在字本位教學中，漢字特徵分析（如：部件頻次、功能及結構組合等）對文本難度具有關鍵影響，然而目前缺少能將部件資訊，以及漢字難度結合的文本分析系統。本研究根據「中文部件組字與形構資料庫」以及學習者實徵難度分級，建置漢字難度分析暨回饋系統，功能包含：分析文本的漢字難度、提供文本漢字部件解構、文本部件衍生字和建議衍生學習內容等四大功能。系統給予使用者直觀的文本分析結果，依據不同教學需求呈現漢字資訊，並提供相關衍生學習內容並提供對象為華語母語者和二語者之教學者，更多元的漢字特徵資料。本系統經教學者調查研究評估，在認知有用性、認知易用性和後續使用意願皆獲得良好回饋，並顯著優於現有教學輔助系統。研究結果顯示本研究建置之系統，容易操作使用且有益於華語文教學，教學者有高度意願持續使用系統，以進行教學預備用途。

Feature analysis of Chinese characters plays a prominent role in "character-based" education. However, there is an urgent need for a text analysis system for processing the difficulty of composing components for characters, primarily based on Chinese learners' performance. This study aims to develop a Chinese character feature and difficulty analysis system based on Chinese Orthography Database and empirical learner difficulty
grading. This system provides four functions：（1）analyzing a text and providing its difficulty regarding Chinese characters;（2）decomposing characters into components and calculating the frequency of components based on the analyzed text;（3）affording component-deriving characters based on the analyzed text and downloadable images as teaching materials; and（4）recommending learning materials of components. The system is expected to benefit educators in designing vocabulary teaching materials and understanding text information. It provides a more diverse range of Chinese character feature data for Mandarin native speakers and second language learners. The system, as
evaluated through survey research with educators, received positive feedback suggesting that the participants agree that this system is useful, easy to use, and engaging. It significantly outperformed existing instructional support systems. The research results indicate that the developed system is user-friendly and beneficial for Chinese language teaching. Educators express a strong willingness to consistently use the system for
instructional preparation.

英文文獻
Chandio, F., Irani, Z., Zeki, A., Shah, A., & Shah, S. (2017). Online Banking Information Systems Acceptance: An Empirical Examination of System Characteristics and Web Security. Information Systems Management, 34, 50 - 64. https://doi.org/10.1080/10580530.2017.1254450.
Chang, L. Y., Chao, T. Y., Tseng, C. C., Lin, H. L., Chen, H. C., & Sung, Y. T. (2023). A comparative study of the Chinese characters in the norm of Chinese character difficulty and the TBCL list. The 12th International Conference on Han Characters Education and Research. Poznan, Poland.
Chang, L. Y., Tseng, C. C., Perfetti, C. A., & Chen, H. C. (2022). Development and validation of a Chinese pseudo-character/non-character producing system. Behavior Research Methods, 1-17. https://doi.org/10.3758/s13428-021-01611-8
Chen, H. C., Chang, L. Y., & Tsai, M. H. (2019, November). Chinese key-image learning: An app designed with handwriting evaluation and instant feedback to support Chinese character learning. In International Conference on Innovative Technologies and Learning (pp. 547-557). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-35343-8_58
Chen, H. C., Hsu, C. C., Chang, L. Y., Lin, Y. C., Chang, K. E., & Sung, Y. T. (2013). Using radical-deriving E-learning platform to increase Chinese learners' Chinese knowledge. Language Learning & Technology, 17(1), 89-106. https://www.researchgate.net/publication/266732441_Using_Radical-deriving_E-learning_platform_to_increase_Chinese_learners%27_Chinese_knowledge
Chen, K. Y. F., & Chen, C. Y. D. (2022). Radical awareness among L2 chinese learners：Chinese vs. non-Chinese spheres. Chinese Language Learning and Technology, 2(1), 31-65. https://doi.org/10.30050/CLLT.202206_2(1).0002
Chen, Y. P., Allport, D. A., & Marshall, J. C. (1996). What are the functional orthographic units in Chinese word recognition：The stroke or the stroke pattern? The Quarterly Journal of Experimental Psychology Section A, 49(4), 1024–1043. https://doi.org/10.1080/713755668
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555
Crossley, S. A., & Kyle, K. (2018). Assessing writing with the tool for the automatic analysis of lexical sophistication (TAALES). Assessing Writing, 38, 46-50. https://doi.org/10.1016/j.asw.2018.06.004
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. https://doi.org/10.1287/mnsc.35.8.982
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. https://doi.org/10.48550/arXiv.1810.04805
Drasgow, F., & Hulin, C. L. (1990). Item response theory. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology, pp. 577-636. Consulting Psychologists Press.
Hargis, C. H., Terhaar-Yonkers, M., Williams, P. C., & Reed, M. T. (1988). Repetition requirements for word recognition. Journal of Reading, 31(4), 320-327.
Hassenzahl, M., & Tractinsky, N. (2006). User experience-a research agenda. Behaviour & information technology, 25(2), 91-97.https://doi.org/10.1080/01449290500330331
Hong, J. F., Sung, Y. T., Tseng, H. C., Chang, K. E., & Chen, J. L. (2016). A multilevel analysis of the linguistic features affecting Chinese text readability. 臺灣華語教學研究, 13, 95-126. https://doi.org/10.29748/TJCSL.201612_(13).0005
Hwang, G. J., Yang, L. H., & Wang, S. Y. (2013). A concept map-embedded educational computer game for improving students' learning performance in natural science courses. Computers & Education, 69, 121-130.
https://doi.org/10.1016/j.compedu.2013.07.008
Internet World Stats. (2022). World internet users and population stats. Retrieved March 25, 2023, from https：//www.internetworldstats.com/stats.html
Joachims, T. (1998). Text categorization with support vector machines：Learning with many relevant features. In：Nédellec, C., Rouveirol, C. Machine Learning：ECML-98. ECML 1998. Lecture Notes in Computer Science, 1398. Springer, Berlin, Heidelberg. https：//doi.org/10.1007/BFb0026683
Joachims, T. (2005, June). Text categorization with support vector machines: Learning with many relevant features. In Machine Learning: ECML-98: 10th European Conference on Machine Learning Chemnitz, Germany, April 21–23, 1998 Proceedings (pp. 137-142). Berlin, Heidelberg: Springer Berlin Heidelberg.
Krashen, S. D. (1981). Second Language Acquisition and Second Language Learning. Pergamon Press Inc., Oxford.
Kuo, T., Lu, C. H., Tsai, H. T., & Tseng, Y. Y. (2008). User Acceptance of E-learning System: A Self-Directed Learning Perspective. Journal of e-Business, 10(4), 963-987. https://doi.org/10.6188/JEB.2008.10(4).11
Lai, C., Qi, X., Lü, C., & Lyu, B. (2020). The effectiveness of guided inductive instruction and deductive instruction on semantic radical development in Chinese character processing. Language Teaching Research, 24(4), 496-518. https://doi.org/10.1177/1362168818805265
Lam, H. C., & Tsui, B. M. A. (2013). Awareness of orthographic structure in children’s learning of Chinese characters. Contemporary Educational Research Quarterly, 21(3), 83-111. https：//doi.org/10.6151/CERQ.2013.2103.03
Li, H., Shu, H., McBride‐Chang, C., Liu, H., & Peng, H. (2012). Chinese children's character recognition: Visuo‐orthographic, phonological processing and morphological skills. Journal of Research in Reading, 35(3), 287-307. https://doi.org/10.1111/j.1467-9817.2010.01460.x
Lin, Y. S., Lim, J. N., & Wu, Y. S. (2022). Developing and applying a Chinese character learning game app to enhance primary school students’ abilities in identifying and using characters. Education Sciences, 12(3), 189. https://doi.org/10.3390/educsci12030189
Lindgaard, G., Fernandes, G., Dudek, C., & Brown, J. (2006). Attention web designers: You have 50 milliseconds to make a good first impression! Behaviour & Information Technology, 25(2), 115-126. https://doi.org/10.1080/01449290500330448
Lo, M., Hue, C., & Tsai, F. (2007). Chinese readers' knowledge of how Chinese orthography represents phonology. Chinese Journal of Psychology, 49, 315-334. https://doi.org/10.6129/CJP.2007.4904.01
Lu, B., & Tsou, B. K. (2010). Combining a large sentiment lexicon and machine learning for subjectivity classification. In Machine Learning and Cybernetics (ICMLC), In 2010 international conference on machine learning and cybernetics, 6, pp.3311-3316. https://doi.org/10.1109/ICMLC.2010.5580672
Peddie, J. (1992). Graphical user interfaces and graphic standards. McGraw-Hill, Inc.
Pernice, K., Whitenton, K., & Nielsen, J. (2018). How People Read Online: The Eyetracking Evidence. Nielsen Norman Group Inc.
Portz, J. D., Bayliss, E. A., Bull, S., Boxer, R. S., Bekelman, D. B., Gleason, K., & Czaja, S. (2019). Using the technology acceptance model to explore user experience, intent to use, and use behavior of a patient portal among older adults with multiple chronic conditions: descriptive qualitative study. Journal of medical Internet research, 21(4), e11604.
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
Stuart, M., Masterson, J., & Dixon, M. (2000). Spongelike acquisition of sight vocabulary in beginning readers? Journal of Research in Reading, 23(1), 12-27. https://doi.org/10.1111/1467-9817.00099
Sung, Y. T., Chang, T. H., Lin, W. C., Hsieh, K. S., & Chang, K. E. (2016). CRIE: An automated analyzer for Chinese texts. Behavior Research Methods, 48, 1238-1251. https://doi.org/10.3758/s13428-015-0649-1
Taft, M., & Chung, K. (1999). Using radicals in teaching Chinese characters to second language learners. Psychologia, 42(4), 243-251.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), https://doi.org/10.1287/mnsc.46.2.186.11926
Wesley, A., (1989). Hypercard Stack Design Guidelines. Apple Computer Inc.
Xu, Y., Chang, L. Y., & Perfetti, C. A. (2014). The effect of radical‐based grouping in character learning in Chinese as a foreign language. The Modern Language Journal, 98(3), 773-793. https://doi.org/10.1111/modl.12122
Zhou, X., Peng, Y., & Liu, B. (2010). Text mining for traditional Chinese medical knowledge discovery：a survey. Journal of Biomedical Informatics, 43(4), 650-660. https://doi.org/10.1016/j.jbi.2010.01.002

中文文獻
曾千芝、陳學志、張瓅勻、胡中凡、陳修元（2018）。常用漢字部件的位置規則性與位置自由度與其他文字特性相關之分析-以中文部件組字與形構資料庫為基礎。教育心理學報， 49（3）， 487-511。https://doi.org/10.6251/BEP.201803_49(3).0007
陳茹玲、曾厚強、宋曜廷、林慶隆、柯華葳（2017）。華語文教材之文本分析與可讀性研究。國際中文教育學報，1，39-71。
陳學志、張瓅勻、邱郁秀、宋曜廷、張國恩（2011）。中文部件組字與形構資料庫之建立及其在識字教學的應用。教育心理學報，43（閱讀專刊），269-290。 https://doi.org/10.6251/BEP.20110412
杜佳真、林世華（2007）。九年一貫課程數學領域能力指標 [數與量]，[代數] 主題軸第一，二階段表現標準適切性評估之研究。師大學報：教育類， 52（1）， 63-85。
方金雅（2001）。多向度詞彙評量與教學之研究〔未出版之博士論文〕。國立高雄師範大學。
黃沛榮（2001）。 漢字教學的理論與實踐。樂學書局。
黃淑齡、白明弘、吳鑑城、李詩敏、林慶隆（2018）。華語認寫教學字表建置探討。臺灣華語教學研究，17， 31-58。 https://doi.org/10.29748/TJCSL.201812_(17).0003
教育部 （2013）。邁向華語文教育產業輸出大國八年計畫。中華民國教育部。https://depart.moe.edu.tw/ed2500/News.aspx?n=E0F2A9154F4E577A&sms=81A49C23F4C8A193
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。
李金龍（2015）。漢字部件意義化創意技藝策略及部件數對華語第二語學習者之漢字識寫寫成效研究 〔未出版之碩士論文〕。國立臺灣師範大學。
連育仁（2018）。華語教材行動互動系統發展與教師科技接受度研究。華文世界，122，32-45。
林慶隆、吳鑑城、白明弘、李施敏、吳欣儒、蔡岳璋、丁彥平、張玳維、余昱瑩、王冠孺、盧昱勳、陳沛璇（2020）。 遣辭用 [據]—臺灣華語文能力第一套標準。國家教育研究院。
毛浩然、高麗珍、徐赳赳（2015）。Van Dijk 話語理論體系的建構與完善。中國外語， 5，31-40。
宋曜廷、陳茹玲、李宜憲、查日龢、曾厚強、 林維駿、張道行、張國恩（2013）。中文文本可讀性探討：指標選取、模型建立與效度驗證。 中華心理學刊，55(1)，75-106。 https://doi.org/10.6129/CJP.20120621
王寧（2002）。漢字教學的原理與各類教學方法的科學運用（下）。課程.教材.教法，11，23-27。
王瓊珠（2005）。高頻部首／部件識字教學對國小閱讀障礙學生讀寫能力之影響。臺北市立師範學院學報。36（1），95-124。
王瓊珠、洪儷瑜、張郁雯、陳秀芬（2008）。一到九年級學生國字識字量發展。教育心理學報，394（4），555-568。 https://doi.org/10.6251/BEP.20071026
姚蘭（2007）。圖庫與閃示卡之結合運用。教學錦囊，74-80。
葉德明（2000）。外籍生漢字書寫策略探討。第六屆世界華語文教學研討會論文集（三），311-320。世界華語文教育學會。
張金蘭（2014）。第一語言漢字教學現況及其對華語文教學的啟示。 國立彰化師範大學文學院學報，9，11-35。
張金蘭（2016）。中國傳統識字教育給對外漢字教學帶來的啟示。開創華語文教育與僑民教育之新視野會議第六屆〔未出版之研討會論文〕。
張婉珍（2015）。科技接受模式與英語教學行動學習之接納程度。教育科學期刊，14(1)，57-81。
張祐瑄、蘇宜芬（2014）。「國小句型理解測驗」之編製及其信、效度研究報 告。測驗學刊，61（3 ），385-408。
網站資源
中央研究院（2010）。 國際電腦漢字及異體字知識庫。 https：//chardb.iis.sinica.edu.tw/
教育部（2018 ）。十二年國民基本教育課程總綱。2023年3月27 日取自https：//cirn.moe.edu.tw/WebContent/index.aspx?sid=11&mid=5737
香港中文大學人文電算研究中心 （2014）。漢語多功能字庫。https：//humanum.arts.cuhk.edu.hk/Lexis/lexi-mf/
國家教育研究院（2019）。華語教學標準體系應用查詢系統-漢字分級標準檢索系統。 https：//coct.naer.edu.tw/standsys/
國家教育研究院（2019）。國教院教材編輯輔助系統。https://coct.naer.edu.tw/sentedit/#
國語教學中心（2011）。ACCESS全漢字檢索系統。
http：//huayutools.mtc.ntnu.edu.tw/mtchanzi/index.aspx