臺灣博碩士論文加值系統

運算思維，成為21世紀不可或缺的技能，尤其在科技迅速發展的時代，它更是現代職場中所需的重要能力。運算思維是電腦科學的基礎概念，以解決問題、設計系統和理解人類行為的能力，而程式語言被視為培養運算思維的重要途徑之一。本研究旨在輔導學生考ITS-Python程式語言國際認證，不僅能提升學生的運算思維能力和專業能力，還提高其職場競爭力。本研究在研究方法中提出概念知識、演算法思維及問題解析等運算思維構面，並應用DANP方法計算構面(Certiport Pearson VUE/ITS的Python技能群組)之權重值，用於產生樹狀之學習概念圖。此外，將計算後之權重值與Certiport Pearson VUE/ITS認證機構設計之Python技能群組分配比率進行比對，用以確認技能群組分配比率是否與專家一致。研究結果顯示，專家們一致認為在學習程式語言的過程中，資料類型和運算子(DT)是基礎和最重要的技能群組(為最大權重值)，而模組和工具(MT)為最進階之技能群組(為最小的權重值)，分別為DT(40-45%)與MT(5-10%)，顯示專家們的判斷與Certiport Pearson VUE/ITS提出之Python技能群組配分比率具有一致性。其次，本研究透過規則空間方法及學習概念圖，產生六條學習路徑，透過專家問卷分析後之結果，再經由關聯權重矩陣之關聯比率值之定義與算法，計算最佳學習路徑。分析結果顯示，推薦的最佳學習路徑之技能群組順序為「DT→DL→IO→DS→TE→MT」，將此規劃於Python程式語言學習與證照輔導教學上，及學生自我線上學習課程的順序，提供給推薦系統內課程補救教學推薦單自我補救學習的依據。為確保學生在練習過程中，對Python程式語言之理解程度，及達到培養學生運算思維能力之目的，本研究將學生成績與成果，分為111、112及113等三個不同學習年度的學生進行統計分析。分析結果顯示，112年度學生在Python練習成績、運算思維能力成果及學習成效之學習上表現尤為優異。總體來看，運算思維的DT對多個學習成效技能群組成績都有正向顯著影響；Python練習後測成績對ITS-Python證照成績的影響非常顯著，顯示學生透過後測有助於提升他們考試之通過率，進而提升學生運算思維能力。

Computational thinking has become an indispensable skill in the 21st century, particularly in an era of rapid technological advancement. It is a critical ability required in the modern workplace. Computational thinking forms the foundational concepts of computer science, involving problem-solving, system design, and understanding human behavior. Programming languages are considered one of the essential pathways to developing computational thinking skills. This research aims to guide students in preparing for the ITS-Python programming language international certification. Not only does it enhance students' computational thinking and professional skills, but it also boosts their competitiveness in the job market. The study introduces dimensions of computational thinking, including Conceptual Knowledge(CK), Algorithmic Thinking(AT), and Problem Solving(PS). We apply the DANP method to calculate the weight values of these dimensions (skill groups) to generate a hierarchical learning concept map. Additionally, the calculated weight values are compared with the skill group distribution ratios designed by the Certiport Pearson VUE/ITS certification to confirm whether the distribution ratios align with expert opinions. The research results indicate that experts and the distribution ratios consistently agree that Data Types and Operators (DT) are the foundational and most critical skill groups (with the highest weight value), while modules and tools (MT) are the most advanced skill groups (with the lowest weight value), with DT at 40-45% and MT at 5-10%.
Furthermore, through the rule space method and learning concept maps, six learning paths were generated. After analyzing the expert questionnaire results and defining and calculating the relation ratio values of the relation weight matrix, the optimal learning path was determined. The analysis results show that the recommended optimal learning path for skill groups is "DT → DL → IO → DS → TE → MT." This sequence is planned for Python programming language learning and certification guidance teaching and the order of students' self-directed online learning courses. It provides a basis for self-remedial learning in the recommendation system's course. To ensure students' understanding of the Python programming language and to achieve the goal of developing their computational thinking skills, this study conducts a statistical analysis of students' grades and outcomes across three different academic years (111, 112, and 113). The analysis results indicate that students in the 112th academic year performed exceptionally well in Python practice scores, computational thinking skill outcomes, and learning performance. Overall, the DT aspect of computational thinking positively and significantly impacts multiple skill groups in learning performance. The post-test scores in Python practice significantly influence the ITS-Python certification exam results, indicating that post-testing helps improve students' pass rates, thereby enhancing their computational thinking skills.

摘要 i
ABSTRACT iii
誌謝 v
目錄 vi
表目錄 ix
圖目錄 xii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究問題 5
1.3 研究目的 6
1.4 研究流程 7
1.5 研究範圍與限制 9
第二章 文獻探討 10
2.1 運算思維(Computational Thinking) 10
2.1.1 概念知識(Conceptual Knowledge, CK) 14
2.1.2 演算法思維(Algorithmic Thinking, AT) 16
2.1.3 問題解析(Problem Solving, PS) 18
2.2 Python程式語言及Certiport Pearson VUE/ITS 資訊科技國際認證 19
2.3 運算思維構面與ITS-Python技能群組的連結關係 29
2.4 DEMATEL-ANP (DANP) 30
2.5 規則空間理論 41
2.5.1 學習概念圖 41
2.5.2 規則空間模型(Rule Space Model, RSM) 43
第三章 研究方法 47
3.1 研究設計 47
3.2 研究工具 47
3.2.1 學生數位學習平台系統 47
3.2.2 SPSS統計分析軟體 48
3.3 問卷設計 49
3.3.1 DANP專家問卷 49
3.3.2 運算思維能力問卷 52
3.4 方法步驟 54
3.4.1 DEMATEL-Based ANP (DANP)方法 54
3.4.2 規則空間理論 (Rule Space Theory) 58
第四章 資料分析與研究假設 59
4.1 DEMATEL-ANP (DANP)方法分析 59
4.2 DANP與規則空間模型之學習概念圖、關聯權重與最佳學習路徑之推論 66
4.2.1 DANP與規則空間模型方法之分析 67
4.2.2 學習概念圖之產生 69
4.2.3 關聯權重矩陣之產生 71
4.2.4 基於規則空間模型學習概念圖之最佳學習路徑推論 74
4.2.5 線上課程學習考試系統及學習成效分析之推薦系統 79
4.3 研究假設 81
第五章 統計分析 87
5.1 描述性統計分析(Descriptive Analysis) 87
5.2 信效度分析(Reliability & Validity Analysis) 93
5.2.1 信度分析(Reliability Analysis) 93
5.2.2 效度分析(Validity Analysis) 94
5.3 差異性分析 97
5.3.1 不同學習年度學生在Python練習成績、運算思維能力及學習成效之差異性 97
5.3.2 通過與未通過證照學生在Python練習成績、運算思維能力及學習成效之差異性 102
5.4 相關分析(Correlation Analysis) 106
5.4.1 Python練習成績與運算思維能力之間的相關分析 106
5.4.2 Python練習成績與學習成效之間的相關分析 110
5.4.3 運算思維能力與學習成效之間的相關分析 113
5.5 迴歸分析(Regression Analysis) 116
5.5.1 Python練習成績及學習成效之迴歸分析 116
5.5.2 Python練習成績及運算思維能力之迴歸分析 119
5.5.3 運算思維能力及學習成效之迴歸分析表 121
5.6 小結 127
第六章 結論與建議 133
6.1 結論 133
6.2 研究建議 135
6.3 研究貢獻 135
參考文獻 137
附錄 158
附錄 A 運算思維評估技能群組之DANP建立ITS-Python技能群組概念圖及學習路徑權重值之專家問卷 158
附錄 B 程式語言之運算思維能力量表問卷 167

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