课程描述撰写指南

课程名称：课程设计与评估

课程定位与宗旨 本课程系统性地引介基于证据的课程设计与评估范式，强调以“目标—教学—评估”同向对齐（constructive alignment）和“逆向设计”为核心框架，指导学习者制定可测量的学习成果、设计有效的教学活动与评估方案，并通过数据与证据持续改进课程质量（Biggs & Tang, 2011；Wiggins & McTighe, 2005）。课程内容整合学习目标分类学、有效性与信度、形成性评估与反馈、通用学习设计（UDL）与真实情境评估等关键概念，旨在培养学习者在多元教育情境中开展高质量课程开发与评估的能力（Anderson & Krathwohl, 2001；Messick, 1995；Meyer, Rose, & Gordon, 2014）。

适用对象与先修要求

• 适用对象：高等教育教师与教学支持人员、中小学教研员、企业/职业教育培训开发者、教育技术与课程开发相关人员。
• 先修要求：具备基础教育学或教学法知识与教学实践经验，能够阅读专业英文文献。

学习目标（完成课程后，学员将能够）

• 进行基于证据的学习需求分析，制定与修订可观察、可测量的学习成果，并据此选择适切的认知层级与表现标准（Anderson & Krathwohl, 2001）。
• 采用逆向设计组织课程结构，确保学习目标、教学活动与评估任务之间的一致性与可追溯性（Wiggins & McTighe, 2005；Biggs & Tang, 2011）。
• 设计包含形成性与总结性、诊断性与真实性的综合评估方案，兼顾效度、信度与公平性，并能据此优化教学决策（Messick, 1995；Gulikers, Bastiaens, & Kirschner, 2004）。
• 构建高质量评分量规与反馈策略，促进自我调节学习与学习成效提升（Brookhart, 2013；Nicol & Macfarlane-Dick, 2006；Hattie & Timperley, 2007）。
• 将通用学习设计原则融入课程与评估，提升可及性、公平性与学习者参与度（Meyer et al., 2014）。
• 运用学习证据（作业表现、量规评分、学习分析数据）评估并改进课程质量，形成持续改进闭环（Suskie, 2018；Palomba & Banta, 2015；Siemens & Long, 2011）。

核心内容与模块安排

• 模块1：学习成果与同向对齐
  • 可测量学习目标的表述与层级，成果导向与证据链构建（Biggs & Tang, 2011；Anderson & Krathwohl, 2001）。
• 模块2：逆向设计与课程蓝图
  • 从“可接受证据”出发组织教学内容与活动，形成课程蓝图与对齐矩阵（Wiggins & McTighe, 2005）。
• 模块3：评估的效度、信度与公平
  • 评估推论的验证逻辑、评分一致性、偏差与公平性审视；证据三角检验（Messick, 1995；AERA, APA, & NCME, 2014）。
• 模块4：形成性评估与高效反馈
  • 形成性评估策略、同伴评审与自评、反馈的时机与特征（Black & Wiliam, 1998；Hattie & Timperley, 2007；Brookhart, 2013）。
• 模块5：真实情境与包容性评估
  • 真实性维度、任务设计与量规匹配；UDL在任务、材料与评估中的应用（Gulikers et al., 2004；Meyer et al., 2014）。
• 模块6：课程成效评估与持续改进
  • 证据收集与分析、学习分析的应用、项目级改进与质量保障（Suskie, 2018；Palomba & Banta, 2015；Siemens & Long, 2011）。

教学与学习策略

• 设计工作坊与案例研讨：围绕真实课程情境开展问题驱动的设计与迭代，提高迁移性与实作能力（Wiggins & McTighe, 2005）。
• 同伴评审与设计评析：利用结构化量规进行双向反馈，提升判断与反思质量（Brookhart, 2013；Nicol & Macfarlane-Dick, 2006）。
• 主动学习与微型教学：通过任务驱动与示范实践强化学习者参与与成效（Freeman et al., 2014）。
• 证据为本的反思：基于学习证据与数据的反思日志和改进计划（Suskie, 2018）。

课程评估与评分方式（针对学员）

• 课程设计组合包（Portfolio）（40%）：包含需求分析、学习成果、对齐矩阵与课程蓝图。
• 评估工具开发（30%）：至少一个真实性任务与对应量规，附效度证据与评分一致性方案。
• 微型教学与形成性反馈实践（20%）：实施并解析反馈策略，提交数据与反思。
• 反思报告（10%）：基于证据的课程改进计划。 评分基于公开量规，覆盖目标达成度、证据质量、设计一致性与公平性考量（AERA et al., 2014；Brookhart, 2013）。

学术伦理与公平性 课程遵循教育与心理测量标准，要求对学习者差异提供合理便利，保护数据隐私，避免评估偏倚，并就AI与学术诚信设定明确规范（AERA et al., 2014；Meyer et al., 2014）。

课程成效评估与持续改进（针对本课程）

• 多元证据：学习成品质量、量规评分分布、同伴与自评数据、学习体验调查与访谈。
• 数据利用：对关键产出进行评分一致性检验与主题分析，形成具体改进措施，并在后续轮次监测效果（Palomba & Banta, 2015；Suskie, 2018）。

主要学习资源（建议）

• 教材与专著：Biggs & Tang (2011)；Wiggins & McTighe (2005)；Anderson & Krathwohl (2001)；Suskie (2018)；Palomba & Banta (2015)；Brookhart (2013)；Meyer et al. (2014)。
• 研究与综述：Black & Wiliam (1998)；Hattie & Timperley (2007)；Gulikers et al. (2004)；Nicol & Macfarlane-Dick (2006)；Freeman et al. (2014)；Siemens & Long (2011)。

参考文献

• AERA, APA, & NCME. (2014). Standards for educational and psychological testing. American Educational Research Association.
• Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
• Biggs, J., & Tang, C. (2011). Teaching for quality learning at university (4th ed.). Open University Press.
• Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74.
• Brookhart, S. M. (2013). How to create and use rubrics for formative assessment and grading. ASCD.
• Freeman, S., et al. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415.
• Gulikers, J. T. M., Bastiaens, T. J., & Kirschner, P. A. (2004). A five-dimensional framework for authentic assessment. Educational Technology Research and Development, 52(3), 67–86.
• Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.
• Meyer, A., Rose, D. H., & Gordon, D. (2014). Universal design for learning: Theory and practice. CAST Professional Publishing.
• Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218.
• Palomba, C. A., & Banta, T. W. (2015). Assessment essentials: Planning, implementing, and improving assessment in higher education (2nd ed.). Jossey-Bass.
• Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.
• Suskie, L. (2018). Assessing student learning: A common sense guide (3rd ed.). Jossey-Bass.
• Wiggins, G., & McTighe, J. (2005). Understanding by design (Expanded 2nd ed.). ASCD.

课程简述总结 本课程以同向对齐与逆向设计为主轴，融合效度与信度、形成性评估与高质量反馈、UDL与真实性评估等关键理念，通过工作坊式设计实践与证据驱动的反思，支持学习者产出可实施、可评估、可改进的课程与评估方案，促进教学决策的科学化与教育质量的持续提升。

Course Title: Data Visualization

Catalog Description This graduate-level course provides a rigorous, research-informed foundation for designing, implementing, and evaluating data visualizations for analysis and communication. Grounded in empirical evidence on graphical perception and visual encoding effectiveness, the course develops proficiency in mapping data, tasks, and audiences to appropriate representations and interaction techniques (Cleveland & McGill, 1984; Mackinlay, 1986; Ware, 2021). Students learn a principled design process spanning problem characterization, data and task abstraction, visual encoding and interaction design, prototype implementation, and empirical evaluation (Munzner, 2014; Lam et al., 2012). Emphasis is placed on perceptual and cognitive underpinnings, uncertainty communication, narrative structures, and ethical and accessible practice (Segel & Heer, 2010; Spiegelhalter et al., 2011; D’Ignazio & Klein, 2020; W3C, 2023). Studio critique and hands-on labs complement readings drawn from canonical and contemporary scholarship. The culminating project requires students to produce an interactive visualization accompanied by a methodological justification and evaluative evidence.

Prerequisites

• Introductory statistics (descriptive and inferential) and basic probability
• Programming proficiency in Python or R (data manipulation and plotting)
• Recommended: basic human-computer interaction or research methods

Learning Outcomes Upon successful completion, students will be able to:

• Analyze visualization problems by decomposing domain goals into data and task abstractions and selecting appropriate analytical form factors (Munzner, 2014; Shneiderman, 1996).
• Justify visual encoding and interaction choices using empirical findings in graphical perception and design theory (Cleveland & McGill, 1984; Mackinlay, 1986; Ware, 2021; Bertin, 2011).
• Implement static and interactive visualizations using contemporary toolchains and apply perceptual best practices for color, shape, position, and motion (Brewer, 2015; Ware, 2021).
• Communicate findings through narrative visualization while maintaining statistical integrity and transparency about uncertainty (Segel & Heer, 2010; Spiegelhalter et al., 2011; Tufte, 2001).
• Evaluate visualization effectiveness using appropriate empirical methods (e.g., controlled experiments, heuristic inspections, and usage analytics) and report results with methodological rigor (Lam et al., 2012; Heer & Bostock, 2010).
• Design ethically and accessibly, addressing bias, inclusivity, and compliance with accessibility standards (D’Ignazio & Klein, 2020; W3C, 2023).

Content Outline

• Foundations: Why visualize; data types; task taxonomies; the nested model for visualization design (Munzner, 2014; Shneiderman, 1996).
• Visual encoding and perception: marks, channels, ranking of encodings, Gestalt principles, preattentive features (Cleveland & McGill, 1984; Mackinlay, 1986; Ware, 2021; Bertin, 2011).
• Color and accessibility: palettes, contrast, color vision deficiency, map design (Brewer, 2015; W3C, 2023).
• Statistical graphics and EDA: comparisons, distributions, trends, multivariate data, model diagnostics; avoiding deceptive designs (Tufte, 2001).
• Interaction: overview–zoom–filter–details-on-demand; coordination; dynamic queries; dashboards (Shneiderman, 1996).
• Narrative visualization: genres, annotations, sequencing, scrollytelling, audience considerations (Segel & Heer, 2010).
• Uncertainty visualization: quantiles, intervals, densities, ensembles; risk communication (Spiegelhalter et al., 2011).
• Evaluation methods: study design, metrics, qualitative/quantitative analysis, validity considerations, replication (Lam et al., 2012; Heer & Bostock, 2010).
• Ethics and power in visualization: representation, privacy, harm mitigation, equity (D’Ignazio & Klein, 2020).
• Implementation labs: grammar-of-graphics approaches; declarative and imperative toolchains; reproducible workflows.

Learning Activities and Pedagogy

• Brief lectures anchored in empirical findings and design theory
• Guided labs in Python/R and web-based visualization
• Studio critiques with structured peer feedback and revision cycles
• Seminar discussions of research papers and reflective response memos
• Evaluation workshops on experimental design and analysis

Assessment Strategy

• Design critiques and redesign assignments referencing empirical literature
• Implementation assignments (static and interactive visualizations) with design rationales
• Reading quizzes and short analytic memos to assess theoretical comprehension
• Empirical evaluation report (e.g., A/B test or task-based study) with analysis and interpretation
• Capstone project: proposal, prototype, user study or expert review, final artifact, and written report documenting the full design–evaluation pipeline

Primary Resources

• Munzner, T. (2014). Visualization Analysis and Design. CRC Press.
• Ware, C. (2021). Information Visualization: Perception for Design (4th ed.). Morgan Kaufmann.
• Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press.
• Bertin, J. (2011). Semiology of Graphics: Diagrams, Networks, Maps. Esri Press.
• Brewer, C. A. (2015). Designing Better Maps (2nd ed.). Esri Press.
• Selected research articles listed in the References.

Software and Technical Requirements

• Python (Altair, Matplotlib, Seaborn) or R (ggplot2), with Jupyter/RStudio
• Optional web stack for interaction (D3.js/Observable)
• Version control (Git) and a reproducible computing environment

References Bertin, J. (2011). Semiology of graphics: Diagrams, networks, maps (W. J. Berg, Trans.). Esri Press. (Original work published 1967)

Brewer, C. A. (2015). Designing better maps: A guide for GIS users (2nd ed.). Esri Press.

Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. New Riders.

Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531–554.

D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.

Heer, J., & Bostock, M. (2010). Crowdsourcing graphical perception: Using Mechanical Turk to assess visualization design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 203–212). ACM.

Lam, H., Bertini, E., Isenberg, P., Plaisant, C., & Carpendale, S. (2012). Empirical studies in information visualization: Seven scenarios. IEEE Transactions on Visualization and Computer Graphics, 18(9), 1520–1536.

Mackinlay, J. (1986). Automating the design of graphical presentations of relational information. ACM Transactions on Graphics, 5(2), 110–141.

Munzner, T. (2014). Visualization analysis and design. CRC Press.

Segel, E., & Heer, J. (2010). Narrative visualization: Telling stories with data. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1139–1148.

Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings 1996 IEEE Symposium on Visual Languages (pp. 336–343). IEEE.

Spiegelhalter, D., Pearson, M., & Short, I. (2011). Visualizing uncertainty about the future. Science, 333(6048), 1393–1400.

Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Graphics Press.

W3C. (2023). Web Content Accessibility Guidelines (WCAG) 2.2. https://www.w3.org/TR/WCAG22/

Ware, C. (2021). Information visualization: Perception for design (4th ed.). Morgan Kaufmann.