非理工科系大學生在科普文章中的數據圖理解表現之探討

Abstract

在這個科技進步迅速、3C產品蓬勃發展的時代，網路上充斥著各式各樣的資訊與文章，當人們遇到有待考證的科普文章、數據圖或統計數據時，是否有足夠的能力來理解數據圖及辨別其正確性是相當重要的。本研究探討34位非理工科系大學生在科普文章中的數據圖理解表現，以及了解有無修習統計相關課程、數據圖理解自我效能與數據圖理解表現之間的關聯性。研究工具的設計是根據Curcio（1987）的數據圖理解理論、洪振方（2015）團隊「數據建模能力」一系列相關研究中的「解釋、統整、應用數據模型能力」進行研究工具之設計，最後整合出四個層次的數據圖理解任務：讀取數據、數值比較、多層數值比較、預測趨勢，且根據任務彙整、設計出一份科普文章與測驗，以及選用Li等人（2018）的數據圖理解自我效能量表、潘怡如（2014）的科學自我效能量表這兩份量表，整合成一份自我效能量表。研究結果顯示：(1)無論是整體表現、讀取數據、數值比較，受試者在「散佈圖」的理解任務表現是四種數據圖中最差的，而在多層數值比較中堆疊長條圖的表現是最差的。而從四種數據圖的「數值比較」進行錯誤分析，可以發現皆有一部份受試者是因在進行數值比較前讀取數據時發生錯誤，代表進行數值比較或是多層數值比較時，對受試者來說還是滿常讀取數據錯誤。此外，受試者在比值、相關程度、迴歸直線、斜率、截距等概念上較有困難。(2)大部分的題型有修習統計相關課程者與無修習統計相關課程者之間數據圖理解表現並無顯著差異，僅在「堆疊長條圖的讀取數據」此題型中有修習統計相關課程者的分數表現顯著低於無修習統計相關課程者。(3)有修習統計相關課程之學生數據圖理解自我效能顯著高於無修習統計相關課程之學生。受試者數據圖理解自我效能量表之得分，與在堆疊長條圖的總得分、堆疊長條圖的多層數值比較任務得分皆達顯著中度正相關。In the era of rapid progress in science and technology, as well as vigorous development of 3C products, various information and articles can be found on the Internet. When people encounter popular science articles, graphs and statistical data that need to be verified, whether they are equipped to comprehend graphs and discern the correctness is quite important. This study aims to investigate the performance of graph comprehension in popular science articles of 34 non-science-major university students, and the relationship among graph comprehension performance, graph self-efficacy and whether students take statistics-related courses in university or not. The instruments were developed based on Curcio's (1987) theory of graph comprehension and Hung's (2015) "data modeling ability" including "interpreting, integrating and applying data modeling abilities." Finally, four levels of graph comprehension tasks were created: reading the data, comparing the data, multi-comparing the data, predicting the trends. A popular science article and a comprehension test were designed based on four levels of graph comprehension tasks. In addition, the graph self-efficacy scale of Li et al. (2018) and the scientific self-efficacy scale of Pan (2014) were combined as the graph self-efficacy scale in this study.The results showed that: (1) Performance in overall,"reading the data", "comparing the data" tasks were worst for "scatterplots" among the four types of graphs. In addition, performance in the "multi-comparing the data" task was worst in "stacked bar chart". In the "comparing the data" task, some students' mistakes were found at the level of "reading the data". Furthermore, students had difficulties about ratio, correlation, regression lines, slopes and intercepts. (2) In most items, whether students take statistics-related courses in universitydid not make a difference. The difference was found in the"reading the data" task with "stacked bar chart", in which students who take statistics-related courses performed worse than those who do not. (3) Graph self-efficacy was significantly higher for students who take statistics-related courses. In addition, scores in graph self-efficacy scale developed by Li et al. (2018) moderately and positively correlated with overall scores of "stacked bar chart" and scores of the "multi-comparing the data" task with "stacked bar chart".