随访（6–9个月）。
  2) 次优方案：准实验差异中的差异（DiD），基线等值检验并应用倾向评分加权/匹配与协变量调整。
- 样本与分层：覆盖不同学段与学科，按学段×学科×地区分层抽样，保证外部效度；每校抽取目标教师全体或随机抽取；学生调查与成绩数据来自所教班级。
- 规模与功效（示例，需据真实参数精算）：以教师观察评分为主结果、期望标准化效应d≈0.30、显著性α=0.05、检验效能80%、两层结构（教师嵌套学校，学校内相关ICC≈0.05–0.10），若每校10名目标教师，则每组需约10–15所学校（总教师数约200–300）更为稳健；建议使用PowerUpR或Optimal Design按实际ICC、变异度和失访率进行功效分析（Spybrook等, 2011）。
- 基线等值：按WWC规范检验标准化差异；若0.05<|SMD|≤0.25，后续模型需协变量调整（What Works Clearinghouse, 2020）。

三、指标体系与测量工具
A. 主要结果（课堂教学质量）
- 课堂观察量表（择一为主、可辅以第二量表做稳健性检验）：
  1) CLASS（学前、小学、初高中版本）：情感支持、课堂组织、教学支持维度，适合跨学段评估（Pianta等）。
  2) Danielson《教学框架》：计划与准备、课堂环境、教学、专业责任（Danielson, 2013）。
  3) 若培训聚焦学科教学，可选学科专向量表，如数学教学质量（MQI）（Hill等）。
- 观察设计：每名教师每时点采集2–3节常态化课堂视频（每节35–45分钟），两名独立评分员盲评；至少20%样本双评，定期漂移校准；采用多面向Rasch或一般化理论分解误差并进行评分者严厉度修正（Shavelson & Webb, 1991）。

B. 次要结果
- 学生感知教学质量：标准化学生问卷（如Tripod 7Cs或同类经验证问卷），班级层面聚合分数；每班至少20名学生问卷以提升信度（MET项目证据；Kane等）。
- 学生学习结果：与课程一致的标准化测验或高质量课程基测/期末分；如使用自编测验，需设置锚题实现前后等值并进行项目反应理论（IRT）标定。
- 教师知识与技能（与培训内容对齐）：如学科教学法/内容知识测验；采用既有经验证题库或自编并进行专家审查、试测、信度分析与等值。
- 教师自我效能感：教师教学效能量表TSES（长/短版）（Tschannen-Moran & Hoy, 2001），经跨文化适配与测量等值检验。
- 教学产出与作业质量：收集教案、作业与评阅样本，采用经验证评分规准（如IQA框架）进行盲评。
- 学生行为性投入：短式课堂投入观察清单或经验采样（在有条件时）。

C. 实施过程与忠实度
- 指标维度：到课率/时长（剂量）、活动完成度（依从/一致性）、培训者素质与互动质量、实践反馈与同伴交流强度（参照Desimone, 2009；Century等, 2010）。
- 工具：签到与时长记录、培训者与教练日志、抽样旁听评分表、教师反思札记与作业完成度量表、访谈/焦点小组。

四、数据收集流程与时间安排
- T0基线（培训前2–4周）：
  1) 教师：课堂视频2–3节；知识/技能测验；TSES；基本背景（年资、最高学历、职称、班额、课时量等）。
  2) 学生：感知教学质量问卷；可用的最近一次标准化学业数据或组织基线测。
  3) 学校：资源与管理特征（规模、师生比、教学常规、地区属性）。
- 培训实施期：过程数据滚动采集（每次活动签到、日志、随堂质性记录）；教练/督导定期记录与抽检。
- T1后测（培训结束后2–4周）：
  1) 教师：课堂视频2–3节；知识/技能测验平行版或等值卷；TSES。
  2) 学生：感知教学质量问卷；单元或期末标准化测验。
  3) 实施忠实度：完整汇总并质控。
- T2/T3随访（3–4月/6–9月）：同T1的缩减版，至少包含课堂视频与学生问卷，优先获取标准化成绩并评估维持效应。
- 日程与现场组织：避免考试周与节假日；随机排列拍摄顺序；统一摄录规范（机位、收音、取景）；建立异常事件报告与替代采集预案。

五、质量控制与信度效度保障
- 工具选择与跨文化适配：优先采用经验证量表；若需翻译，采用“翻译—回译—专家评审—认知访谈—小样本试测”的流程（Beaton等, 2000），并开展验证性因素分析与测量等值检验（跨时点与组别）。
- 评分员培训与校准：
  1) 培训天数与合格标准：依据量表官方要求（如CLASS需认证）；考核通过方可上岗。
  2) 盲评与漂移控制：评分员对组别与时点双盲；每月进行共评分与阈值校准；计算ICC/加权Kappa，目标≥0.70。
  3) 一般化理论/多面向Rasch：估计任务数、评分员数与时长对信度的贡献，优化采样设计以达G系数≥0.70。
- 测验质量：
  1) 前后测等值：设置锚题；用IRT或同等化方法链接分数，控制天花板/地板效应。
  2) 内部一致性：Cronbach’s α或ω≥0.70；条目反应特征检查与偏差项处理。
- 学生调查实施：
  1) 统一监考与电子化施测；最小化社会期许偏差与教师干预。
  2) 班级层面聚合并报告设计效应；对小样本班级设最小样本阈值（建议≥15–20）。
- 盲法与偏差控制：观察与评分全程盲法；对照组提供延迟培训以提高参与同意率；预防霍桑效应与补偿性竞争。
- 数据完整性与缺失：
  1) 监控关键KPI（视频完成率、双评比例、问卷有效率、测验缺失率）。
  2) 设立追补与替代采样机制；记录缺失原因；分析阶段使用多重插补并报告敏感性分析。

六、数据管理与伦理合规
- 审批与同意：通过机构伦理审查；获取教师与学生家长（或监护人）知情同意/同意豁免；对视频与问卷用途、保存期限、去标识化与撤回权利进行明示。
- 隐私与安全：分离身份信息与研究数据；视频与PII加密存储、限权访问；建立数据保留与销毁计划；遵循《教育和心理测量标准》(AERA/APA/NCME, 2014)。
- 数据治理：编制数据字典与版本控制；双人复核关键变量；日志记录数据变更；预设异常值与逻辑校验规则。

七、预注册与分析准备（与数据收集直接相关的要点）
- 预注册：在OSF登记研究与前分析计划，明确定义主次结果、时点、排除准则与协变量集。
- 统计框架（概述）：多层模型（学生/课堂/教师/学校），对基线值与关键协变量调整；组别×时点交互项为主效应；聚类稳健标准误；在有评分者的结果中使用多面向Rasch或多层模型控制评分者效应。
- 异质性与机制：预先定义分层（年资、学科、基线水平、学校特征）；以实施忠实度作为中介/调节进行机制探索。
- 报告规范：遵循WWC与透明报告原则，公开材料与（去标识化）数据字典。

八、关键操作细节清单（供现场执行对照）
- 课堂视频：每位教师每时点2–3节；两名盲评评分员；≥20%双评；每月漂移校准一次。
- 学生调查：同一周内完成；每班≥20份有效问卷为目标；统一施测说明。
- 测验：平行卷或IRT等值；锚题比例建议≥20%；设回收与补测窗口。
- 忠实度：每次培训全程签到（时长到分钟）、内容覆盖核对表、教练/讲师日志、抽样旁听评分；形成每位教师的剂量与质量指数组合。
- 质量阈值：观察评分ICC≥0.70；问卷α≥0.80（总分）；测验信息函数覆盖目标能力区间；缺失率控制在<10%并记录原因。

参考文献（示例，按APA风格）
- American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing.
- Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186–3191.
- Century, J., Rudnick, M., & Freeman, C. (2010). A framework for measuring fidelity of implementation. American Journal of Evaluation, 31(2), 199–218.
- Danielson, C. (2013). The framework for teaching evaluation instrument. Danielson Group.
- Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educational Researcher, 38(3), 181–199.
- Kane, T. J., McCaffrey, D. F., Miller, T., & Staiger, D. O. (2013). Have we identified effective teachers? Bill & Melinda Gates Foundation MET Project.
- Kraft, M. A., Blazar, D., & Hogan, D. (2018). The effect of teacher coaching on instruction and achievement. Review of Educational Research, 88(4), 547–588.
- Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom Assessment Scoring System (CLASS) manual. Paul H. Brookes.
- Shavelson, R. J., & Webb, N. M. (1991). Generalizability theory: A primer. SAGE.
- Spybrook, J., et al. (2011). Optimal design for longitudinal and multilevel research. In S. H. Cole (Ed.), The Oxford handbook of quantitative methods.
- Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783–805.
- What Works Clearinghouse. (2020). Procedures and standards handbook (Version 4.1/5.0). Institute of Education Sciences.

注：具体工具的中文版本与使用授权需在项目启动前确认；若进行本土化适配，须完成系统的翻译、等值与信效度检验流程，并在预注册中明确主结果的操作化定义与计分方案。上述采样规模、ICC与效应量为常见取值的示意，实际研究应基于先导研究与现地参数进行功效与设计优化。