應用分類與迴歸樹探討高中職生金融知識結構

Applying Classification and Regression Trees to Investigate High School Students' Financial Knowledge Structures

李沂澂(Li Yi- Cheng)

指導教授 ： 林正昌

國立臺灣師範大學/教育學院/教育心理與輔導學系/碩士(2016年)

https://doi.org/10.6345/NTNU202204092

摘要

本研究的目的在透過分類與迴歸樹（CART）分析方法，探討臺灣高中職生在金融素養認知測驗的表現情形及其不同表現的知識結構差異。研究者以林正昌（2016）創新金融教育課程之設計中，針對全國各區高中職學生以相同金融素養認知測驗版本施測的結果為資料來源，有效樣本數為993人。研究過程將測驗得分透過CART建立分類預測模型，找出預測不同表現的金融概念，概念間積差相關係數大小，建構出高分群與低分群的金融知識結構圖。本研究有以下四點發現：一、高中職生的金融知識表現呈現常態分配。二、高中職生的金融素養認知測驗表現分類模型之分類正確率整體高達88.1％、高分群達89.3％、低分群達86.9％。三、預測高分表現的的試題為「風險評估mu2」、「金融政策mu8」、「金融機構F19」與「國際貿易mu1」；預測低分表現的試題為「風險評估F29」、「金融政策F20」、「金融政策mu5」與「貨幣F23」。兩群的共同預測試題為：「風險評估F14」、「金融政策F26」與「通貨膨脹mu6」，可視為基本試題。四、預測高分表現的五個金融概念間呈現正相關，分別為「風險評估」、「金融政策」、「通貨膨脹」、「國際貿易」與「金融機構」。其中，以「風險評估與金融政策」相關程度最高；「通貨膨脹與金融機構」相關程度最低。預測低分表現的四個金融概念間呈現正相關，分別為「風險評估」、「金融政策」、「通貨膨脹」與「貨幣」。其中，以「風險評估與金融政策」的相關程度最高；「通貨膨脹與貨幣」的相關程度最低。本研究可瞭解高低分群的金融知識結構差異，並作為金融課程、測驗或教材篩選重點金融概念的參考，建議未來研究可採用其他測驗版本進行分析。

關鍵字

分類與迴歸樹 ； 金融素養 ； 概念結構

並列摘要

Through Classification and Regression Trees (CART), this study aims at investigating the performance of Taiwanese high school students on the financial literacy cognitive assessment, based on which the differences among their financial knowledge structures are further discussed. This study targeted at one of the six versions of the financial cognitive assessment under the Design of Innovative Financial Education Curriculum conducted by Cheng-Chang Lin (2016) collecting the assessment results as data to be analyzed for this study, and 993 samples collected were effective. Based on the data collected, a classification model was formed up through CART, through which various financial concepts of high and low scoring groups respectively were thus defined. Correlation coefficients between each financial concept were further identified through product-moment correlation analysis, based on which a financial knowledge chart was finally made. The four main findings are as follows: 1. The financial knowledge performance of Taiwanese high school students is normally distributed. 2. The average accuracy rate of the classification model was 88.1%, with 89.3% for the high scoring group and 86.9% for the low scoring group. 3. Test items that can predict high scoring are “risk assessment F14”, “financial policy F26” and “inflation mu6”, “risk assessment mu2”, “financial policy mu8”, “financial institution F19”and “international trade mu1”. Test items that can predict low scoring are “risk assessment F14”, “financial policy F26” and “inflation mu6”, “risk assessment F29”, “financial policy F20”, “financial policy mu5” and “currency F23”. As “risk assessment F14”, “financial policy F26” and “inflation mu6” are items that can predict both high and low scoring, they can be regarded as basic test items for financial cognitive assessment. 4. The five financial concepts predicting high scoring are significant positive correlated, including “risk assessment”, “financial policy”, “inflation”, “international trade” and “financial institution”, among which the correlation between “risk assessment” and “financial policy” is the highest, and that between “inflation” and “financial institution” is the lowest. The four financial concepts predicting low scoring are significant positive correlated, including “risk assessment”, “financial policy”, “inflation” and “currency”, among which the correlation between “risk assessment” are “financial policy” is the highest, and that between “inflation” and “currency” is the lowest. This study makes understanding of the differences of financial knowledge structures between high and low scoring groups, hoping to provide suggestions for educators on choosing which concepts to include in financial courses, tests or teaching materials. It is also suggested that the other five versions of the financial cognitive assessment can be targeted for future studies.

並列關鍵字

Classification and Regression Trees (CART) ； Financial Literacy ； Conceptual Structures

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