本研究旨在了解臺灣學生的資訊與通訊科技(Information and Communication Technology,簡稱ICT)自我效能(ICT self-efficacy)、數學素養(mathematics literacy)及數學焦慮(mathematics anxiety)之現況，進行不同背景變項的臺灣學生ICT自我效能、數學素養及數學焦慮之差異情形分析，了解ICT自我效能、數學素養及數學焦慮的相關性，分析ICT自我效能對於數學素養的預測力，以及進一步探討數學焦慮對於ICT自我效能及數學素養的中介效果。
本研究採用次級資料分析法，以2022年國際學生能力評量計畫(Programme for International Student Assessment, PISA)資料庫中的臺灣樣本進行分析，研究對象為參與PISA 2022的5518位臺灣15歲學生。且運用IDB analyzer 5.0、SPSS 23.0以及AMOS 28.0統計套裝軟體進行統計分析，包含描述性統計、獨立樣本t檢定、皮爾森積差相關、簡單迴歸分析、多元迴歸分析以及結構方程模式等。研究結論如下所述:
一、臺灣學生的ICT自我效能與數學焦慮情形屬於中上程度，且具有良好的數學素養表現。
二、臺灣學生的ICT自我效能情形會因不同性別及父親教育程度有差異，數學素養會因為不同的父親教育程度而有差異，數學焦慮會因不同性別而有所差異。
三、臺灣學生的ICT自我效能與數學素養兩者為正相關，而ICT自我效能與數學焦慮間以及數學素養與數學焦慮間皆為負相關。
四、臺灣學生的ICT自我效能對數學素養具有預測力。
五、臺灣學生的數學焦慮在ICT自我效能對數學素養具有中介效果。
最後根據本研究的結果提出具體的建議，以提供教育現場的教師、教育行政機關、學術相關研究單位參考。

This study aimed to examine the current status, differences, and correlations between ICT (Information and Communication Technology) self-efficacy, mathematics literacy, and mathematics anxiety among 15-year-old students in Taiwan. Specifically, the study aimed to analyze the predictive role of ICT self-efficacy on mathematics literacy and to explore the mediating effect of mathematics anxiety on the relationship between ICT self-efficacy and mathematics literacy.
This study utilized secondary data from the PISA 2022 database. Various statistical methods were employed to analyze the data, including descriptive statistics, independent samples t-tests, Pearson correlation analysis, simple regression analysis, multiple regression analysis and structural equation modeling. Data analysis was conducted using IDB Analyzer 5.0, SPSS 23.0 and AMOS 28.0 software.
The results revealed that Taiwanese students' ICT self-efficacy and mathematics anxiety were at moderate levels, while their performance in mathematics literacy was relatively strong. Significant gender differences were found in ICT self-efficacy and mathematics anxiety, as well as differences in ICT self-efficacy and mathematics literacy based on fathers' educational background. Additionally, a positive correlation was observed between ICT self-efficacy and mathematics literacy, while ICT self-efficacy and mathematics literacy were negatively correlated with mathematics anxiety.
Further analysis showed that ICT self-efficacy is a significant predictor of mathematics literacy. Moreover, mathematics anxiety partially mediates the relationship between ICT self-efficacy and mathematics literacy.
In conclusion, this study underscores the importance of efforts to enhance students' ICT self-efficacy and mathematics literacy while addressing the issue of high levels of mathematics anxiety among students. These findings suggest a multifaceted approach is necessary to improve students' academic outcomes and emotional well-being.

壹、中文文獻
佘曉清、林煥祥（主編）（2017）。PISA 2015臺灣學生的表現。臺北市：心理。
吳正新、林裕峯、吳添寶（2023）。素養好問題——素養導向評量研發指南。測驗及評量研究中心。新北市：國家教育研究院。
吳明隆、葛建志（2006）。國民小學學生數學歸因信念、數學態度、數學焦慮與數學成就之相關研究。高雄師大學報：自然科學與科技類，(21)，1-18。https://doi.org/10.7060/KNUJST.200612.0001
兒童福利聯盟（2023）。 2023年臺灣兒少學習狀況調查報告。https://www.children.org.tw/publication_research/research_report/2655
林郁婷、龔心怡（2022）。以PISA 2012探究臺灣學生性情經歷因素對數學焦慮之影響。臺灣數學教育期刊，9(2)，33–62。https://doi.org/10.6278/tjme.202210_9(2).002
林素微（2019）。中學生閱讀策略使用與數學素養的關聯及其意涵。測驗學刊，66(3)，213-248。https://www.airitilibrary.com/Article/Detail?DocID=16094905-201909-201910080015-201910080015-213-248
洪兆祥（2018）。中介效果的時序設計與因果推論。〔博士論文，國立政治大學〕。華藝線上圖書館。https://doi.org/10.6814/DIS.NCCU.EDU.020.2018.F02
洪碧霞(主編)、鄒慧英、陸怡琮、林素微、林哲彥、江培銘、吳康民、王長勝（2021）。PISA 2018臺灣學生的表現。新北市：心理。
張芳全（2021）。高中生數學素養表現因素之分析：以自我教育期望與數學學習動機為中介變項。學校行政，(134)，23-58。https://doi.org/10.6423/HHHC.202107_(134).0002
張春興（1989）。張氏心理學辭典。臺北市：東華。
教育部（2014）。十二年國民基本教育課程綱要︰總綱。臺北市：教育部。
教育部（2018）。十二年國民基本教育課程綱要國民中小學暨普通型高級中等學校數學領域。https://www.naer.edu.tw/PageSyllabus?fid=52
教育部（2021）。因應疫情停課居家線上學習規劃。https://ws.moe.edu.tw/Download.ashx?u=C099358C81D4876C725695F2070B467E8B81ED614D7AF43E6382B9A0A99D0B5C1425AB55F4FDBA0BFDF2C82F543B4B4E857D56294DEE11A8CE3695DF703DA6B012CAD8B401B6F1B0B950EF79757887E3&n=CADBA6C5A30D516A63E541EA4E73904DAC2BCBCA02D9B5FA01AD4BB2905AA0F7D90F6EF499977D188F8FE0F80B8200C4&icon=..pdf
教育部（2023）。教育部中小學數位教學指引2.0版。https://ite.nccu.edu.tw/upload/41/doc/26380/%E6%95%99%E8%82%B2%E9%83%A8%E4%B8%AD%E5%B0%8F%E5%AD%B8%E6%95%B8%E4%BD%8D%E6%95%99%E5%AD%B8%E6%8C%87%E5%BC%952.0%E7%89%88.pdf
教育部（2024）。教育部部史—重大教育政策發展歷程之數位教學與學習。https://history.moe.gov.tw/Policy/Detail/1e540341-4d3b-44aa-bea7-17e55d6f26b5
陳姿螢（2018）。學生對於數學教師教學看法與數學焦慮之關聯性－以PISA 2012臺灣學生為例。教育學誌，(39)，55-85。https://www.airitilibrary.com/Article/Detail?DocID=20713126-201805-201807130020-201807130020-55-85
單維彰（2016）。素養、課程與教材—以數學為例。教育脈動電子期刊，5，19-37。https://www.airitilibrary.com/Article/Detail?DocID=P20160218002-201602-201603110030-201603110030-c1-19
溫雅婷（2020）。探討素養導向之教科書所培養的數學素養—以對數單元為例。﹝碩士論文。國立臺灣師範大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/9z5ax3
臺灣PISA國家研究中心（2014）。臺灣PISA2012精簡報告。臺灣PISA國家研究中心。臺南：臺南大學。
臺灣PISA國家研究中心（2022）。評量架構（中）_PISA2022數學評量架構。臺灣PISA國家研究中心。臺北：臺灣師範大學。https://cirn.moe.edu.tw/WebContent/index.aspx?sid=1224&mid=16299
臺灣PISA國家研究中心（2023）。臺灣PISA 2022成果報告會議簡報。臺灣PISA國家研究中心。臺北：臺灣師範大學。https://cirn.moe.edu.tw/WebFile/index.aspx?sid=1224&mid=15766
臺灣PISA國家研究中心（2024）。臺灣 PISA 2025 01/25 預試宣導會議簡報。臺灣PISA國家研究中心。臺北：臺灣師範大學。https://cirn.moe.edu.tw/WebFile/index.aspx?sid=1224&mid=15766
劉玉玲（2016）。國中生數學學業情緒及數學學習策略與數學學業成就之研究。課程與教學，19(2)，161-192。https://doi.org/10.6384/CIQ.201604_19(2).0007
劉玉玲（2017）。學生的數學素養由教師陶冶－教師的素養誰關照。臺灣教育評論月刊，6(3)，54-60。
劉玉玲、沈淑芬（2015）。數學自我概念、數學學習策略、數學學業情緒與數學學業成就之研究－自我提升模式觀點。教育心理學報，46(4)，491-516。https://doi.org/10.6251/BEP.20140716
劉育春、陳柏霖、洪兆祥（2017）。以數學學業復原力為調節角色：國中生數學考試焦慮與數學學業成就之關係。課程與教學，20(4)，151-176。https://doi.org/10.6384/CIQ.201710_20(4).0006
鄧藩小璃、阮玉錦（2023）。父母教育水平對越南學生數學成績的影響：PISA 2018的調查結果。台灣教育研究期刊，4(5)，233-254。
鄭章華、林佳慧（2021）。從數學教科書設計初探素養導向理念之轉化。台灣教育研究期刊，2(3)，27-46。
謝豐瑞（2018）。2018 中華民國數學年會特色展區工作坊暨論壇簡報內容：數學素養評量。臺北市：國立臺灣師範大學。
蘇泓誠、陳佳欣（2022）。不同心態之臺灣學生成就動機與數學素養關聯研究。臺灣數學教育期刊，9(2)，63-86。https://doi.org/10.6278/tjme.202210_9(2).003
貳、外文文獻
Adimora, D. E., Nwokenna, E. N., Omeje, J. C., & Eze, U. N. (2015). Influence of Socio-Economic Status and Classroom Climate on Mathematics Anxiety of Primary School Pupils. Procedia - Social and Behavioral Sciences, 205, 693-701. https://doi.org/10.1016/j.sbspro.2015.09.109
Aesaert, K., & van Braak, J. (2014). Exploring factors related to primary school pupils’ ICT self-efficacy: A multilevel approach. Computers in Human Behavior, 41, 327-341. https://doi.org/10.1016/j.chb.2014.10.006
Aesaert, K., van Braak, J., van Nijlen, D., & Vanderlinde, R. (2015). Primary school pupils' ICT competences: Extensive model and scale development. Computers & Education, 81, 326-344. https://doi.org/10.1016/j.compedu.2014.10.021
Aesaert, K., Van Nijlen, D., Vanderlinde, R., Tondeur, J., Devlieger, I., & van Braak, J. (2015). The contribution of pupil, classroom and school level characteristics to primary school pupils' ICT competences: A performance-based approach. Computers & Education, 87, 55-69. https://doi.org/10.1016/j.compedu.2015.03.014
Aldrup, K., Klusmann, U., & Lüdtke, O. (2020). Reciprocal associations between students’ mathematics anxiety and achievement: Can teacher sensitivity make a difference? Journal of Educational Psychology, 112(4), 735–750. https://doi.org/10.1037/edu0000398
Areepattamannil, S., & Khine, M. S. (2017). Early adolescents’ use of information and communication technologies (ICTs) for social communication in 20 countries: Examining the roles of ICT-related behavioral and motivational characteristics. Computers in Human Behavior, 73, 263-272. https://doi.org/10.1016/j.chb.2017.03.058
Ashcraft, M. H. (2002). Math Anxiety: Personal, Educational, and Cognitive Consequences. Current Directions in Psychological Science, 11(5), 181-185. https://doi.org/10.1111/1467-8721.00196
Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130(2), 224–237. https://doi.org/10.1037/0096-3445.130.2.224
Aydin, M. (2021). Does the digital divide matter? Factors and conditions that promote ICT literacy. Telematics and Informatics, 58, 101536. https://doi.org/10.1016/j.tele.2020.101536
Bandura, A. (1993). Perceived Self-Efficacy in Cognitive Development and Functioning. Educational Psychologist, 28(2), 117–148. https://doi.org/10.1207/s15326985ep2802_3
Bandura, A. (1997). Self-Efficacy: The Exercise of Control. W.H. Freeman and Company, New York.
Bentler, P. M. (1982). Confirmatory factor analysis via noniterative estimation: A fast inexpensive method. Journal of Marketing Research, 19, 417-424.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606.
Bollen, K. A. (1989). Structural equation modeling with latent variables. New York: John Wiley.
Bollen, K. A., & Stine, R. (1990). Direct and Indirect Effects: Classical and Bootstrap Estimates of Variability. Sociological Methodology, 20, 115–140. https://doi.org/10.2307/271084
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp.136-162). Newbury Park, CA: Sage.
Cassady, J. C., & Johnson, R. E. (2002). Cognitive test anxiety and academic performance. Contemporary Educational Psychology, 27, 270-295. https://doi.org/10.1006/ceps.2001.1094
Caviola, S., Primi, C., Chiesi, F., & Mammarella, I. C. (2017). Psychometric properties of the Abbreviated Math Anxiety Scale (AMAS) in Italian primary school children. Learning and Individual Differences, 55, 174-182.
Chen, Q., & Ma, Y. (2022). The influence of teacher support on vocational college students’ information literacy: The mediating role of network perceived usefulness and information and communication technology self-efficacy. Frontiers in Psychology,13,1032791. https://doi.org/10.3389/fpsyg.2022.1032791
Chen, X., & Hu, J. (2020). ICT-related behavioral factors mediate the relationship between adolescents’ ICT interest and their ICT self-efficacy: Evidence from 30 countries. Computers & Education, 159, 104004. https://doi.org/10.1016/j.compedu.2020.104004
Ching, B. H.-H. (2017). Mathematics anxiety and working memory: Longitudinal associations with mathematical performance in Chinese children. Contemporary Educational Psychology, 51, 99-113. https://doi.org/10.1016/j.cedpsych.2017.06.006
Christoph, G., Goldhammer, F., Zylka, J., & Hartig, J. (2015). Adolescents' computer performance: The role of self-concept and motivational aspects. Computers & Education, 81, 1-12. https://doi.org/10.1016/j.compedu.2014.09.004
Clark, R. C., & Mayer, R. E. (2008). E-learning and the science of instruction(2nd ed.). San Francisco, CA : Pfeiffer .
Cockroft, W. H. (1982). Mathematics Counts, Report of the Committee of Inguiry Into the Teaching of Mathematics in Schools. London: Her Majesty’s Stationery Office.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688
Costado, T., & Piñero, J. (2024). Mathematical Anxiety among Primary Education Degree Students in the Post-Pandemic Era: A Case Study. Education Sciences, 14, 171. https://doi.org/10.3390/educsci14020171
Courtney, M., Karakus, M., Ersozlu, Z., & Nurumov, K. (2022). The influence of ICT use and related attitudes on students’ math and science performance: multilevel analyses of the last decade’s PISA surveys. Large-scale Assessments in Education, 10(1), 8. https://doi.org/10.1186/s40536-022-00128-6
Feng, L. & He, L. & Ding, J. (2023). The Association between Perceived Teacher Support, Students’ ICT Self-Efficacy, and Online English Academic Engagement in the Blended Learning Context. Sustainability, 15(8),6839. https://doi.org/10.3390/su15086839
Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Gebhardt, E. (2014). Students’ Use of and Engagement with ICT at Home and School. In J. Fraillon, J. Ainley, W. Schulz, T. Friedman, & E. Gebhardt (Eds.), Preparing for Life in a Digital Age: The IEA International Computer and Information Literacy Study International Report (pp. 125-166). Springer International Publishing. https://doi.org/10.1007/978-3-319-14222-7_6
Frenzel, A. C., Pekrun, R., & Goetz, T. (2007a). Perceived learning environment and Students’ emotional experiences: A multilevel analysis of mathematics classrooms. Learning and Instruction, 17(5), 478-493. https://doi.org/10.1016/j.learninstruc.2007.09.001
Frenzel, A. C., Pekrun, R., & Goetz, T. (2007b). Girls and mathematics – A “hopeless” issue? A control-value approach to gender differences in emotions towards mathematics. European Journal of Psychology of Education, 22(4),497-514. https://doi.org/10.1007/BF03173468
Geary, D. C., Hoard, M. K., Nugent, L., Chu, F., Scofield, J. E., & Ferguson Hibbard, D. (2019). Sex differences in mathematics anxiety and attitudes: Concurrent and longitudinal relations to mathematical competence. Journal of Educational Psychology, 111(8), 1447–1461. https://doi.org/10.1037/edu0000355
Goetz, T., Frenzel, A. C., & Hall, N. C. (2007). Between- and within domain relations of students’ academic emotions. Journal of Educational Psychology,99(4), 715-723. https://doi.org/10.1037/0022-0663.99.4.715
Goetz, T., Pekrun, R., Hall, N., & Haag, L. (2006). Academic emotions from a social‐cognitive perspective: Antecedents and domain specificity of students’ affect in the context of Latin instruction. British Journal of Educational Psychology,76(2), 289-308. https://doi.org/10.1348/000709905X42860
Goos, M., Geiger, V., & Dole, S. (2012). Auditing the numeracy demands of the Australian curriculum. In J. Dindyal, L. P. Cheng & S. F. N. (Eds.), Mathematics education: Expanding horizons: Proceedings of the 35th annual conference of the Mathematics Education Research Group of Australasia (pp. 314‒321). Singapore: Merga.
Gunderson, E. A., Park, D., Maloney, E. A., Beilock, S. L., & Levine, S. C. (2018). Reciprocal relations among motivational frameworks, math anxiety, and math achievement in early elementary school. Journal of Cognition and Development, 19(1), 21–46. https://doi.org/10.1080/15248372.2017.1421538
Hatlevik, O. E. (2016). Examining the Relationship between Teachers’ Self-Efficacy, their Digital Competence, Strategies to Evaluate Information, and use of ICT at School. Scandinavian Journal of Educational Research, 61(5), 555–567. https://doi.org/10.1080/00313831.2016.1172501
Hatlevik, O. E., Throndsen, I., Loi, M., & Gudmundsdottir, G. B. (2018). Students’ ICT self-efficacy and computer and information literacy: Determinants and relationships. Computers & Education, 118, 107-119. https://doi.org/10.1016/j.compedu.2017.11.011
Hiller, S. E., Kitsantas, A., Cheema, J. E., & Poulou, M. (2021). Mathematics anxiety and self-efficacy as predictors of mathematics literacy. International Journal of Mathematical Education in Science and Technology, 53(8), 2133–2151. https://doi.org/10.1080/0020739X.2020.1868589
Hong, J. C., & Tai, T. Y. (2024). Exploring the role of internet self-efficacy, perceived enjoyment, and anxiety on learning outcome in intelligent personal assistant-based EFL learning. Innovation in Language Learning and Teaching, 1–19. https://doi.org/10.1080/17501229.2024.2347884
Hopko, D. R., Mahadevan, R., Bare, R. L., & Hunt, M. K. (2003). The Abbreviated Math Anxiety Scale (AMAS): Construction, Validity, and Reliability. Assessment, 10(2), 178-182. https://doi.org/10.1177/1073191103010002008
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. S. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1-13. https://doi.org/10.1016/j.compedu.2018.05.021
Huang, S., Jiang, Y., Yin, H., & Jong, M. S.-y. (2021). Does ICT use matter? The relationships between students' ICT use, motivation, and science achievement in East Asia. Learning and Individual Differences, 86, 101957. https://doi.org/10.1016/j.lindif.2020.101957
Jewsbury, P.A., Jia, Y. & Gonzalez, E.J. (2024). Considerations for the use of plausible values in large-scale assessments. Large-scale Assessments in Education, 12, (1), 1-27. https://doi.org/10.1186/s40536-024-00213-y
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575
Krinzinger, H., Kaufmann, L., & Willmes, K. (2009). Math Anxiety and Math Ability in Early Primary School Years. Journal of Psychoeducational Assessment, 27(3), 206-225. https://doi.org/10.1177/0734282908330583
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
Linnenbrink, E. A. (2006). Emotion research in education: Theoretical and methodological perspectives on the integration of affect, motivation, and cognition. Educational Psychology Review, 18(4), 307–314. https://doi.org/10.1007/s10648-006-9028-x
Liu, H., Liu, Q., Du, X., Liu, J., Hoi, C. K. W., & Schumacker, R. E. (2023). Teacher-student relationship as a protective factor for socioeconomic status, students’ self-efficacy and achievement: a multilevel moderated mediation analysis. Current Psychology, 42(4), 3268-3283. https://doi.org/10.1007/s12144-021-01598-7
Lomax, R.G. (2004). A Beginner's Guide to Structural Equation Modeling: Fourth Edition (2nd ed.). Psychology Press. https://doi.org/10.4324/9781410610904
Lukowski, S. L., DiTrapani, J., Jeon, M., Wang, Z., J.Schenker, V., Doran, M. M., Hart, S. A., Mazzocco, M. M. M., Willcutt, E. G., Thompson, L. A., & Petrill, S. A. (2019). Multidimensionality in the measurement of math-specific anxiety and its relationship with mathematical performance. Learning and Individual Differences, 70, 228-235. https://doi.org/10.1016/j.lindif.2016.07.007
Luttenberger, S., Wimmer, S., & Paechter, M. (2018). Spotlight on math anxiety. Psychology Research and Behavior Management, 11, 311–322. https://doi.org/10.2147/PRBM.S141421
Marks, G. N. (2008). Are Father’s or Mother’s Socioeconomic Characteristics More Important Influences on Student Performance? Recent International Evidence. Social Indicators Research, 85(2), 293–309. http://www.jstor.org/stable/27734583
McDonald, R. P., & Marsh, H. M. (1990). Choosing a multivariate model: Noncentrality and goodness-of-fit. Psychological Bulletion, 107, 247-255.
Mulaik, S. A., James, L. R., Van Altine, J., Bennett, N., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105, 430-445.
Murphy, C. A., Coover, D., & Owen, S. V. (1989). Development and Validation of the Computer Self-Efficacy Scale. Educational and Psychological Measurement, 49(4), 893-899. https://doi.org/10.1177/001316448904900412
Namkung, J. M., Peng, P., & Lin, X. (2019). The Relation Between Mathematics Anxiety and Mathematics Performance Among School-Aged Students: A Meta-Analysis. Review of Educational Research, 89(3), 459-496. https://doi.org/10.3102/0034654319843494
National Council of Teachers of Mathematics. (1989). Curriculum and evaluation standards for school mathematics. Author.
National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Author.
OECD (2004), The PISA 2003 Assessment Framework: Mathematics, Reading, Science and Problem Solving Knowledge and Skills, PISA, OECD Publishing, Paris, https://doi.org/10.1787/9789264101739-en.
OECD (2009), PISA Data Analysis Manual: SPSS, Second Edition, PISA, OECD Publishing, Paris, https://doi.org/10.1787/9789264056275-en.
OECD (2013), PISA 2012 Assessment and Analytical Framework: Mathematics, Reading, Science, Problem Solving and Financial Literacy, PISA, OECD Publishing, Paris, https://doi.org/10.1787/9789264190511-en.
OECD (2014). PISA 2012 results: What students know and can do - Student performance in mathematics, reading and science (Volume I, Revised edition, February 2014) ,PISA, OECD Publishing, Paris, https://doi.org/10.1787/9789264208780-en.
OECD (2016). PISA 2015 results (Volume I): Excellence and equity in education, PISA, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264266490-en
OECD (2019). PISA 2018 results (Volume I): What students know and can do, PISA, OECD Publishing, Paris, https://doi.org/10.1787/5f07c754-en
OECD (2023a), PISA 2022 Results (Volume I): The State of Learning and Equity in Education, PISA, OECD Publishing, Paris, https://doi.org/10.1787/53f23881-en.
OECD (2023b). PISA 2022 database. Retrieved from https://www.oecd.org/pisa/data/2022database/
OECD (2023c), PISA 2022 Assessment and Analytical Framework, PISA, OECD Publishing, Paris, https://doi.org/10.1787/dfe0bf9c-en.
OECD (2023d), PISA 2022 RESULTS: FACTSHEETS – CHINESE TAIPEI. Retrieved from https://www.oecd.org/publication/pisa-2022-results/country-notes/chinese-taipei-ebda1f30/
OECD (2023e), PISA 2022 Mathematics test question - other languages. Retrieved from https://www.oecd.org/pisa/test/pisa-2022-mathematics-test-questions.htm
OECD (2024), PISA 2022 Technical Report, PISA, OECD Publishing, Paris, https://doi.org/10.1787/01820d6d-en.
Pajares, F. (1996). Self-efficacy beliefs in academic settings [Article]. Review of Educational Research, 66(4), 543-578. https://doi.org/10.3102/00346543066004543
Pajares, F., & Miller, M. D. (1994). Role of Self-Efficacy and Self-Concept Beliefs in Mathematical Problem Solving: A Path Analysis [Article]. Journal of Educational Psychology, 86(2), 193-203. https://doi.org/10.1037/0022-0663.86.2.193
Pajares, F., & Schunk, D. H. (2002). Chapter 1 - Self and Self-Belief in Psychology and Education: A Historical Perspective. In J. Aronson (Ed.), Improving Academic Achievement (pp. 3-21). Academic Press. https://doi.org/10.1016/B978-012064455-1/50004-X
Pekrun, R. (2000). A social-cognitive, control-value theory of achievement emotions. In J. Heckhausen (Ed.), Motivational psychology of human development: Developing motivation and motivating development (pp. 143–163). Elsevier Science. https://doi.org/10.1016/S0166-4115(00)80010-2
Pekrun, R. (2008). Assumptions of the control-value theory of achievement emotions. International Journal of Psychology, 43(3-4), 385. https://doi.org/10.1080/00207594.2008.10108485
Pekrun, R., Elliot, A. J., & Maier, M. A. (2006). Achievement goals and discrete achievement emotions: A theoretical model and prospective test. Journal of Educational Psychology. 98(3), 583-597.
Pekrun, R., Goetz, T., & Titz, W. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2),91-105.
Pekrun, R., Goetz, T., Perry, R. P., Kramer, K., Hochstadt, M., & Molfenter, S. (2004). Beyond test anxiety:Development and validation of the test emotions questionnaire (TEQ). Anxiety, Stress, and Coping,17(3), 287–316. https://doi.org/10.1080/10615800412331303847
Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis. SAGE Publications, Inc. https://doi.org/10.4135/9781412984898
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and Self-Regulated Learning Components of Classroom Academic Performance [Article]. Journal of Educational Psychology, 82(1), 33-40. https://doi.org/10.1037/0022-0663.82.1.33
Pintrich, P. R., & Schunk, D. H. (2002). Motivation in Education. Englewood Cliffs, NJ: Prentice Hall.
Pirrone, C., Di Corrado, D., Privitera, A., Castellano, S., Varrasi, S. (2022). Students’ Mathematics Anxiety at Distance and In-Person Learning Conditions during COVID-19 Pandemic: Are There Any Differences? An Exploratory Study. Education Sciences, 12(6):379. https://doi.org/10.3390/educsci12060379
Posamentier, A. S., Kose, G., Virgadamo, D. S., & Kathleen, K. C. (2019). The Psychology of Problem Solving: The Background to Successful Mathematics Thinking. World Scientific Publishing Co. https://doi.org/10.1142/11426
Pugalee, D. K. (1999). Constructing a model of mathematical literacy. The Clearing House, 73(1), 19‒22.
Ramirez, G., Chang, H., Maloney, E. A., Levine, S. C., & Beilock, S. L. (2016). On the relationship between math anxiety and math achievement in early elementary school: The role of problem solving strategies. Journal of Experimental Child Psychology, 141, 83-100. https://doi.org/10.1016/j.jecp.2015.07.014
Richardson, E., & Suinn, R. M. (1972). The mathematics anxiety rating scale:Psychometric data. Journal of Counseling Psychology, 19, 551-554. https://doi.org/10.1037/h0033456
Rohatgi, A., Scherer, R., & Hatlevik, O. E. (2016). The role of ICT self-efficacy for students' ICT use and their achievement in a computer and information literacy test. Computers & Education, 102, 103-116. https://doi.org/10.1016/j.compedu.2016.08.001
Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling (2nd ed.). Lawrence Erlbaum Associates.
Tømte, C., & Hatlevik, O. E. (2011). Gender-differences in Self-efficacy ICT related to various ICT-user profiles in Finland and Norway. How do self-efficacy, gender and ICT-user profiles relate to findings from PISA 2006. Computers & Education, 57(1), 1416-1424. https://doi.org/10.1016/j.compedu.2010.12.011
Tsai, M. J., & Tsai, C. C. (2003). Information searching strategies in Web-based science learning: The role of Internet self-efficacy [Article]. Innovations in Education and Teaching International, 40(1), 43-50. https://doi.org/10.1080/1355800032000038822
Vekiri, I., & Chronaki, A. (2008). Gender issues in technology use: Perceived social support, computer self-efficacy and value beliefs, and computer use beyond school. Computers & Education, 51(3), 1392-1404. https://doi.org/10.1016/j.compedu.2008.01.003
Wang, Z., Borriello, G. A., Oh, W., Lukowski, S., & Malanchini, M. (2021). Co-development of math anxiety, math self-concept, and math value in adolescence: The roles of parents and math teachers. Contemporary Educational Psychology, 67, 102016. https://doi.org/10.1016/j.cedpsych.2021.102016
Wang, Z., Lukowski, S. L., Hart, S. A., Lyons, I. M., Thompson, L. A., Kovas, Y., Mazzocco, M. M., Plomin, R., & Petrill, S. A. (2015). Is Math Anxiety Always Bad for Math Learning? The Role of Math Motivation. Psychological science, 26(12), 1863–1876. https://doi.org/10.1177/0956797615602471
Yang, X., Chen, Z., Dong, J., & Tan, D. (2024). How is the conceptual level of students associated with learning outcomes in math? Testing the mediating role of math anxiety and the moderating role of motivating style. Current Psychology,43,19548–19561.https://doi.org/10.1007/s12144-024-05759-2
Yu, H., & Hu, J. (2022). ICT Self-Efficacy and ICT Interest Mediate the Gender Differences in Digital Reading: A Multilevel Serial Mediation Analysis. International Journal of Emerging Technologies in Learning (iJET), 17(05), pp. 211–225. https://doi.org/10.3991/ijet.v17i05.25691