教學大綱與進度

課程基本資料：

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
115-1	366699	教育訓練實施與績效評鑑	1	2.0	2	★	程聖倫	技職所	0	0

教學大綱與進度：

教師姓名	程聖倫　教師諮商時間(Office Hours)
Email	scheng@ntut.edu.tw
最後更新時間	2026-06-29 22:43:01
課程大綱	In this course, students will gain a conceptual and practical understanding of program evaluation as a problem of causal inference under measurement error. Because every evaluation makes a causal claim about a construct that is not directly observed—knowledge, skill, transfer, performance—students will learn why measurement error is the central threat to a credible evaluation and how the latent-variable (structural equation modeling) framework addresses it. The first half builds the measurement and structural machinery; the second half re-examines the standard impact-evaluation designs (randomized assignment, instrumental variables, regression discontinuity, difference-in-differences, matching) as special cases of a structural model, making explicit where each one assumes the outcome is measured without error and what to do when it is not. Students will gain practical experience specifying, estimating, and interpreting these models, and judging what an evaluation's findings can and cannot support.
課程進度 (1-16週)	Week 1: Overview of the course; program evaluation as a causal claim about an unobserved construct Week 2: Causality and causal models Week 3: Structural models with observed variables: regression and path analysis Week 4: The consequences of measurement error: attenuation, bias, and reliability Week 5: Measurement models: the relation between latent and observed variables Week 6: Confirmatory factor analysis Week 7: Measurement invariance across groups and over time Week 8: The general model: measurement and structure combined Week 9: Mid-term Week 10: Causal inference and counterfactuals (I): potential outcomes and structural causal models Week 11: Causal inference and counterfactuals (II): confounding and latent confounders Week 12: Randomized assignment (I): the RCT benchmark and the latent outcome Week 13: Randomized assignment (II): pre/post designs and latent change models Week 14: Instrumental variables and model-implied instrumental variables (MIIV-2SLS) Week 15: Regression discontinuity design with measurement error in the running variable Week 16: Difference-in-differences and longitudinal measurement invariance Week 17: Matching and propensity scores with error-laden covariates Week 18: Final
彈性學習 (17-18週)	類別 　 內容 　 時數 (小時) 　 學習成果 　 評量比例
評量方式與標準	Attendance 20% In-class presentation 40% Final evaluation report 40%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：是 Course materials will be prepared by the instructor. Optional: Bollen, K. A. (1989). Structural equations with latent variables. Wiley. Guerra-Lòpez, I. (2008). Performance evaluation: Proven approaches for improving program and organizational performance. San Francisco: Jossey-Bass. Gertler, P. J. et al. (2016) Impact evaluation in practice, 2nd ed. (Inter-American Development Bank; World Bank, 2016).
課程諮詢管道	The best way to reach out to me is through my email: scheng@ntut.edu.tw. I am happy to speak with you during my office hours.
延伸教學與資源	● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification. ● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
課程對應SDGs指標	SDG4：優質教育（Quality Education）
課程是否導入AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
備註	Google Meet will be used in case in-person classes are suspended due to the pandemic. The instructor will send an invitation link to the whole class in advance.