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研究生: 張祖瑜
Chang, Tsu-Yu
論文名稱: PISA與社經地位相關研究之文獻計量分析(2000-2024)
A Bibliometric Analysis of Research on PISA and Socioeconomic Status, 2000-2024
指導教授: 陳榮政
CHEN, JUNG-CHENG
口試委員: 何希慧
HO, SHI-HUEI
林信志
LIN, HSIN-CHIH
學位類別: 碩士
Master
系所名稱: 教育學院 - 教育學系
Department of Education
論文出版年: 2026
畢業學年度: 115
語文別: 中文
論文頁數: 80
中文關鍵詞: PISA社經地位(SES)ESCS文獻計量分析VOSviewer
外文關鍵詞: Programme for International Student Assessment (PISA), Socioeconomic Status (SES), Economic, Social and Cultural Status (ESCS), Bibliometric Analysis, VOSviewer
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  • 隨著Organisation for Economic Co-operation and Development(OECD)所推動之國際學生能力評量計畫(Programme for International Student Assessment, PISA)逐漸成為國際教育研究的重要資料來源,學生社經地位(Socioeconomic Status, SES)及經濟、社會與文化地位(Economic, Social and Cultural Status, ESCS)與學習成就之關係,已成為教育公平研究的重要議題。然而,既有研究多聚焦於個別實證分析,較少從整體學術發展脈絡系統性整理相關研究之主題結構、知識發展及演變趨勢。因此,本研究採用文獻計量分析方法,系統性探討PISA與社經地位相關研究之整體發展情形,以建構該領域之知識結構並掌握其研究演變脈絡。
    本研究以2000年至2024年間收錄於Web of Science Core Collection資料庫之PISA與社經地位相關國際期刊文獻為研究對象,依據設定之檢索條件共納入276篇文獻,採用文獻計量分析進行整理,並運用VOSviewer進行關鍵字共現分析與視覺化分析,探討本研究領域之整體發展趨勢、主要研究主題、主題演變及知識結構。
    研究結果顯示,PISA與社經地位相關研究自2000年以來整體呈現持續成長趨勢,近年研究產出明顯增加,主要發文國家集中於中國、美國及土耳其;然而,研究產出數量與研究影響力並非完全一致,部分國家雖發文量較少,仍具有較高之平均引用表現。關鍵字共現分析結果顯示,本研究領域主要形成五大研究主題群集,分別為「PISA學習情境與學校因素」、「社經地位與教育不平等」、「學生心理因素與學習成就」、「學生學習表現與教育成果」以及「社經背景、教育公平與教育政策」。此外,研究主題由早期著重於社經背景與學業成就之關聯,逐步擴展至教育制度差異、學生心理因素、教育公平、數位科技應用及教育韌性等議題,呈現研究內容日益多元且跨領域發展之趨勢;其中,學生學習成果為整體知識網絡之核心,並與社經背景、教育公平及學校因素等研究主題形成緊密連結。
    本研究透過文獻計量分析系統性整理2000年至2024年間PISA與社經地位相關研究之整體發展脈絡,建構本研究領域之知識結構與發展版圖,並呈現主要研究主題、核心議題及其演變趨勢。研究結果除可作為未來教育公平及PISA相關研究之參考外,亦可提供教育政策制定與相關研究發展之參考依據,並有助於掌握本研究領域未來之發展方向。


    The Programme for International Student Assessment (PISA), developed by the Organisation for Economic Co-operation and Development (OECD), has become one of the most important international data sources in educational research. The relationship between students' Socioeconomic Status (SES), Economic, Social and Cultural Status (ESCS), and academic achievement has long been a central topic in research on educational equity. However, previous studies have primarily focused on individual empirical investigations, while relatively few have systematically examined the thematic structure, knowledge development, and evolution of this research field from a comprehensive perspective. Therefore, this study employed bibliometric analysis to systematically investigate the overall development of research on PISA and socioeconomic status, with the aim of constructing the knowledge structure and identifying the evolutionary trajectory of this field.
    This study analyzed international journal articles related to PISA and socioeconomic status published between 2000 and 2024 and indexed in the Web of Science Core Collection database. Based on predefined search criteria, a total of 276 articles were included. Bibliometric analysis was employed, and VOSviewer was used to conduct keyword co-occurrence analysis and visualization to examine research trends, major research themes, thematic evolution, and the knowledge structure of this field.
    The results indicate that research on PISA and socioeconomic status has demonstrated a continuous growth trend since 2000, with a substantial increase in publications in recent years. China, the United States, and Türkiye were the leading contributors in terms of publication output. Nevertheless, research productivity was not entirely consistent with research impact, as several countries with relatively fewer publications achieved higher average citation rates. Keyword co-occurrence analysis identified five major thematic clusters: (1) PISA learning contexts and school factors; (2) socioeconomic status and educational inequality; (3) student psychological factors and academic achievement; (4) student learning performance and educational outcomes; and (5) socioeconomic background, educational equity, and educational policy. Furthermore, the research focus has gradually shifted from the direct relationship between socioeconomic background and academic achievement toward broader issues, including educational systems, psychological factors, educational equity, digital technology, and academic resilience, reflecting an increasingly diverse and interdisciplinary research landscape. Among these themes, student learning outcomes constitute the core of the overall knowledge network and are closely connected with socioeconomic background, educational equity, and school-related factors.
    By applying bibliometric analysis, this study systematically maps the overall development of research on PISA and socioeconomic status from 2000 to 2024, constructs the knowledge structure of this research field, and illustrates its major themes, core issues, and evolutionary trends. The findings provide valuable references for future research on educational equity, educational policymaking, and related studies, while also contributing to a more comprehensive understanding of the future development of research on PISA and socioeconomic status.

    第一章 緒論 1
    第一節 研究背景與動機 1
    第二節 研究目的與問題 2
    第三節 研究流程與範圍限制 3
    第四節 名詞釋義 5
    第二章 文獻探討 9
    第一節 國際大型學生成就評量之興起與PISA研究定位 9
    第二節 PISA評量架構與背景變項設計 16
    第三節 學生社經地位(SES/ESCS)之概念與測量 19
    第四節 PISA與社經地位研究之國際實證發展 22
    第五節 文獻計量分析與研究主題整理之必要性 30
    第六節 綜合討論 32
    第三章 研究設計與實施 35
    第一節 研究方法與研究架構 35
    第二節 資料來源與文獻篩選條件 36
    第三節 資料處理流程與研究工具 37
    第四節 資料分析方法 38
    第五節 研究信度、效度與研究倫理 39
    第四章 研究結果與分析 43
    第一節 PISA與社經地位研究之整體發展趨勢 43
    第二節 PISA與社經地位研究之主題結構分析 52
    第三節 PISA與社經地位研究之主題演變分析 60
    第四節 PISA與社經地位研究之知識結構分析 62
    第五節 綜合討論 66
    第五章 研究結論與建議 69
    第一節 研究結論 69
    第二節 研究建議 70
    參考文獻 75

    中文文獻
    教育部(2023年12月5日)。臺灣PISA 2022成果發表。
    教育部國民及學前教育署(2023年12月7日)。臺灣學生參加國際學生能力評量(PISA)暨公民素養(ICCS)研究成果報告。行政院第3883次院會報告。
    李敦仁、余民寧(2005)。社經地位、手足數目、家庭教育資源與教育成就結構關係模式之驗證:以 TEPS 資料庫資料為例。臺灣教育社會學研究,5(2),1–47。
    林俊瑩、吳裕益(2007)。家庭因素、學校因素對學生學業成就的影響:階層線性模式的分析。教育研究集刊,53(4),107–144。https://doi.org/10.6910/BER.200712_(53-4).0004
    孫青山、黃毅志(1996)。補習教育、文化資本與教育取得。臺灣社會學刊,19,95–139。https://doi.org/10.6786/TJS.199603.0095
    張芳全(2021a)。家庭社經地位對數學學習成就的影響:多重中介變項之探究。教育與多元文化研究,24,1–43。https://doi.org/10.3966/207802222021110024001
    張芳全(2021b)。家庭社經地位與閱讀學習成就關聯:多重中介變項探究。學校行政雙月刊,136,201–228。https://doi.org/10.6423/HHHC.202111_(136).0010
    張芳全(2022)。補習有用嗎?國中生家庭社經地位、英語補習時間成長軌跡對英語學習成就的影響。臺灣教育社會學研究,22(2),47–91。https://doi.org/10.53106/168020042022122202002
    張芳全(2024)。學生家庭經濟社會文化地位與數學素養關聯之後設分析:PISA 2022資料為例。教育研究學報,58(2),77–102。https://doi.org/10.53106/199044282024105802004
    張芳全(2025a)。學生的家庭社經地位與英語學習成就關聯之後設分析。學校行政雙月刊,156,310–338。https://doi.org/10.6423/HHHC.202503_(156).0010
    張芳全(2025b)。家庭經濟社會文化地位、數學焦慮、好奇心、學校歸屬感、學習效能、教師支持對數學素養預測的探討——以臺灣參加PISA 2022為例。學校行政雙月刊,156,1–29。https://doi.org/10.6423/HHHC.202503_(156).0001
    蔡明學(2025年4月)。〖國際脈動〗以PISA 2022探討臺灣學生學習表現:從教育堅韌性國家(臺、日、韓、立)進行比較。國家教育研究院電子報,254,20–29。
    英文文獻
    Agasisti, T., Avvisati, F., Borgonovi, F., & Longobardi, S. (2018). Academic resilience: What schools and countries do to help disadvantaged students succeed in PISA. OECD Education Working Papers No. 167. OECD Publishing. https://doi.org/10.1787/e22490ac-en
    Avvisati, F. (2020). The measure of socioeconomic status in PISA. OECD Publishing.
    Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
    Borgonovi, F., & Montt, G. (2012). Parental involvement in selected PISA countries and economies. OECD Education Working Papers, No. 73. OECD Publishing.
    Bourdieu, P. (1986). The forms of capital. In J. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). Greenwood Press.
    Bradley, R. H., & Corwyn, R. F. (2002). Socioeconomic status and child development. Annual Review of Psychology, 53, 371-399.https://doi.org/10.1146/annurev.psych.53.100901.135233
    Bray, M. (2009). Confronting the shadow education system: What government policies for what private tutoring? UNESCO- IIEP.
    Breakspear, S. (2012). The policy impact of PISA: An exploration of the normative effects of international benchmarking in school system performance. OECD Education Working Papers No. 71. OECD Publishing.
    Carvalho, L. M. (2012). The fabrications and travels of a knowledge-policy instrument. European Educational Research Journal, 11(2), 172–188. https://doi.org/10.2304/eerj.2012.11.2.172
    Chen, C. (2017). Science mapping: A systematic review of the literature. Journal of Data and Information Science, 2(2), 1–40.https://doi.org/10.1515/jdis-2017-0006
    Chmielewski, A. K. (2019). The global increase in the socioeconomic achievement gap, 1964 to 2015. American Sociological Review, 84(3), 517–544.https://doi.org/10.1177/0003122419847165
    Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A., Weinfeld, F. D., & York, R. L. (1966). Equality of educational opportunity. U.S. Department of Health, Education, and Welfare.
    Davis-Kean, P. (2005). The influence of parent education and family income on child achievement. Journal of Family Psychology, 19(2), 294–304.https://doi.org/10.1037/0893-3200.19.2.294
    Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis. Journal of Business Research, 133, 285–296.https://doi.org/10.1016/j.jbusres.2021.04.070
    Esping-Andersen, G. (2004). Untying the Gordian knot of social inheritance. Research in Social Stratification and Mobility, 21, 115–138. https://doi.org/10.1016/S0276-5624(04)21007-1
    Grek, S. (2009). Governing by numbers: The PISA effect in Europe. Journal of Education Policy, 24(1), 23–37.https://doi.org/10.1080/02680930802412669
    Hanushek, E. A., & Woessmann, L. (2006). Does educational tracking affect performance and inequality? Differences-in-differences evidence across countries. The Economic Journal, 116(510), 63–76.
    Hauser, R. M., & Warren, J. R. (1997). Socioeconomic indexes for occupations. Sociological Methodology, 27, 177–298.
    Hernández-Torrano, D., & Courtney, M. G. R. (2021). Modern international large-scale assessment in education: An integrative review and mapping of the literature. Large-Scale Assessments in Education, 9, Article 9.https://doi.org/10.1186/s40536-021-00109-1
    IEA. (n.d.). TIMSS overview. International Association for the Evaluation of Educational Achievement. https://www.iea.nl/studies/iea/timss
    Kriegbaum, K., Jansen, M., & Spinath, B. (2015). Motivation: A predictor of PISA’s mathematical competence beyond intelligence and prior test achievement. Learning and Individual Differences, 43, 140–148. https://doi.org/10.1016/j.lindif.2015.08.026
    Marks, G. N. (2017). Is SES really that important for educational outcomes? The Australian Educational Researcher, 44(2), 191–211.https://doi.org/10.1007/s13384-016-0219-2
    Martin, M. O., Mullis, I. V. S., & Hooper, M. (2016). TIMSS 2015 technical report. IEA.
    Mullis, I. V. S., & Martin, M. O. (2017). TIMSS 2019 assessment frameworks. IEA.
    OECD. (2010). PISA 2009 results: Overcoming social background (Vol. II). OECD Publishing.https://doi.org/10.1787/9789264091504-en
    OECD. (2016). Equity in education: Breaking down barriers to social mobility. OECD Publishing. https://doi.org/10.1787/9789264073234-en
    OECD. (2016). PISA 2015 results (Volume I): Excellence and equity in education. OECD Publishing.
    OECD. (2017). PISA 2015 technical report. OECD Publishing.
    OECD. (2018). Equity in education: Breaking down barriers to social mobility. OECD Publishing. https://doi.org/10.1787/9789264073234-en
    OECD. (2018). PISA 2018 results (Volume I): What students know and can do. OECD Publishing.
    OECD. (2018). PISA 2018 technical report. OECD Publishing.
    OECD. (2019). PISA 2018 assessment and analytical framework. OECD Publishing.
    OECD. (2020). PISA 2018 technical report. OECD Publishing.
    OECD. (2022). PISA 2022 assessment framework. OECD Publishing.https://www.oecd.org/pisa/data/pisa-2022-assessment-framework.pdf
    OECD. (2023). PISA 2022 results (Volume I): What students know and can do. OECD Publishing.
    OECD. (2023). PISA 2022 results (Volume I): The state of learning and equity in education. OECD Publishing. https://doi.org/10.1787/53f23881-en
    OECD, & World Bank. (2015). A review of international large-scale assessments in education. OECD Publishing.
    Ozga, J. (2009). Governing education through data in England. Journal of Education Policy, 24(2), 149–162.
    Perry, L. B., & McConney, A. (2010). Does the SES of the school matter? An examination of socioeconomic status and student achievement using PISA 2003. Teachers College Record, 112(4), 1137–1162. https://doi.org/10.1177/016146811011200401
    Pintrich, P. R. (1999). The role of motivation in self-regulated learning. International Journal of Educational Research, 31(6), 459–470. https://doi.org/10.1016/S0883-0355(99)00015-4
    Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? (pp. 91–115). Russell Sage Foundation.
    Rutkowski, L., & Rutkowski, D. (2013). Measuring socioeconomic background in PISA. Research in Comparative and International Education, 8(3), 259–278. https://doi.org/10.2304/rcie.2013.8.3.259
    Rutkowski, L., & Rutkowski, D. (2013). Trends in TIMSS and PISA performance. International Journal of Testing, 13(3), 238–258. https://doi.org/10.1080/15305058.2012.728356
    Rutter, M. (2012). Resilience as a dynamic concept. Development and Psychopathology, 24(2), 335–344. https://doi.org/10.1017/S0954579412000028
    Schleicher, A. (2018). World class: How to build a 21st-century school system. OECD Publishing.
    Schleicher, A. (2019). PISA 2018: Insights and interpretations. OECD Publishing.
    Sellar, S., & Lingard, B. (2013). The OECD and global governance in education. Journal of Education Policy, 28(5), 710–725.https://doi.org/10.1080/02680939.2013.779791
    Sirin, S. R. (2005). Socioeconomic status and academic achievement. Review of Educational Research, 75(3), 417–453.https://doi.org/10.3102/00346543075003417
    Takayama, K. (2010). Politics of externalisation in international education reform discourse. Comparative Education, 46(4), 411–433.
    Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
    Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact, 285–320. Springer.
    Wang, X. S., Perry, L. B., Malpique, A., & Ide, T. (2023). Factors predicting mathematics achievement in PISA: A systematic review. Large-scale Assessments in Education, 11, Article 24. https://doi.org/10.1186/s40536-023-00174-8
    White, K. (1982). The relation between SES and academic achievement. Psychological Bulletin, 91(3), 461–481.
    Wiseman, A. W. (2010). The uses of evidence for educational policymaking. Review of Research in Education, 34, 1–24.
    Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629

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