查詢教學大綱與進度

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

110-1 294670 校務研究 1 2.0 2 ★ 傅遠智技職所 10 0 ◎碩博課程全英文授課

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
110-1	294670	校務研究	1	2.0	2	★	傅遠智	技職所	10	0	◎碩博課程全英文授課

教學大綱與進度：

教師姓名傅遠智

Email fuyc20@gmail.com

最後更新時間 2021-09-15 12:02:05

課程大綱 The mission of Institutional Research is to support the evidence-based decision-making in education institutions. Therefore, in this course, the classmates would be asked to learn the programming language to deal with the real data and provide solid evidence to the leadership. You would be asked to fully engage in the data analysis during this semester. By the end of this course, you should be able to conduct complicated data analysis with confidence. Meanwhile, you should be able to provide productive suggestions for your supervisor's consideration. This course not only prepare you as a qualified IR officers. It also lead you as a professional data analytician.

課程進度 Week 1: the introduction of the syllabus and course requirement Week 2: the foundation of Institutional research Week 3: the working environment in Stata Week 4: data cleaning & data manipulation Week 5: Project Task I Week 6: data visualization Week 7: data modeling: multiple regression, logistic regression, quantile regression Week 8: data reporting Week 9: Project Task II Week 10: team work in PIAAC data Week 11: team work in PISA data Week 12: team work in administration record data Week 13: Advanced topic I:panel data, fixed effect and random effect Week 14: Advanced topic II: instrument variable Week 15: quasi-experiment I: Difference in Difference, Synthetic Control Week 16: quasi-experiment II: Regression discontinuity, Coarsened Exact Matching, Propensity Score Method Week 17: Special talk: Geographic Information System, Social network analysis Week 18: Project Task III

評量方式與標準 1.Project task I: 30% 2.Project task II: 30% 3. Project task III: 30% 4. class participation: 10%

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
Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist's companion. Princeton university press.

課程諮詢管道課後於教室詢問。

延伸教學與資源

課程對應SDGs指標

課程是否導入AI

備註 ●上課方式：遠距上課

●評量方式：
參考評量標準，採線上報告及操作方式。
●補充說明資訊：
相關課程補充資料將於開學第一周提供網路社群連結供修課學生使用。

教師姓名	傅遠智
Email	fuyc20@gmail.com
最後更新時間	2021-09-15 12:02:05
課程大綱	The mission of Institutional Research is to support the evidence-based decision-making in education institutions. Therefore, in this course, the classmates would be asked to learn the programming language to deal with the real data and provide solid evidence to the leadership. You would be asked to fully engage in the data analysis during this semester. By the end of this course, you should be able to conduct complicated data analysis with confidence. Meanwhile, you should be able to provide productive suggestions for your supervisor's consideration. This course not only prepare you as a qualified IR officers. It also lead you as a professional data analytician.
課程進度	Week 1: the introduction of the syllabus and course requirement Week 2: the foundation of Institutional research Week 3: the working environment in Stata Week 4: data cleaning & data manipulation Week 5: Project Task I Week 6: data visualization Week 7: data modeling: multiple regression, logistic regression, quantile regression Week 8: data reporting Week 9: Project Task II Week 10: team work in PIAAC data Week 11: team work in PISA data Week 12: team work in administration record data Week 13: Advanced topic I:panel data, fixed effect and random effect Week 14: Advanced topic II: instrument variable Week 15: quasi-experiment I: Difference in Difference, Synthetic Control Week 16: quasi-experiment II: Regression discontinuity, Coarsened Exact Matching, Propensity Score Method Week 17: Special talk: Geographic Information System, Social network analysis Week 18: Project Task III
評量方式與標準	1.Project task I: 30% 2.Project task II: 30% 3. Project task III: 30% 4. class participation: 10%
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 Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist's companion. Princeton university press.
課程諮詢管道	課後於教室詢問。
延伸教學與資源
課程對應SDGs指標
課程是否導入AI
備註	●上課方式：遠距上課 ●評量方式：參考評量標準，採線上報告及操作方式。 ●補充說明資訊：相關課程補充資料將於開學第一周提供網路社群連結供修課學生使用。