臺灣博碩士論文加值系統

本研究使用 PISA 2022 年的資料庫，探討資訊科技融入教學對於學生數學素養與
科學素養表現之影響。使用學生問卷、ICT 問卷、學校問卷，樣本經遺漏值處理後，
共計學生人數 5814 位、學校數 182 所。本研究以科技接受模型為基礎架構，透過模型
選取適合的 ICT 變項，利用多層次模型進行分析，探討學生層次變項對數學及科學素
養表現影響、學校層次變項對數學及科學素養表現影響，以及學校層次變項與學生層
次變項間是否具有跨層次的交互作用影響。研究結果如下：
一、學生層次變項中，「ICT 設備使用品質」、「ICT 數位資源品質」、「ICT 學校資源」、
「ICT 資源」、「ICT 的自我效能」、「學生辨別網路資訊方面的能力」、「學生對於學
校使用 ICT 規定的看法」、「課堂外使用 ICT 進行學校活動」、「平日使用 ICT 的頻
率」、「假日使用 ICT 的頻率」對學生數學素養表現有顯著影響。
二、學校層次變項中，「學校提供遠距教學支持的程度」對學生數學素養表現有顯著影
響。
三、學校層次中的「學校提供遠距教學支持的程度」與學生層次的「ICT 數位資源品
質」對數學素養表現具有跨層次的交互作用影響。
四、學生層次變項中，「ICT 設備使用品質」、「ICT 數位資源品質」、「ICT 學校資源」、
「ICT 資源」、「ICT 的自我效能」、「學生辨別網路資訊方面的能力」、「學生對於學
校使用 ICT 規定的看法」、「課堂外使用 ICT 進行學校活動」、「平日使用 ICT 的頻
率」對學生科學素養表現有顯著影響。
五、學校層次變項中，「學校提供遠距教學支持的程度」對學生科學素養表現有顯著影
響。
六、學校層次中的「學校提供遠距教學支持的程度」與學生層次的「ICT 數位資源品
質」與「ICT 資源」對科學素養表現具有跨層次的交互作用影響。
本研究根據研究結果，提出實務學習與未來研究兩個部份的相關建議。實務學習方面，如鼓勵師生利用網路優勢進行教學活動與完成任務作業等，以此熟悉運用數位
資源並作為學習的管道，另外學校可加強 ICT 設備資源品質，提供學生穩定的網路資
源品質與速度的學習環境；未來研究方面，如後續研究者可嘗試進行不同國家的比較，
探討我國與其他國家使用 ICT 的變項於數學素養及科學素養研究結果，找出教育政策
上借鏡的地方。

This study utilized data from the Programme for International Student Assessment
(PISA) 2022 to explore the impact of integrating information and communication technology
(ICT) into teaching students mathematical and scientific literacy. The data were collected
through student, ICT, and school questionnaires. After handling missing values, the sample
comprised 5,814 students from 182 schools.
This study selected appropriate ICT variables based on the Technology Acceptance
Model (TAM). It employed a multilevel model to analyze the effects of student-level variables
on mathematical and scientific literacy, the effects of school-level variables on mathematical
and scientific literacy, and whether there are cross-level interactions between school-level and
student-level variables. The findings are as follows:
1. Among student-level variables, "quality of ICT equipment usage," "quality of ICT digital
resources," "ICT school resources," "ICT resources," "ICT self-efficacy," "students' ability
to discern online information," "students' views on school ICT usage regulations," "use of
ICT for school activities outside the classroom," "frequency of ICT use on weekdays," and
"frequency of ICT use on holidays" have significant effects on students' mathematical
literacy.
2. Among school-level variables, "Problems with Schools’ Capacity to Provide Remote
Instruction" significantly influences students' mathematical literacy performance.
3. A significant cross-level interaction effect exists between the school-level variables,
"Problems with Schools’ Capacity to Provide Remote Instruction," and the student-level
variables, "Quality of ICT digital resources," on students' mathematical literacy.
4. Among student-level variables, "quality of ICT equipment usage," "quality of ICT digital
resources," "ICT school resources," "ICT resources," "ICT self-efficacy," "students' ability to discern online information," "students' views on school ICT usage regulations," "use of
ICT for school activities outside the classroom," and "frequency of ICT use on weekdays"
have significant effects on students' scientific literacy.
5. Among school-level variables, "Problems with Schools’ Capacity to Provide Remote
Instruction" significantly affect students' scientific literacy.
6. There exists a significant cross-level interaction effect between the school-level variables,
"Problems with Schools’ Capacity to Provide Remote Instruction," and the student-level
variables, "Quality of ICT digital resources" and "ICT resources," on students' scientific
literacy.
This study proposes practical learning and future research recommendations based on
the findings. In terms of practical learning, suggestions include encouraging teachers and
students to utilize the advantages of the internet for teaching activities and completing
assignments, thus familiarizing themselves with digital resources and using them as learning
channels. Additionally, schools can enhance the quality of ICT equipment resources to
provide students with a stable learning environment with reliable internet resources and speed.
Regarding future research, subsequent researchers could attempt comparative studies
across different countries to investigate the variables of ICT usage in mathematics literacy
and scientific literacy research results, identifying areas for educational policy reference.

摘要
Abstract
目錄
表目錄
圖目錄
第一章 緒論
第一節 研究背景與動機
第二節 研究目的
第三節 名詞釋義
第四節 研究範圍與限制
第二章 文獻探討
第一節 科技接受模型
第二節 PISA的資料庫與科技輔助學習之相關研究
第三節 多層次模型
第三章 研究方法
第一節 研究架構與研究流程
第二節 研究對象
第三節 研究變項
第四節 資料處理與分析
第四章 研究結果
第一節 學生於數學素養之不同層次分析結果
第二節 學生於科學素養之不同層次分析結果
第五章 結論與建議
第一節 研究結論
第二節 研究建議
參考文獻
中文部分
英文部分
附錄
附錄一 PISA 2022 學生ICT問卷題目
附錄二 PISA 2022 學生問卷題目
附錄三 PISA 2022 學校問卷題目
附錄四 共線性診斷評估

牛育靖 (2020)。以階層線性模型探討 PISA 2018 資訊與通信科技對臺灣學生學科素養之影響。〔碩士論文，國立臺北科技大學〕。臺灣博碩士論文知識加值系統。
吳思華 (2016)。2016-2020資訊教育總藍圖。教育部。https://ws.moe.edu.tw/001/Upload/3/relfile/6315/46563/65ebb64a-683c-4f7a-bcf0-325113ddb436.pdf
余曉清、林煥祥 (2017)。PISA 2015臺灣學生的表現。心理。
林素微、吳康民、王長勝 (2021)。臺灣學生的科學素養表現。心理。
洪淑華、黃靖文（2022）。基於科技接受模式探討國小學生因材網使用意圖之研究。學校行政，(139)，114-133。
陳怡琄、鍾璧徽、鮑慧文 (2022)。以科技接受模型探討民眾對數位金融平台接受度之研究－以行動銀行為例。管理資訊計算，11，122-136。
孫玉喬、陳俐文、陳棟樑 (2021)。運用科技接受模式探討使用Microsoft Teams進行遠距教學之學生學習滿意度。兩岸職業教育論叢，5(1)，37-52。
郭靜燕、鄭舜平、張舒婷、賴雪鈴、詹前隆 (2018)。 應用結合科技接受模式與計畫行為理論的概念架構推動電子病歷之經驗-以某區域教學醫院為例。醫學與健康期刊，7(1)，65-75。
莊楹蕿 (2017)。 影響臺灣八年級學生數學學習成就因素之多層次模型分析。〔博士論文。國立臺北教育大學〕臺灣碩博士論文知識加值系統。
溫福星 (2006)。階層線性模式—原理、方法與應用。雙葉書廊。
溫福星、邱皓政 (2009)。多層次模式方法論: 階層線性模式的關鍵議題與試解。臺大管理論叢，19(2)，263-293。
劉宜衡 (2023)。以階層線性模式(HLM)探討不同背景變項對臺灣 15 歲學生科學素養成就之影響－PISA 2015 資料分析。﹝碩士論文。國立臺北教育大學﹞臺灣博碩士論文知識加值系統。
劉虹秀 (2023)。PISA 2018臺灣學生閱讀素養表現之影響因素診斷與比較研究〔碩士論文，國立臺中教育大學〕。國立臺中教育大學機構典藏。
賴宜弘、黃芬芬、楊雪華 (2015)。科技接受模式中文版量表之編製與相關研究。亞東學報，(35)，201-221。
Atasoy, R., Çoban, Ö., & YATAĞAN, M. (2022). Effect of ICT Use, Parental Support and Student Hindering on Science Achievement: Evidence from PISA 2018.Journal of Learning and Teaching in Digital Age,7(2), 127-140. https://dergipark.org.tr/en/pub/joltida
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://link.springer.com/
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Eribaum. https://www.utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. http://www.jstor.org/stable/2632151
Enders, C. K., & Tofight, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at old issue. Psychological Methods, 12(2), 121-138. https://doi.org/10.1037/1082-989X.12.2.121
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.
Hussein, Z. (2017). Leading to intention: The role of attitude in relation to technology acceptance model in e-learning. Procedia Computer Science, 105, 159-164.
Johari, N., Mustaffha, H., Ripain, N., Zulkifli, A., & Ahmad, N. W. (2015, May). Student Acceptence of online learning in KUIS. In International Conference on Economics and Banking (iceb-15), 326-335.
Kim, M., & Kim, H. (2023). Profiles of Students’ ICT Use in High-Performing Countries in PISA 2018. Computers in the Schools, 1-20. https://doi.org/10.1080/07380569.2023.2180338
Kong, Y., Seo, Y. S., & Zhai, L. (2022). ICT and digital Reading achievement: A cross-national comparison using PISA 2018 data. International Journal of Educational Research, 111, 101912. https://doi.org/10.1016/j.ijer.2021.101912
Lezhnina, O., & Kismihók, G. (2022). Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA. International Journal of Research & Method in Education, 45(2), 180-199. https://doi.org/10.1080/1743727X.2021.1963226
Lazim, C. S. L. M., Ismail, N. D. B., & Tazilah, M. D. A. K. (2021). Application of technology acceptance model (TAM) towards online learning during covid-19 pandemic: Accounting students perspective. Int. J. Bus. Econ. Law, 24(1), 13-20.
Mustafa, A. S., & Garcia, M. B. (2021, November). Theories integrated with technology acceptance model (TAM) in online learning acceptance and continuance intention: A systematic review. In 2021 1st Conference on online teaching for mobile education (OT4ME) (pp. 68-72). IEEE. https://doi.org/10.1109/OT4ME53559.2021.9638934
Namome, C., & Moodley, M. (2021). ICT in mathematics education: an HLM analysis of achievement, access to and use of ICT by African Middle School Students. SN Social Sciences, 1(9), 224. https://doi.org/10.1007/s43545-021-00230-6
OECD (2009). PISA 2009 assessment framework-Key Competencies in Reading,Mathematics and Science, OECD Publishing. https://doi.org/10.1787/9789264062658-en
OECD (2017). PISA 2015 Assesment and Analytical Framework: Science, Reading, Mathematic and Financial Literacy, OECD Publishing. https://doi.org/10.1787/9789264281820-en
OECD (2018). PISA 2022 Assesment and Analytical Framework: PISA 2022 Mathematics Framework. OECD Publishing. https://doi.org/10.1787/dfe0bf9c-en
OECD (2023). PISA 2022 Assessment and Analytical Framework, OECD Publishing. https://doi.org/10.1787/dfe0bf9c-en
OECD (2024). PISA 2022 Technical Report. OECD Publishing. https://doi.org/10.1787/01820d6d-en
Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G., & Hanson, J. (2016). Attitude, digital literacy and self-efficacy: Flow-on effects for online learning behavior. The Internet and Higher Education, 29, 91-97.
Rosli, M. S., Saleh, N. S., Md. Ali, A., Abu Bakar, S., & Mohd Tahir, L. (2022). A Systematic review of the technology acceptance model for the sustainability of higher education during the COVID-19 pandemic and identified research gaps. Sustainability, 14(18), 11389. https://doi.org/10.3390/su141811389
Siti, C., Md, L., Nur, L., Binti, D., Mohd, I., Afiq, D., & Tazilah, K. (2021). Application of technology acceptancemodel (TAM) toward online learning during covid-19 pandemic: Accounting students perspective. International Journal of Business, Economics and Law, 24(1), 13–20. https://www.researchgate.net/profile/mohd-khamar-tazilah/publication/349214593.
Taat, M. S., & Francis, A. (2020). Factors Influencing the Students' Acceptance of E-Learning at Teacher Education Institute: An Exploratory Study in Malaysia. International Journal of Higher Education, 9(1), 133-141.