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研究生: 喬祺
Chiao, Chi
論文名稱: 社經地位、ICT使用與運算思維和學業成就間關係之探討
The Study of Socioeconomic Status, ICT Usage and Computational Thinking on Academic Achievement
指導教授: 邱瓊慧
Chiu, Chiung-Hui
口試委員: 林秋斌
Lin, Chiu-pin
崔夢萍
Tsuei, Meng-ping
歐陽誾
Ouyang, Yin
蘇建元
Su, Chien-yuan
邱瓊慧
Chiu , Chiung-hui
口試日期: 2023/07/31
學位類別: 博士
Doctor
系所名稱: 資訊教育研究所
Graduate Institute of Information and Computer Education
論文出版年: 2023
畢業學年度: 111
語文別: 英文
論文頁數: 112
中文關鍵詞: 數位落差資訊與通訊科技使用運算思維青少年性別
英文關鍵詞: Digital divide, ICT usage, Computational thinking, Adolescent, Gender
研究方法: 次級資料分析調查研究
DOI URL: http://doi.org/10.6345/NTNU202301677
論文種類: 學術論文
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  • 數位落差的研究隨著時間和技術的發展而不斷演變。本論文聚焦在近來引起研究人員關注的兩種數位落差:資訊與通訊科技(ICT)使用落差和運算思維落差。為了研究社經地位、ICT使用與運算思維和學業成就間關係。本論文的第一個研究探討了學生的ICT使用對其家庭社會經濟地位和學業成績的中介效果。研究一使用PISA 2012數據來探討四種ICT使用的中介效果差異:包含學習、資訊檢索、社交互動、和休閒,並分析了性別對研究一中介模型的干擾效果。本論文的第二項研究探討了運算思維對學生社會經濟地位和學業成績的中介效果。研究二以問卷收集1128名台灣國中生的數據進行研究。研究二探討了五種運算思維技能的中介效果差異:包含抽象、分解、演算法思維、評估、和概括。本論文的兩項研究發現,用於資訊檢索和社交互動的ICT使用頻率以及計算思維技能可能會擴大學生因家庭社會經濟地位造成的成績差距。性別也可能調節學生的社會經濟地位、信息通信技術的使用及其學業成績之間的直接效果。本論文的發現可以幫助研究人員和教育工作者了解ICT使用和運算思維可能造成的數位落差影響,並採取適當的行動來縮短這些數位落差。

    Research on the digital divide evolves over time and technology development. This dissertation focused on two digital divides that had recently attracted researchers’ attention: ICT usage gap and computational thinking gap. In order to study the relationship between students’ socioeconomic status, ICT usage and computational thinking on their academic achievement. The first study explored the mediating effect of students’ ICT usage on their family socioeconomic status and academic achievement. Study 1 of this dissertation used PISA 2012 data to investigate the difference of mediation effect of four ICT usage: learning, info retrieval, social interaction, and leisure. The moderating effect of gender in the proposed mediation model was also analyzed. The second study in this dissertation explored the mediating effect of computational thinking on students’ socioeconomic status and their academic achievement. A total of 1128 junior high school students from Taiwan participated in study 2, wherein a questionnaire survey was conducted to gather data. Study 2 investigates the difference of mediation effect of five computational thinking skills: abstraction, decomposition, algorithmic thinking, evaluation, and generalization. These two studies showed that ICT for information retrieval and social interactions as well as computational thinking skills might widen achievement gaps caused by students’ socioeconomic status. Gender could moderate the direct effect between students’ socioeconomic status, ICT usage, and their academic achievement. The results of this dissertation could help researchers and educators understand the digital divide of ICT usage and computational thinking and take appropriate action to bridge these digital divides.

    Table of Contents 摘要 i Abstract ii Acknowledgements iii Table of Contents iv List of Tables vi List of Figures viii 1 Introduction 1 1.1 Background 1 1.2 Problem Statement 6 1.3 Purpose of the Study 7 1.4 Research Questions 10 1.5 Definition of Terms 11 1.5.1 Digital divide 11 1.5.2 Adolescent 11 1.5.3 Socioeconomic status (SES) 12 1.5.4 ICT usage 12 1.5.5 CT skills 13 1.5.6 Gender, and region 14 1.5.7 Academic achievement 14 2 Literature Review 16 2.1 The Relationship between Students' SES and ICT usage 16 2.2 The Relationship between Students' SES and Achievement 19 2.3 The Relationship between Students' ICT Usage and Achievement 24 2.4 The Relationship between Students' SES and CT 27 2.5 Relationship between CT and Academic Achievement 29 2.6 Gender Differences 30 3 Study 1 33 3.1 Sample 34 3.2 Materials and Instruments 34 3.3 Analysis 37 3.4 Results 37 3.4.1 Descriptive statistics 37 3.4.2 Relationships between variables 41 3.4.3 Confirmatory factor analysis 41 3.4.4 Direct and indirect effects 44 3.4.5 The moderation effect 50 3.5 Discussion 52 4 Study 2 55 4.1 Participants 56 4.2 Measures and Procedure 58 4.3 Analysis 62 4.4 Results 62 4.4.1 Descriptive statistics 62 4.4.2 Relationships between variables 70 4.4.3 Multi-level direct effect 72 4.4.4 Multi-level indirect effect 75 4.4.5 Moderation analysis 75 4.5 Discussion 80 5 Conclusions 83 5.1 Summary 83 5.2 Limitations 85 5.3 Implications 85 5.4 Recommendations for future study 86 References 87 Appendix 107

    References
    Acilar, A., & Sæbø, Ø. (2023). Towards understanding the gender digital divide: A systematic literature review. Global Knowledge, Memory and Communication, 72(3), 233–249.
    Aesaert, K., & Van Braak, J. (2015). Gender and socioeconomic related differences in performance based ICT competences. Computers and Education, 84, 8–25. https://doi.org/10.1016/j.compedu.2014.12.017
    Aissaoui, N. (2022). The digital divide: A literature review and some directions for future research in light of COVID-19. Global Knowledge, Memory and Communication, 71(8/9), 686–708.
    Ali, R., Hussain, I., & Rahmani, S. H. (2019). How Socioeconomic Classes Influence Academic Grades of Elementary School Students? Defining Mediation Role of School Backgrounds and Cognitive Processing Strategies. Journal of Educational Research, 22(2), 201–227.
    Areepattamannil, S., & Kaur, B. (2013). Factors Predicting Science Achievement of Immigrant and Non-Immigrant Students: A Multilevel Analysis. International Journal of Science & Mathematics Education, 11(5), 1183–1207. https://doi.org/10.1007/s10763-012-9369-5
    Astatke, M., Weng, C., & Chen, S. (2023). A literature review of the effects of social networking sites on secondary school students’ academic achievement. Interactive Learning Environments, 31(4), 2153–2169.
    Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670.
    Aypay, A. (2010). Information and communication technology (ICT) usage and achievement of Turkish students in PISA 2006. Turkish Online Journal of Educational Technology - TOJET, 9(2), 116–124.
    Bazán-Ramírez, A., Hernández-Padilla, E., Bazán-Ramírez, W., & Tresierra-Ayala, M. (2022). Effects of Opportunities to Learn on Peruvian Students’ Science Achievement in Program for International Student Assessment 2015. Frontiers in Education, 7, 297.
    Berkowitz, R., Moore, H., Astor, R. A., & Benbenishty, R. (2017). A research synthesis of the associations between socioeconomic background, inequality, school climate, and academic achievement. Review of Educational Research, 87(2), 425–469.
    Bernardo, A. B. (2021). Socioeconomic status moderates the relationship between growth mindset and learning in mathematics and science: Evidence from PISA 2018 Philippine data. International Journal of School & Educational Psychology, 9(2), 208–222.
    Bhutoria, A., & Aljabri, N. (2022). Patterns of cognitive returns to Information and Communication Technology (ICT) use of 15-year-olds: Global evidence from a Hierarchical Linear Modeling approach using PISA 2018. Computers & Education, 181, 104447.
    Biagi, F., & Loi, M. (2013). Measuring ICT use and learning outcomes: Evidence from recent econometric studies. European Journal of Education, 48(1), 28–42. https://doi.org/10.1111/ejed.12016
    Blignaut, P. (2009). A Bilateral Perspective on the Digital Divide in South Africa. Perspectives on Global Development & Technology, 8(4), 581–601. https://doi.org/10.1163/156915009X12583611836091
    Büchi, M., Just, N., & Latzer, M. (2016). Modeling the second-level digital divide: A five-country study of social differences in Internet use. New Media & Society, 18(11), 2703–2722.
    Byker, E. J. (2014). ICT Oriented toward Nyaya: Community Computing in India’s Slums. International Journal of Education and Development Using Information and Communication Technology, 10(2), 19–28.
    Calderon Gomez, D. (2021). The third digital divide and Bourdieu: Bidirectional conversion of economic, cultural, and social capital to (and from) digital capital among young people in Madrid. New Media & Society, 23(9), 2534–2553.
    Cheema, J. R., & Bo, Z. (2013). Quantity and quality of computer use and academic achievement: Evidence from a large-scale international test program. International Journal of Education & Development Using Information & Communication Technology, 9(2), 95–106.
    Cheema, J. R., & Galluzzo, G. (2013). Analyzing the gender gap in Math achievement: Evidence from a large-scale US sample. Research in Education, 90, 98–112. https://doi.org/10.7227/RIE.90.1.7
    Chen, M. H.-P. (2014). New policy, new opportunity: An unexpected opportunity for music education emerges from Taiwan’s new, twelve-year public education program. Policy and Media In and For a Diverse Global Community, 31.
    Chen, Y.-C., Chiang, M.-H., Shih, V. R.-C., & Lou, S.-J. (2019). The Implementation of the 12-Year Basic Education Science and Technology New Curriculum Guidelines in the Primary School. 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 1–2.
    Chiao, C., & Chiu, C. H. (2018). The mediating effect of ICT usage on the relationship between students’ socioeconomic status and achievement. Asia-Pacific Education Researcher, 27(2), Article 2. https://doi.org/10.1007/s40299-018-0370-9
    Chiu, M.-S. (2022). Linear or quadratic effects of ICT use on science and mathematics achievements moderated by SES: Conditioned ecological techno-process. Research in Science & Technological Education, 40(4), 549–570.
    Chongo, S., Osman, K., & Nayan, N. A. (2020). Level of Computational Thinking Skills among Secondary Science Student: Variation across Gender and Mathematics Achievement. Science Education International, 31(2), 159–163.
    Chytrý, V., Kubiatko, M., Šindelářová, R., & Medová, J. (2022). Socioeconomic Status of University Students as a Limiting Factor for Various Forms of Distance Education during COVID-19 Measures. Sustainability, 14(10), 5898.
    Claro, M., Cabello, T., San Martín, E., & Nussbaum, M. (2015). Comparing marginal effects of Chilean students’ economic, social and cultural status on digital versus reading and mathematics performance. Computers & Education, 82, 1–10. https://doi.org/10.1016/j.compedu.2014.10.018
    Corkin, M. T., Meissel, K., Peterson, E. R., Lee, K., Giacaman, N., Janicot, S., & Morton, S. M. (2022). Are distinct modes of using digital technologies evident by age eight? Implications for digital divides. Computers & Education, 191, 104642.
    Cox, M. J., Niederhauser, D. S., Castillo, N., McDougall, A. B., Sakamoto, T., & Roesvik, S. (2013). Researching IT in education. Journal of Computer Assisted Learning, 29(5), 474–486. https://doi.org/10.1111/jcal.12035
    Czerkawski, B. C., & Lyman, E. W. (2015). Exploring issues about computational thinking in higher education. TechTrends, 59, 57–65.
    Delen, E., & Bulut, O. (2011). The relationship between students’ exposure to technology and their achievement in science and math. Turkish Online Journal of Educational Technology - TOJET, 10(3), 311–317.
    Dobiáš, V., & Šimandl, V. (2022). Socially Disadvantaged Pupils and Computational Thinking: Is There A New Form of Digital Divide? INTED2022 Proceedings, 6542–6551.
    Dolan, J. E. (2016). Splicing the divide: A review of research on the evolving digital divide among K–12 students. Journal of Research on Technology in Education, 48(1), 16–37.
    Dominik Petko, Andrea Cantieni, & Doreen Prasse. (2017). Perceived Quality of Educational Technology Matters. Journal of Educational Computing Research, 54(8), 1070–1091. https://doi.org/doi:10.1177/0735633116649373
    Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155–174. https://doi.org/10.1037/1082-989X.5.2.155
    Erdogdu, F., & Erdogdu, E. (2015). The impact of access to ICT, student background and school/home environment on academic success of students in Turkey: An international comparative analysis. Computers & Education, 82, 26–49. https://doi.org/10.1016/j.compedu.2014.10.023
    Fariña, P., San Martín, E., Preiss, D. D., Claro, M., & Jara, I. (2015). Measuring the relation between computer use and reading literacy in the presence of endogeneity. Computers and Education, 80, 176–186. https://doi.org/10.1016/j.compedu.2014.08.010
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research (JMR), 18(1), Article 1. http://dx.doi.org/10.1177/002224378101800104
    Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Duckworth, D. (2020). Preparing for life in a digital world: IEA international computer and information literacy study 2018 international report. Springer Nature.
    Gorsuch, R. L. (2013). Factor analysis. Psychology press.
    Gouws, L., Bradshaw, K., & Wentworth, P. (2013). First year student performance in a test for computational thinking. Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference, 271–277.
    Gravetter, F. J., & Wallnau, L. B. (2014). Essentials of statistics for the behavioral sciences. Cengage Learning.
    Gretter, S., & Yadav, A. (2016). Computational thinking and media & information literacy: An integrated approach to teaching twenty-first century skills. TechTrends, 60, 510–516.
    Guo, C., & Wan, B. (2022). The digital divide in online learning in China during the COVID-19 pandemic. Technology in Society, 71, 102122.
    Guzeller, C. O., & Akin, A. (2014). Relationship between ICT variables and mathematics achievement based on PISA 2006 database: International evidence. Turkish Online Journal of Educational Technology, 13(1), 184–192.
    Hair, J. F. (2010). Multivariate data analysis (7th ed.). Pearson Education.
    Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.
    Harville, D. A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72(358), 320–338.
    Hayes, A. F., & Rockwood, N. J. (2020). Conditional process analysis: Concepts, computation, and advances in the modeling of the contingencies of mechanisms. American Behavioral Scientist, 64(1), 19–54.
    Heintz, F., Mannila, L., & Farnqvist, T. (2016). A review of models for introducing computational thinking, computer science and computing in k-12 education. 2016 IEEE Frontiers in Education Conference (FIE), 1–9. https://doi.org/10.1109/FIE.2016.7757410
    Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), Article 5. https://doi.org/10.1177/014920639502100509
    Hohlfeld, T. N., Ritzhaupt, A. D., & Barron, A. E. (2010). Connecting schools, community, and family with ICT: Four-year trends related to school level and SES of public schools in Florida. Computers & Education, 55(1), 391–405. https://doi.org/10.1016/j.compedu.2010.02.004
    Hohlfeld, T. N., Ritzhaupt, A. D., Barron, A. E., & Kemker, K. (2008). Examining the digital divide in K-12 public schools: Four-year trends for supporting ICT literacy in Florida. Computers & Education, 51(4), 1648–1663. https://doi.org/10.1016/j.compedu.2008.04.002
    Hohlfeld, T. N., Ritzhaupt, A. D., Dawson, K., & Wilson, M. L. (2017). An examination of seven years of technology integration in Florida schools: Through the lens of the Levels of Digital Divide in Schools. Computers & Education, 113, 135–161.
    Hollingshead, A. B. (2011). Four Factor Index of Social Status1. YALE JOURNAL OF SOCIOLOGY, 21.
    Hollingworth, S., Mansaray, A., Allen, K., & Rose, A. (2011). Parents’ perspectives on technology and children’s learning in the home: Social class and the role of the habitus. Journal of Computer Assisted Learning, 27(4), 347–360. https://doi.org/10.1111/j.1365-2729.2011.00431.x
    Hsing-Ning, L., & Yung-Fu, C. (2022). Comparison of Efficiency of Reading/Literacy Education in East Asian Countries in PISA 2018. Educational Review, 59, 77–117.
    Hu, L., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling, 6(1), 1–55.
    Hu, X., Gong, Y., Lai, C., & Leung, F. K. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1–13.
    Huang, J.-T., & Hsieh, H.-H. (2011). Linking socioeconomic status to social cognitive career theory factors: A partial least squares path modeling analysis. Journal of Career Assessment, 19(4), 452–461.
    Huang, S., Jiang, Y., Yin, H., & Jong, M. S. (2021). Does ICT use matter? The relationships between students’ ICT use, motivation, and science achievement in East Asia. Learning and Individual Differences, 86, 101957.
    Jiang, M. Y.-C., Jong, M. S.-Y., Lau, W. W.-F., & Luk, E. T.-H. (2019). Does ICT Use Matter between Socioeconomic Status and Academic Performance? 2019 International Symposium on Educational Technology (ISET), 83–86.
    Kale, U., Akcaoglu, M., Cullen, T., & Goh, D. (2018). Contextual factors influencing access to teaching computational thinking. Computers in the Schools, 35(2), 69–87.
    Kang, H., & Cogan, L. (2022). The differential role of socioeconomic status in the relationship between curriculum-based mathematics and mathematics literacy: The link between TIMSS and PISA. International Journal of Science and Mathematics Education, 1–16.
    Karpinski, Z., Biagi, F., & Di Pietro, G. (2021). Computational Thinking, Socioeconomic Gaps, and Policy Implications. IEA Compass: Briefs in Education. Number 12. International Association for the Evaluation of Educational Achievement.
    Kılıç, S., Gökoğlu, S., & Öztürk, M. (2021). A valid and reliable scale for developing programming-oriented computational thinking. Journal of Educational Computing Research, 59(2), 257–286.
    Kline, R. B. (2011). Principles and practice of structural equation modeling: Vol. Methodology in the social sciences (3rd ed.). Guilford Press.
    Korovkin, V., Park, A., & Kaganer, E. (2022). Towards conceptualization and quantification of the digital divide. Information, Communication & Society, 1–36.
    Kubiatko, M., & Vlckova, K. (2010). The relationship between ICT use and science knowledge for Czech students: A secondary analysis of PISA 2006. International Journal of Science and Mathematics Education, 8(3), 523–543. https://doi.org/10.1007/s10763-010-9195-6
    LeBreton, J. M., & Senter, J. L. (2008). Answers to 20 questions about interrater reliability and interrater agreement. Organizational Research Methods, 11(4), 815–852.
    Lee, C.-Y., & Kung, H.-Y. (2018). Math self-concept and mathematics achievement: Examining gender variation and reciprocal relations among junior high school students in Taiwan. Eurasia Journal of Mathematics, Science and Technology Education, 14(4), 1239–1252.
    Lee, J., Zhang, Y., & Stankov, L. (2019). Predictive validity of SES measures for student achievement. Educational Assessment, 24(4), 305–326.
    Lee, Y.-H., & Wu, J.-Y. (2012). The effect of individual differences in the inner and outer states of ICT on engagement in online reading activities and PISA 2009 reading literacy: Exploring the relationship between the old and new reading literacy. Learning and Individual Differences, 22(3), 336–342. https://doi.org/10.1016/j.lindif.2012.01.007
    Lee, Y.-H., & Wu, J.-Y. (2013). The indirect effects of online social entertainment and information seeking activities on reading literacy. Computers & Education, 67, 168–177. https://doi.org/10.1016/j.compedu.2013.03.001
    Lei, H., Chiu, M. M., Li, F., Wang, X., & Geng, Y. (2020). Computational thinking and academic achievement: A meta-analysis among students. Children and Youth Services Review, 118, 105439.
    Lenkeit, J., Schwippert, K., & Knigge, M. (2018). Configurations of multiple disparities in reading performance: Longitudinal observations across France, Germany, Sweden and the United Kingdom. Assessment in Education: Principles, Policy & Practice, 25(1), 52–86.
    Li, Y., & Ranieri, M. (2013). Educational and social correlates of the digital divide for rural and urban children: A study on primary school students in a provincial city of China. Computers and Education, 60(1), Article 1. https://doi.org/10.1016/j.compedu.2012.08.001
    Lin, S.-W., Tzou, H.-I., Lu, I.-C., & Hung, P.-H. (2021). Taiwan: Performance in the Programme for International Student Assessment. Improving a Country’s Education: PISA 2018 Results in 10 Countries, 203–226.
    Lu, J., Hao, Q., & Jing, M. (2016). Consuming, sharing, and creating content: How young students use new social media in and outside school. Computers in Human Behavior, 64, 55–64. https://doi.org/10.1016/j.chb.2016.06.019
    Lucendo-Monedero, A. L., Ruiz-Rodríguez, F., & González-Relaño, R. (2019). Measuring the digital divide at regional level. A spatial analysis of the inequalities in digital development of households and individuals in Europe. Telematics and Informatics, 41, 197–217.
    Luu, K., & Freeman, J. G. (2011). An analysis of the relationship between information and communication technology (ICT) and scientific literacy in Canada and Australia. Computers & Education, 56(4), 1072–1082. https://doi.org/10.1016/j.compedu.2010.11.008
    Lythreatis, S., Singh, S. K., & El-Kassar, A.-N. (2021). The digital divide: A review and future research agenda. Technological Forecasting and Social Change, 121359.
    Ma, J. K.-H. (2021). The digital divide at school and at home: A comparison between schools by socioeconomic level across 47 countries. International Journal of Comparative Sociology, 62(2), 115–140.
    Macho, S., & Ledermann, T. (2011). Estimating, Testing, and Comparing Specific Effects in Structural Equation Models: The Phantom Model Approach. Psychological Methods, 16(1), 34–43. https://doi.org/10.1037/a0021763
    MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation Analysis. Annual Review of Psychology, 58(1), 593–614. https://doi.org/10.1146/annurev.psych.58.110405.085542
    MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods. Multivariate Behavioral Research, 39(1), 99–128.
    Marks, G. N., & O’Connell, M. (2021). Inadequacies in the SES–Achievement model: Evidence from PISA and other studies. Review of Education, 9(3), e3293.
    McConney, A., & Perry, L. B. (2010). Science and Mathematics Achievement in Australia: The Role of School Socioeconomic Composition in Educational Equity and Effectiveness. International Journal of Science and Mathematics Education, 8(3), 429–452. https://doi.org/10.1007/s10763-010-9197-4
    Mertens, S., & d’Haenens, L. (2010). The digital divide among young people in Brussels: Social and cultural influences on ownership and use of digital technologies.
    Micheli, M. (2016). Social networking sites and low-income teenagers: Between opportunity and inequality. Information, Communication & Society, 1–17. https://doi.org/10.1080/1369118X.2016.1139614
    Milicic, G., van Borkulo, S. P., Medova, J., Wetzel, S., & Ludwig, M. (2021). Design and development of a learning environment for computational thinking: The erasmus+ project. EDULEARN21 Proceedings, 7376–7383.
    Mindetbay, Y., Bokhove, C., & Woollard, J. (2019). What is the relationship between students’ computational thinking performance and school achievement? International Journal of Computer Science Education in Schools, 2(5), 3–19.
    Ministry of Education. (2014). Curriculum guidelines of 12-year basic education. Ministry of Education Taipei.
    Odell, B., Cutumisu, M., & Gierl, M. (2020). A scoping review of the relationship between students’ ICT and performance in mathematics and science in the PISA data. Social Psychology of Education, 23(6), 1449–1481.
    OECD. (2009). PISA Data Analysis Manual: SPSS, Second Edition. https://doi.org/10.1787/9789264056275-en
    OECD. (2010). Are the new millennium learners making their grade? Technology use and educational performancein PISA 2006. Educational Research and Innovation, OECD Publishing, Paris. https://doi.org/10.1787/9789264076044-en
    OECD. (2014). PISA 2012 Technical Report. https://www.oecd.org/pisa/pisaproducts/PISA-2012-technical-report-final.pdf
    OECD. (2015a). Students, Computers and Learning: Making the Connection. OECD Publishing. https://doi.org/10.1787/9789264239555-en.
    OECD. (2015b). The ABC of gender equality in education: Aptitude, behaviour, confidence, PISA. OECD Publishing. https://doi.org/10.1787/9789264229945-en
    Olsson, D., Gericke, N., Boeve-de Pauw, J., Berglund, T., & Chang, T. (2019). Green schools in Taiwan–Effects on student sustainability consciousness. Global Environmental Change, 54, 184–194.
    Ortiz, R. W., Green, T., & Lim, H. (2011). Families and Home Computer Use: Exploring Parent Perceptions of the Importance of Current Technology. Urban Education, 46(2), 202–215. https://doi.org/10.1177/0042085910377433
    Özgür, H. (2020). Relationships between Computational Thinking Skills, Ways of Thinking and Demographic Variables: A Structural Equation Modeling. International Journal of Research in Education and Science, 6(2), 299–314.
    Polidano, C., Hanel, B., & Buddelmeyer, H. (2013). Explaining the socio-economic status school completion gap. Education Economics, 21(3), 230–247. https://doi.org/10.1080/09645292.2013.789482
    Price, G. (2014). English for all? Neoliberalism, globalization, and language policy in Taiwan. Language in Society, 43(5), 567–589.
    Quaicoe, J. S., & Pata, K. (2020). Teachers’ digital literacy and digital activity as digital divide components among basic schools in Ghana. Education and Information Technologies, 25, 4077–4095.
    Ren, W., Zhu, X., & Yang, J. (2022). The SES-based difference of adolescents’ digital skills and usages: An explanation from family cultural capital. Computers & Education, 177, 104382.
    Richards, C. (2005). The Design of Effective ICT-Supported Learning Activities: Exemplary Models, Changing Requirements, and New Possibilities. Language Learning & Technology, 9(1), 60–79.
    Rockwood, N. J. (2017). Advancing the formulation and testing of multilevel mediation and moderated mediation models. The Ohio State University.
    Rockwood, N. J., & Hayes, A. F. (2017). MLmed: An SPSS macro for multilevel mediation and conditional process analysis. Poster Presented at the Annual Meeting of the Association of Psychological Science (APS), Boston, MA.
    Rodrigues, R. S., Andrade, W. L., & Campos, L. M. S. (2016). Can Computational Thinking help me? A quantitative study of its effects on education. 2016 IEEE Frontiers in Education Conference (FIE), 1–8.
    Román-González, M., Pérez-González, J.-C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the computational thinking test. Computers in Human Behavior, 72, 678–691. https://doi.org/10.1016/j.chb.2016.08.047
    Román-González, M., Pérez-González, J.-C., Moreno-León, J., & Robles, G. (2018). Can computational talent be detected? Predictive validity of the Computational Thinking Test. International Journal of Child-Computer Interaction, 18, 47–58.
    Sari, I. N., & Kismiantini, K. (2023). The school climate in mathematics achievement: Findings from PISA 2018 Indonesia. AIP Conference Proceedings, 2556(1).
    Scheerder, A., Van Deursen, A., & Van Dijk, J. (2017). Determinants of Internet skills, uses and outcomes. A systematic review of the second-and third-level digital divide. Telematics and Informatics, 34(8), 1607–1624.
    Scherer, R. (2020). The case for good discipline? Evidence on the interplay between disciplinary climate, socioeconomic status, and science achievement from PISA 2015. Equity, Equality and Diversity in the Nordic Model of Education, 197–224.
    Schulz, W. (2005, April). Measuring the Socio-Economic Background of Students and Its Effect on Achievement on PISA 2000 and PISA 2003. Annual Meeting of the American Educational Research Association.
    Selby, C., & Woollard, J. (2013). Computational thinking: The developing definition.
    Shala, A., Grajcevci, A., & Latifi, F. (2021). Does Socioeconomic Status Influence Achievement? An analysis of the Performance of Kosovar Students on the 2015 and 2018 PISA Assessment. Journal of Elementary Education, 14(4), 393–408.
    Shank, D. B., & Cotten, S. R. (2014). Does technology empower urban youth? The relationship of technology use to self-efficacy. Computers & Education, 70, 184–193. https://doi.org/10.1016/j.compedu.2013.08.018
    She, H.-C., Lin, H., & Huang, L.-Y. (2019). Reflections on and implications of the Programme for International Student Assessment 2015 (PISA 2015) performance of students in Taiwan: The role of epistemic beliefs about science in scientific literacy. Journal of Research in Science Teaching, 56(10), 1309–1340.
    Shera, P., & Mitre, T. (2012). How does socio-economic status influence educational achievement: A multilevel analysis. International Journal of Science, Innovation & New Technology, 1–9.
    Sirin, S. R. (2005). Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research. Review of Educational Research, 75(3), 417–453.
    Slavinskis, A., Reinkubjas, K., Kahn, K., Ehrpais, H., Kalnina, K., Kulu, E., & Noorma, M. (2015). The Estonian Student Satellite Programme: Providing skills for the modern engineering labour market. Proceedings of First Symposium on Space Educational Activities.
    Song, H.-D., & Kang, T. (2012). Evaluating the impacts of ICT use: A multi-level analysis with hierarchical linear modeling. Turkish Online Journal of Educational Technology, 11(4), 132–140.
    Stevenson, O. (2011). From Public Policy to Family Practices: Researching the Everyday Realities of Families’ Technology Use at Home. Journal of Computer Assisted Learning, 27(4), 336–346. https://doi.org/10.1111/j.1365-2729.2011.00430.x
    Sung, Y.-T., Tseng, F.-L., Kuo, N.-P., Chang, T.-Y., & Chiou, J.-M. (2014). Evaluating the Effects of Programs for Reducing Achievement Gaps: A Case Study in Taiwan. Asia Pacific Education Review, 15(1), 99–113. https://doi.org/10.1007/s12564-013-9304-7
    Tsai, M.-J., Liang, J.-C., & Hsu, C.-Y. (2021). The computational thinking scale for computer literacy education. Journal of Educational Computing Research, 59(4), 579–602.
    Tsai, M.-J., Liang, J.-C., Lee, S. W.-Y., & Hsu, C.-Y. (2022). Structural validation for the developmental model of computational thinking. Journal of Educational Computing Research, 60(1), 56–73.
    Tucker-Drob, E. M., Cheung, A. K., & Briley, D. A. (2014). Gross Domestic Product, Science Interest, and Science Achievement: A Person × Nation Interaction. Psychological Science, 25(11), 2047–2057. https://doi.org/10.1177/0956797614548726
    Valdez, V. B., & Javier, S. P. (2020). Digital divide: From a peripheral to a core issue for all SDGs. Reduced Inequalities, 1–14.
    van Deursen, A., & Andrade, L. S. (2018). First-and second-level digital divides in Cuba: Differences in Internet motivation, access, skills and usage. First Monday.
    Van Deursen, A. J., & Helsper, E. J. (2015). The third-level digital divide: Who benefits most from being online? In Communication and information technologies annual. Emerald Group Publishing Limited.
    Van Deursen, A. J., Helsper, E. J., & Eynon, R. (2016). Development and validation of the Internet Skills Scale (ISS). Information, Communication & Society, 19(6), 804–823.
    van Deursen, A. J., & van Dijk, J. A. (2014). The digital divide shifts to differences in usage. New Media & Society, 16(3), 507–526. https://doi.org/10.1177/1461444813487959
    van Dijk, J. A. G. M. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4/5), 221–235. https://doi.org/10.1016/j.poetic.2006.05.004
    Vazquez-Lopez, V., & Huerta-Manzanilla, E. L. (2021). Factors Related with underperformance in reading proficiency, the case of the programme for international student assessment 2018. European Journal of Investigation in Health, Psychology and Education, 11(3), 813–828.
    Vigdor, J. L., Ladd, H. F., & Martinez, E. (2014). Scaling the Digital Divide: Home Computer Technology and Student Achievement. Economic Inquiry, 52(3), 1103–1119. https://doi.org/10.3386/w16078
    Von Stumm, S. (2017). Socioeconomic status amplifies the achievement gap throughout compulsory education independent of intelligence. Intelligence, 60, 57–62.
    Wang, D., Zhou, T., & Wang, M. (2021). Information and communication technology (ICT), digital divide and urbanization: Evidence from Chinese cities. Technology in Society, 64, 101516.
    Wei, K.-K., Teo, H.-H., Chan, H. C., & Tan, B. C. (2011). Conceptualizing and testing a social cognitive model of the digital divide. Information Systems Research, 22(1), 170–187.
    Werner, L., Denner, J., Campe, S., & Kawamoto, D. C. (2012). The fairy performance assessment: Measuring computational thinking in middle school. Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, 215–220.
    Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
    Witte, J. C., & Mannon, S. E. (2010). The internet and social inequalitites. Routledge.
    Witten, D., & James, G. (2013). An introduction to statistical learning with applications in R. springer publication.
    Wong, Y. C., Fung, J. Y. C., Law, C. K., Lam, J. C. Y., & Lee, V. W. P. (2009). Tackling the digital divide. British Journal of Social Work, 39(4), 754–767.
    Xiao, Y., & Hu, J. (2019). The moderation examination of ICT use on the association between Chinese mainland students’ socioeconomic status and reading achievement. International Journal of Emerging Technologies in Learning (Online), 14(15), 107.
    Yang, Z., Barnard-Brak, L., & Siwatu, K. (2019). How does the availability of information and communication technology (ICT) resources mediate the relationship between socioeconomic status and achievement? Journal of Technology in Behavioral Science, 4, 262–266.
    Yuen, A. H. K., Lau, W. W. F., Park, J. H., Lau, G. K. K., & Chan, A. K. M. (2016). Digital Equity and Students’ Home Computing: A Hong Kong Study. Asia-Pacific Education Researcher, 25(4), 509–518. https://doi.org/10.1007/s40299-016-0276-3
    Zhang, D., & Liu, L. (2016). How does ICT use influence students’ achievements in math and science over time? Evidence from PISA 2000 to 2012. Eurasia Journal of Mathematics, Science and Technology Education, 12(9), 2431–2449.
    Zhang, Tlili, A., Guo, J., Griffiths, D., Huang, R., Looi, C.-K., & Burgos, D. (2023). Developing rural Chinese children’s computational thinking through game-based learning and parental involvement. The Journal of Educational Research, 1–16.
    Zhong, Z.-J. (2011). From access to usage: The divide of self-reported digital skills among adolescents. Computers & Education, 56(3), 736–746. https://doi.org/10.1016/j.compedu.2010.10.016
    Zhou, Y., & Wang, D. (2015). The Family Socioeconomic Effect on Extra Lessons in Greater China: A Comparison between Shanghai, Taiwan, Hong Kong, and Macao. Asia-Pacific Education Researcher, 24(2), 363–377. https://doi.org/10.1007/s40299-014-0187-0
    Zillien, N., & Hargittai, E. (2009). Digital distinction: Status‐specific types of internet usage. Social Science Quarterly, 90(2), 274–291.
    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。
    侯劭怡 (2022)。國中學生在校成績與教育會考學習成就相關之研究-以高雄市某國中為例〔未出版之碩士論文〕。國立高雄師範大學工業科技教育學。https://hdl.handle.net/11296/79xnsk。
    張芳全 (2021)。國中生會考成績之相關因素探究:以澎湖縣為例。學校行政,133,203–230。
    陳彥君 (2020)。運算思維量表發展之研究〔未出版之碩士論文〕。國立臺北教育大學課程與教學傳播科技研究所教育傳播與科技碩士學位在職進修專班。https://hdl.handle.net/11296/h9yf7j。
    陳順利、黃毅志 (2015)。解除 Coleman 等人報告書的魔咒:學校中的班級因素對學業成績之影響。教育科學研究期刊,60(2),111–138。
    梁瑜芳 (2016)。國三模擬考對國中教育會考成績之影響分析〔未出版之碩士論文〕。國立高雄師範大學數學系。https://hdl.handle.net/11296/m2s23y。
    葉冠和 (2021)。國中教育會考與在校模擬考成績之建模-以臺中市某國中為例〔未出版之碩士論文〕。國立中興大學應用數學所。https://hdl.handle.net/11296/esvpxm。
    趙宥寧 (2022年9月22日)。開學了找不到生科師!某國中30招沒人來 建中也在尋資訊師。聯合新聞網。https://udn.com/news/story/6898/6633534。
    鄭閔聲 (2022年10月25日)。「可去護國神山,誰要當代理教師?」建中都缺人 資訊教師荒怎麼解?。天下雜誌。https://www.cw.com.tw/article/5123276。
    賴尹慧、范斯淳 (2019)。國小教師實施科技領域教學之意願-以高雄市國小為例。科技與人力教育季刊,5(3),33–52。

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