| 摘要: | 現有的講述性教學方法進行化學與數學學習效果往往不如預期，這樣方法容易造成學生無法融會貫通學習目的或誤解科學術語與含意。本研究運用運算思維教學策略改良現有的講述性教學方式，並加入延展實境技術融入化學與數學課程教具，其目的是為了探討延展實境與運算思維融入數學與化學課程對學生的學習感受、學習行為與學習成效之差異。
實驗對象為北部某一所國中的學生，實驗主題為化學融合反應課程。另一所高中的學生，實驗主題為數學幾何圖形課程。課程實驗活動之後，我們蒐集前測與後測評量、學習感受問卷、學習動機問卷、以及系統平台之操作紀錄等數據。依據前測結果，我們將先備知識分成高、中、及低三組，我們運用統計方法進行分析學生的學習感受、學習行為及學習成效。
實驗結果發現延展實境技術與運算思維教學策略融入數學與化學課程教具，有助於學生在運算思維能力與學習成效，就像數學與化學課程將單元一個大範圍問題能拆解成一個個步驟、瞭解組合模式、抽象畫思考模式、產生演算法思考模式等。
;Existing didactic strategies for learning chemistry and mathematics often do not work as well as expected. This approach may result in students not being able to integrate the learning objectives or misunderstand the terminology and meaning of the learning objective. In this thesis, this study applies computational thinking strategies to improve the existing didactic approach and incorporates extended reality technologies into chemistry and mathematics instructional aids. The purpose of this study is to explore the differences in learning experiences, learning behaviors, and learning outcomes of students who have integrated extended reality and computational thinking into the mathematics and chemistry curricula.
The subject of the experiment was students from a northern high school, and the topic of the experiment was the chemical fusion reaction. In addition, the subject of the experiment was a mathematical geometry course for students from another high school. After the experimental activity, we collected the experimental data such as pre-test and post-test evaluations, learning experience questionnaires, learning motivation questionnaires, and operating records of the system. The students were divided into high, medium, and low prior knowledge groups according to their pre-tests, and we used statistical methods to analyze their learning experience, learning behavior, and learning effectiveness.
The experimental results found that the integration of extended reality technologies and computational thinking teaching strategies into mathematics and chemistry curriculum aids students in their computational thinking skills and learning outcomes. For example, the mathematics and chemistry courses break down a large range of problems into individual steps, understanding combinatorial patterns, abstracting patterns of thinking, generating algorithmic patterns of thinking, and so on. |
