程式設計課程融入體驗學習之探究

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2018

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本研究以探究研究者自行建構之高中職程式設計融入體驗學習課程架構,並於實施五堂課程後,進行運算思維學習成效的評估,進而探討程式設計融入體驗學習課程架構是否完善與學生的學習成效成果,且同時分析學生不同背景變項與高中職程式設計融入體驗學習之運算思維變化的關係。研究對象為研究者本身任教學校學生,高一71人,高二33人,高三13人(共117人)。於課程實施前先進行運算思維前測,接著施行由研究者自行設計之五堂程式設計融入體驗學習課程,在課程結束後實施運算思維後測與學習態度問卷量表。資料分析以量化資料分析為主、質性資料為輔,根據學生學習問卷量表與五堂課的形成性評量,探討課程架構是否完善,接著透過運算思維前測、後測的成績,進行t檢定與變異數分析。研究結果發現,根據程式設計融入體驗學習五堂的課程架構,普遍學生喜愛實作或遊戲式課程。在實施五堂程式設計融入體驗學習課程後,學生的運算思維能力(包括:抽象化、資料表示、樣式辨識/一般化、問題解析、演算法思維)中,除了「資料表示」無明顯進步外,其餘四項能力皆有明顯進步。學生不同背景變項(包含:性別、年級、在班級成績、居住地、學制、是否曾接觸過程式設計課程)對於學生運算思維學習狀況,皆無顯著的影響。預期本研究成果未來讓程式設計課程的教師與研究人員在設計課程上,能有所依據並更加完整,讓學生運算思維能力的培養更加順利。
The thesis is based on writer’s research:The Inquiry of Programming Curriculum Delivered through Experiential Learning. After five classes, the researcher evaluates the result of Computation Thinking to know how about the effect of inquiry of programming curriculum delivered through experiential learning for high school students.The target of the research is 117 high school students, including grade one to three. Before the lesson, they made some pretests about Computation Thinking and took effect to five lesson of the inquiry of programming curriculum, designed by the researcher. The data analyses mainly rely on quantization, based on student’s questionnaire and test to know about the infrastructure of the lessons.After the reseach, most students like practical course and game-based learning. After five lessons, student’s Computation Thinking (abstraction, data representation, pattern recognition/generalization, decomposition, and algorithmic thinking)are improved. There is no distinguished affect between student’s individual differences (gender, grade, class performance, place of residence, school system, whether or not contacting with the process design course)and learning Computation Thinking lesson.This reaeach is aim to help teachers who want to do inquiry of programming curriculum, and make students effective in learning Computation Thinking.

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程式設計教學, 體驗學習, 運算思維, 學習成效, 個別差異, programming instruction, experiential learning, computational thinking, learning effectiveness, individual differences

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