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標題: | 診斷式的適讀性評估系統:以小學文本探討四種模式的比較研究 Readability Diagnosis System: A Comparative Study of Four Models on Elementary School Textbook |
作者: | Yu-Hsiang Tseng 曾昱翔 |
指導教授: | 胡志偉(Chih-Wei Hue) |
關鍵字: | 適讀性,文本屬性,輸入透明度,參數透明度,文本分析,診斷式系統, readability,text properties,input transparency,parameter transparency,text analysis,diagnosis system, |
出版年 : | 2019 |
學位: | 博士 |
摘要: | 適讀性研究試圖透過分析文本的文字安排,以建立讀者閱讀時,文本易理解程度的預測模式。有研究者根據適讀性模式納入的文本屬性,以及模式建構的方式,將此類研究的發展分為三個時期。不同時期的研究在建構適讀性模式時深受「計算預測」和「認知解釋」等兩種考量的影響。在早期的傳統公式期,研究者希望從文本中找出一些屬性,然後以不同方法(如統計迴歸法)根據這些屬性,建構預測文本適讀性的模型。受到文本分析工具的限制,這時期納入適讀性模式的多是方便計算的文本表層屬性(如,文章中的難詞比例、文句的長度等)。隨著認知心理學不斷深化研究者對於人類閱讀的了解,以及電腦作為文本分析工具的出現,適讀性研究者參考閱讀理解的認知研究成果,在適讀性模式中納入一些可能影響閱讀理解歷程的文本屬性;這個階段被稱做認知理論期。當文本適讀性模式牽涉的文本屬性愈來愈多,且複雜時,一些比統計迴歸模式更有效率的文本適讀性計算方式受到了研究者的重視;這個階段可被稱做統計語言模式期。因為一些適讀性模式的計算複雜度超出一般使用者的理解範疇,有研究者提出,這可能會影響他們接受適讀性預測模式的意願。本研究的目的如下:(1)根據建構適讀性模式時,輸入資料的來源是否與適讀性文獻相關(本文稱之輸入透明度),以及適讀性模式之參數透明度,區分出四種建構適讀性模式的取徑。其中,低輸入透明度/高參數透明度的模式是一種新的文本適讀性的預測取徑。本研究將實際建構這四種模式,並比較它們對文本適讀性預測的有效性。(2)研究顯示,句法複雜度會影響讀者的文章閱讀,但因為這個屬性不易被量化,所以之前的文本適讀性模式均未納入能直接反應句法複雜度的屬性。本研究根據小學課本中的文本,建構各種語式的出現頻率常模,然後據以找出兩個能反應句法複雜度的屬性,並探討能否將它們納入適讀性模式中。(3)本研究針對一群現任小學教師,進行問卷調查及訪談,收集他們對幾種適讀性模式之建構取向的看法。(4)本研究開發了一套診斷式適讀性系統;該系統除了預測文本的適讀性外,還能為使用者提示哪些文本屬性影響文本的適讀性。本研究亦透過問卷調查與訪談結果瞭解國小教師對此系統的看法,調查結果顯示此系統可幫助教師理解及改變文章困難之處。 Reading is an essential process through which people learn and communicate. In order to predict how comprehensible texts were for readers, readability studies identified text properties and build predicting models. Three genres of studies were differentiated in literatures basing on their text properties considered and modeling methods. In the genre of traditional methods, researchers used easily available text properties (e.g. percentage of difficult words, sentence length, etc.), and used models such as linear regression to predict readability. In the genre of cognitive science-inspired methods, readability studies started to incorporate theories from cognitive science and include more text properties relating to reading comprehension. Some of these properties could be extracted by computerized automatic text analysis tools. As models involved more and more properties, genre of statistical language modeling methods emerged. Researchers employed more elaborate models to predict readability. However, other researchers argued these elaborate models were not easily understandable by average users, and therefore affected how users would adopt the predictions. Purposes of current study were as follows: (1) four modeling approaches were differentiated by input transparency, which based on the relationship between model’s input data and readability literatures, and parameter transparency. Among them, a new modeling approach (i.e. the one with topic modeling) of low input and high parameter transparency was attempted to predict readability of text. This study implemented four models and compared their performances on predicting readability. (2) Literatures showed that text properties related to syntactic complexities affected reading comprehension, but these properties are not easily computable and thus few readability models directly incorporated them. This study built a frequency norm of phrasal patterns, based on which text properties were extracted to reflect syntactic complexities of phrases. These properties were then tested if they improved readability models. (3) In a survey study, we interviewed teachers in elementary schools, and collected their opinions on how willingly they were to adopt predicitons made by different readability modeling approaches. (4) A readability diagnosis system was developed. The system not only predicted readability but provided extra information on the properties affecting readability. Survey studies on teachers showed the diagnosis system could help them understand text properties and edit text. |
URI: | http://tdr.lib.ntu.edu.tw/handle/123456789/884 |
DOI: | 10.6342/NTU201900027 |
全文授權: | 同意授權(全球公開) |
顯示於系所單位: | 心理學系 |
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