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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/158030


    Title: 出席助教課程與考試成績之關聯性：迴歸不連續性方法之應用
    Ta Recitation Classes and Students Learning Outcomes: an Application of Regression Discontinuity Design
    Authors: 陳鎮洲
    Contributors: 經濟系
    Keywords: 助教;考試成績;迴歸不連續性
    teaching assistant;exam performance;regression discontinuity
    Date: 2020-03
    Issue Date: 2025-07-16 11:12:02 (UTC+8)
    Abstract: 本研究計畫主要的目的在於收集與整理有關大學生出席助教實習課程與其課業表現的文獻，特別是與經濟學教學有關的研究文獻，並且建立適當的理論與假說，進行分析與預測大學生助教實習課程出席與否，如何影響學生學業學習成效。同時透過收集詳盡的學生個人特性、學習型態、以及課業表現等資料，從事嚴謹的統計分析，以探討個人特性以及學習型態對學生課業表現的影響，並驗證相關理論模型的假說與預測的結果。最後根據相關的理論模型分析以及實證的估計結果，提供未來其他國內外學校規劃相關課程教學之參考。本計畫預計利用迴歸不連續性實驗方法，並透過利用追蹤資料的優勢，以及控制更多的學習投入因素，從而精確地估計影響大學生助教實習課程出席與否的行為模式，進而利用此行為模式以工具變數法來修正上課出席可能是內生性的問題，獲得較為精確的助教實習課程出席與否對學生學習成效的影響估計。具體而言，本計畫主要分成兩個主要部分：（1）估計學生出席助教實習課程對其學習成效的影響；（2）分析影響學生助教實習課程出席與否的因素。
    Determinants of college students' academic performance is an important issue in higher education. Among all factors, whether or not attending supplemental instruction, like weekly teaching assistant (TA) review sessions, affects students' exam performance has received substantial attention. However, only a few studies have explored similar topics. In this proposed project, we plan to study the TA supplemental instruction attendance effect on students, using several micro-level data sets from Taiwan. Firstly, a regression discontinuity experimental design will be used in order to address the issue of endogenous attendance decisions. Specifically, students with midterm exam grades below the median grade will be required to attend the weekly TA supplemental instruction. Then, using the variable "whether students are required to attend TA sessions" as an instrumental variable for students' TA session attendance, we will be able to consistently estimate the attending TA supplemental instruction.The major difference and contribution of this project is that regression discontinuity experimental design and more control variables are used in the models. We also take advantage of the panel data nature of the data set. Both factors help us to obtain better and more precise estimates of the effects.
    Relation: 科技部, MOST107-2410-H004-014, 107.08-108.07
    Data Type: report
    Appears in Collections:[經濟學系] 國科會研究計畫

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