制定研究目标建议

以下研究目标面向研究生与博士生的在线学习与绩效议题，强调机制识别、因果推断与可操作的改进路径，并以权威证据为依据：

1) 阐明研究生/博士生在在线环境中的自我调节学习策略（如时间管理、元认知监控、寻求帮助）对学业绩效（课程成绩、综合考试与开题通过、按期进度）的影响机制，并检验社区临在框架中的教学、社会与认知临在在其中的中介作用及同步/异步学习条件的调节作用（Broadbent & Poon, 2015；Garrison, Anderson, & Archer, 2000；Richardson, Maeda, Lv, & Caskurlu, 2017）。

2) 评估高质量互动设计与导师制安排对研究生/博士生在线学习投入与绩效的因果效应，比较不同互动强度与形式（如高结构化同侪评阅、定期同步研讨、导师一对一反馈与快速反馈机制）在课程成绩、留存率与学术里程碑达成上的差异性效果，为项目层面的教学设计优化提供证据（Bernard et al., 2009；Means, Toyama, Murphy, Bakia, & Jones, 2010）。

3) 构建并跨项目验证面向研究生/博士生的学习分析早预警模型，整合学习管理系统行为数据与互动网络指标，检验模型的预测效度、可迁移性与公平性，并评估个性化干预（如学术支持与导师提醒）对后续绩效与留存的实际增益，确保在伦理与隐私原则下实施（Gašević, Dawson, & Jovanović, 2016；Slade & Prinsloo, 2013）。

参考文献（APA 第7版）：
- Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R., Surkes, M., & Bethel, E. (2009). A meta-analysis of three types of interaction treatments in distance education. Review of Educational Research, 79(3), 1243–1289.
- Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education: A systematic review. The Internet and Higher Education, 27, 1–13.
- Gašević, D., Dawson, S., & Jovanović, J. (2016). Ethics and privacy as enablers of learning analytics. Computers & Education, 100, 16–26. [注：该文亦讨论情境差异对预测模型与实施的影响。]
- Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2–3), 87–105.
- Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. U.S. Department of Education.
- Richardson, J. C., Maeda, Y., Lv, J., & Caskurlu, S. (2017). Social presence in relation to students’ satisfaction and learning in the online environment: A meta-analysis. Computers in Human Behavior, 71, 402–417.
- Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. British Journal of Educational Technology, 46(3), 662–677.

以下研究目标面向“新课改”情境中教师协同与学校支持的关系机制与改进路径，强调可测量的概念界定、基于证据的推断与可推广的改进知识生成。

1) 描述与测量目标：界定并量化新课改背景下教师协同与学校支持的关键维度，刻画其分布与共变模式
- 明确教师协同的结构与质量（如共同备课、观课议课与同伴反馈、跨学科项目化协作、以数据为依据的教学对话），以及学校支持要素（教学领导、制度性时间与资源保障、专业学习共同体结构、评价激励与学习文化）（OECD, 2019；Bryk et al., 2010）。
- 建立测量工具与指标体系，描述不同学段与学校类型的差异，并估计学校支持与协同质量的相关强度（Louis et al., 2010；OECD, 2019）。
- 目标产出：经验证的量表、结构模型与基准性描述，为后续因果检验与干预设计提供依据。

2) 机制与效应目标：检验“学校支持→教师协同质量→教学改进→学生学习”的因果链，并识别调节与异质性
- 通过纵向与多层数据，估计学校支持影响协同质量，协同质量进一步影响教学改进与学生学习结果的中介效应；同时检验领导实践与学校专业环境对该路径的调节作用（Ronfeldt et al., 2015；Kraft & Papay, 2014；Vescio et al., 2008）。
- 优先采用多层结构方程模型、差分—差分、断点或工具变量等准实验策略以增强因果识别，并报告效应异质性与稳健性（Bryk et al., 2010）。
- 目标产出：可复核的因果估计与机制证据，明确哪些支持要素通过提升协同质量带来教学与学习成效。

3) 设计与改进目标：共创并评估提升教师协同与学校支持效能的改进策略，形成可推广的实施知识
- 与学校共同设计并迭代优化支持机制（如时间表重构保障协作、数据会议协议、基于实践的同伴观课反馈流程、分布式领导与角色分担），评估实施保真度、机制运作与成本—效果（Cobb et al., 2003；Fixsen et al., 2005；Bryk, Gomez, Grunow, & LeMahieu, 2015）。
- 采用设计型研究与改进科学方法，结合混合方法与网络分析，识别在不同学校情境中的有效适配与扩散条件（Hargreaves & Fullan, 2012；Bryk et al., 2015）。
- 目标产出：情境化的改进包、实施指南与扩散策略，为新课改落地提供可操作的系统性支持。

参考文献
- Bryk, A. S., Gomez, L. M., Grunow, A., & LeMahieu, P. G. (2015). Learning to improve: How America’s schools can get better at getting better. Harvard Education Press.
- Bryk, A. S., Sebring, P. B., Allensworth, E., Luppescu, S., & Easton, J. Q. (2010). Organizing schools for improvement: Lessons from Chicago. University of Chicago Press.
- Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13.
- Fixsen, D. L., Naoom, S. F., Blase, K. A., Friedman, R. M., & Wallace, F. (2005). Implementation research: A synthesis of the literature. University of South Florida.
- Hargreaves, A., & Fullan, M. (2012). Professional capital: Transforming teaching in every school. Teachers College Press.
- Kraft, M. A., & Papay, J. P. (2014). Can professional environments in schools promote teacher development? Explaining heterogeneity in returns to teaching experience. Educational Evaluation and Policy Analysis, 36(4), 476–500.
- Louis, K. S., Leithwood, K., Wahlstrom, K. L., & Anderson, S. E. (2010). Learning from leadership: Investigating the links to improved student learning. University of Minnesota & University of Toronto.
- OECD. (2019). TALIS 2018 results (Volume I): Teachers and school leaders as lifelong learners. OECD Publishing.
- Ronfeldt, M., Farmer, S. O., McQueen, K., & Grissom, J. A. (2015). Teacher collaboration in instructional teams and student achievement. American Educational Research Journal, 52(3), 475–514.
- Vescio, V., Ross, D., & Adams, A. (2008). A review of research on the impact of professional learning communities on teaching practice and student learning. Teaching and Teacher Education, 24(1), 80–91.

Research objectives for an evaluation and ethical design study of Project-Based Learning (PBL):

1) Build and validate a multidimensional, fair assessment framework for PBL
- Specify and model core constructs that PBL aims to develop (e.g., disciplinary understanding, inquiry and problem-solving, collaboration, metacognition, and civic/real-world application) using evidence-centered design and construct maps. Develop performance tasks and rubrics aligned to these constructs and to contemporary assessment standards, gathering a validity argument that integrates content, internal structure, relations to other variables, consequences of use, and fairness. Establish score reliability and comparability in authentic contexts, employing generalizability theory and rater calibration; test measurement invariance and differential item functioning across student subgroups to ensure equity.
- Key sources: AERA, APA, & NCME, 2014; Kane, 2013; Messick, 1995; Mislevy, Steinberg, & Almond, 2003; Pellegrino, Chudowsky, & Glaser, 2001; Shavelson & Webb, 1991.

2) Estimate the causal and distributional impacts of PBL assessment practices on learning, motivation, and equity
- Using rigorous quasi-experimental or experimental designs (e.g., cluster randomized trials, difference-in-differences, and multilevel modeling), identify the effects of ethically designed PBL assessments on student achievement, engagement/motivation, and collaborative competencies. Examine heterogeneity of effects by prior achievement, language background, socioeconomic status, and race/ethnicity, and assess whether the assessments narrow or widen opportunity and outcome gaps. Investigate consequences of assessment use (intended and unintended) and align task design with culturally relevant pedagogy and Universal Design for Learning to improve accessibility and reduce construct-irrelevant variance.
- Key sources: Condliffe et al., 2017; Duke, Halvorsen, Strachan, Kim, & Konstantopoulos, 2020; Ladson-Billings, 1995; CAST, 2018; Messick, 1995.

3) Co-design and govern ethically responsible PBL assessment systems with stakeholders
- Employ design-based research/DBIR and participatory co-design with teachers, students, families, and leaders to iteratively refine assessment tasks, rubrics, feedback practices, and any analytic tools. Embed ethical safeguards—transparency, data minimization, privacy-by-design, informed consent/assent, and learner agency—in data collection and reporting. Establish governance structures for data access, algorithmic auditing (if analytics are used), and accountability that comply with applicable regulations and professional ethics. Produce practical guidelines and decision tools for schools to implement PBL assessment ethically at scale.
- Key sources: AERA, 2011; The Design-Based Research Collective, 2003; Penuel, Fishman, Cheng, & Sabelli, 2011; Slade & Prinsloo, 2013; Sclater, 2016; European Union, 2016/679 (GDPR); FERPA (20 U.S.C. §1232g; 34 CFR Part 99).

References
- AERA, APA, & NCME. (2014). Standards for educational and psychological testing. American Educational Research Association.
- AERA. (2011). Code of ethics. American Educational Research Association.
- CAST. (2018). Universal Design for Learning guidelines version 2.2. CAST.
- Condliffe, B., Quint, J., Visher, M., Bangser, M. R., Drohojowska, S., Saco, L., & Nelson, E. (2017). Project-based learning: A literature review. MDRC.
- The Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8.
- Duke, N. K., Halvorsen, A.-L., Strachan, S. L., Kim, J., & Konstantopoulos, S. (2020). Putting PBL to the test: The impact of project-based learning on second graders’ social studies and literacy learning and motivation. AERA Open, 6(3), 1–17.
- European Union. (2016). Regulation (EU) 2016/679 (General Data Protection Regulation).
- Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50(1), 1–73.
- Ladson-Billings, G. (1995). Toward a theory of culturally relevant pedagogy. American Educational Research Journal, 32(3), 465–491.
- Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry. American Psychologist, 50(9), 741–749.
- Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003). On the structure of educational assessments. Measurement: Interdisciplinary Research and Perspectives, 1(1), 3–62.
- Penuel, W. R., Fishman, B. J., Cheng, B. H., & Sabelli, N. (2011). Organizing research and development at the intersection of learning, implementation, and design. Yearbook of the National Society for the Study of Education, 110(2), 463–480.
- Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. National Academies Press.
- Sclater, N. (2016). Developing a code of practice for learning analytics. Journal of Learning Analytics, 3(1), 16–42.
- Shavelson, R. J., & Webb, N. M. (1991). Generalizability theory: A primer. SAGE.
- Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.
- U.S. Department of Education. Family Educational Rights and Privacy Act (FERPA), 20 U.S.C. §1232g; 34 CFR Part 99.