STEM策略對不同學習風格學生之影響：以巨量資料學習為例

Abstract

本研究以STEM科際整合教學策略（簡稱STEM策略）發展適合高中生學習的巨量資料課程單元，並且探討此教學策略對不同學習風格學生學習巨量資料概念之影響。STEM策略為將科學、科技、工程與數學等領域知識進行整合，以解決實際生活問題，本研究以生活中常見的議題──「空氣汙染」為學習情境，運用STEM策略設計實作活動，讓學生進行巨量資料概念的學習。研究採單組後測設計，參與者為47名台北市某公立高中之高二學生，教學實驗（含後測）為期四週共八節課。學生的學習風格分為資訊感知（具體經驗、抽象概念）與資訊轉換（反思觀察、主動驗證）兩個向度，探討STEM策略對其學習的影響。研究結果發現：（1）STEM策略能幫助學生理解巨量資料概念；（2）STEM策略對學生學習巨量資料的態度有正向影響；（3）STEM策略對於不同學習風格學生的學習成就、學習態度皆無顯著差異。建議未來研究應增加受試者人數；在巨量資料教學設計上，預留足夠教學時間進行實作活動，並以生活情境為脈絡進行教學時補充多元的例子說明，由不同情境中理解巨量資料概念。The study aims to develop a big data instructional module for high school students using the STEM approach and to evaluate the effects of the STEM approach on students' achievement and learning attitudes between different learning styles. The STEM approach integrates science, technology, engineering, and mathematics skills into the instructional module. In this study, the instructional module was developed based on “air pollution issue” and used it as the learning context. Hands-on activities that apply the STEM approach are designed for students to learn big data concepts. A single-group posttest design was implemented in the study. The participants were 4710th grade students from a public high school in Taipei. The experiment lasted for four weeks. Learning styles are divided into two dimensions, including “grasping information” (concrete experience, abstract conceptualization) and “transforming information” (reflective observation, active experimentation). The study explored the effects of STEM approach on students between different learning styles.The research results showed that the STEM approach helps students realize big data concepts and facilitates students' positive attitude toward learning big data. No significant differences were observed in students’ learning achievement and attitudes between different learning styles when applying STEM approach. It is suggested that future studies should increase the number of participants, provide students with enough time for hands-on activities, and explain concepts through diverse exampleswhen teaching big data.