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研究生(外文):Chuang, Ting-Zhen
論文名稱(外文):Exploring the Influence of Health Conditions of Students and the Usage of Digital Network Devices on PISA Scores: A Comparative Study of 22 Countries
指導教授(外文):Hsu, Mei
口試委員(外文):Chen, Been-LonLai, Chih-FangHsu, Mei
外文關鍵詞:The Program for International Student Assessment (PISA)Health Condition of StudentsThe Usage of Digital Network DevicesOLS Regression Model
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過去已有相當多關於國際學生能力評估計劃(the Program for International Student Assessment, PISA)的研究,而隨著網路時代的來臨及學生健康議題的關注,本研究主要探討學生健康狀況和數位網路設備對PISA分數的影響。研究方法採用最小平方法(Ordinary-Least-Squares, OLS)之迴歸模型,主要研究解釋變數學生健康狀況及網路設備的使用,另外也包含家庭背景(如:父母教育程度、家戶所得)、家庭資源(如:教育資源、教育經費支出)與自我父母期望等變數。為了讓研究更加完善,採用2009年、2012年、2015年及2018年的PISA資料,擴及22個國家,期望研究結果更全面。研究結果發現,學生健康狀況對PISA成績有顯著的正向影響,學生健康保持良好,確實有助於學業成績的表現,另外在數位網路設備使用上,學生擁有越多元的數位網路設備,在學習利用上更有效率,有助於PISA成績的表現,呈現顯著的正向影響,然而在網路網頁瀏覽的變數,卻與PISA成績呈反向影響,可見網路使用是需要更妥善的管理與運用,才能發揮正向的影響力。最後,22國家區域的比較分析中,發現東南亞國家在PISA分數表現上,相對於落後其他地區,尤其以東亞及北歐國家的PISA分數表現最佳,而其中東亞地區國家再深入探討,發現新加坡、中國的分數特別突出,值得我國台灣教育決策者借鏡,以及師生一起共同努力。
Numerous studies have been conducted in the past about the Program for International Student Assessment (PISA). In light of the internet era and growing concerns about student health, this study focuses on examining the influence of student health conditions and digital internet devices on PISA scores. The research methodology utilizes the Ordinary-Least-Squares (OLS) regression model, with health conditions of students and the usage of digital internet devices as the primary explanatory variables. It also incorporates factors such as parental education backgrounds, household income, educational equipment and expenditure, and self-expectations.

To enhance the comprehensiveness of the research, data from PISA assessments conducted in 2009, 2012, 2015, and 2018 are analyzed, encompassing 22 countries. By incorporating a broader range of data, the study aims to generate more compelling outcomes. The research findings reveal a significant positive impact of health conditions of students on PISA scores, emphasizing the role of good health in academic performance. Moreover, students who possess a diverse array of digital internet devices demonstrate more efficient learning, resulting in improved PISA scores and exhibiting a notable positive effect. However, the variable related to internet web browsing exhibits a negative impact on PISA scores, underscoring the need for proper management and utilization of internet usage to leverage its positive influence.

Lastly, in the comparative analysis among the 22 countries, it is evident that Southeast Asian countries lag behind other regions in terms of PISA performance. Particularly, East Asian and North Europe countries display the highest PISA scores. Among the East Asian countries, Singapore and China excel with exceptional scores, offering valuable insights for educational policymakers in Taiwan and motivating teachers and students to strive for improvement.
Chapter 1 Introduction 1
1.1 Research Backgrounds 1
1.2 Research Purposes 4
1.3 Research Framework 5

Chapter 2 Literature Review and Hypotheses 6
2.1 The Usage of Digital Network Devices 6
2.2 Health Condition of Students 7

Chapter 3 Data and Variables 9
3.1 Sources of Data 9
3.2 Variables 11
3.2.1 PISA Scores 11
3.2.2 Students’ Attributes 12
3.2.3 Parents’ Backgrounds 13
3.2.4 Educational Resources 14
3.2.5 Health Condition of Students 15
3.2.6 The Usage of Digital Network Devices 16
3.2.7 Countries 17
3.2.8 Years 17

Chapter 4 Model Setup 18
4.1 Econometric Model 18
4.2 OLS Model for Year 2009 to Year 2018 19
4.3 OLS Model for Year 2018 22

Chapter 5 Empirical Results 24
5.1 The influence of health condition of students on students' PISA scores. 24
5.2 The impact of the usage of digital network devices on students' PISA scores. 24
5.3 The influence of gender, mother and father education, and educational resources on students' PISA scores. 25
5.4 The impact of self-expectation and parental expectation on students' PISA scores. 27
5.5 Comparisons within groups and between groups for the 22 countries. 28
5.6 The trend of students' PISA performance from Year 2009 to Year 2018. 29

Chapter 6 Conclusions and Suggestions 30

References 59
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