智慧QAC機制及其對真實情境中小學生幾何學習的影響

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姓名

Juli Hildayati(Juli Hildayati) 查詢紙本館藏

畢業系所

網路學習科技研究所

論文名稱

智慧QAC機制及其對真實情境中小學生幾何學習的影響
(Smart QAC Mechanism and Its Influence on Geometry Learning for Elementary School Students in Authentic Contexts)

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摘要(中)

幾何學習很重要。然而，通常情況下，在課堂上使用課本會讓學生失去動力，因為過度強調記憶和理解幾何公式，而不應用它們來解決周圍的日常生活問題。
基於上述問題，在本研究中，我們提出了一種專注於3D物件的幾何學習應用程序，即具有Smart QAC機制的Smart-3D UG。該應用程式是在行動裝置上開發的，具有兩個主要功能：（1）擴增實境（AR）功能，用於在真實環境（即周圍區域）中實現真實幾何物件的 3D 視覺化；（2）智慧問題和澄清（QAC）機制與人工智慧（AI）相結合，產生與學生幾何學習實踐相關的有意義的問題和澄清。此外，我們也啟用了所提出的智慧 QAC 的多種表示形式，即視覺表示、邏輯表示和語言表示。
我們設計了學習活動的三個階段（即情境化、個人化和社交化）。在每個階段，學生都需要回答與幾何相關的問題，然後才能收到人工智慧的澄清。為了探討智慧QAC機制對學生幾何能力（GA）、空間能力（SA）和估計能力（EA）等學業成績的影響，我們對76位五年級學生進行了準實驗。他們被分成三組，分別是實驗組（EG）和兩個對照組（CG-A和CG-B）。實驗中有兩個任務。即教師設計和自由探索。實驗持續了九週，並採用了混合方法分析。
結果揭示了一些有趣的發現。首先，在與 GA、SA 和 EA 相關的學業成績上，EG 顯著優於 CG-A 和 CG-B。其次，Smart QAC 機制與學業成績密切相關。此外，EG 表現出比 CG-A 更高的測量精度，特別是在教師設計活動和自由探索中。這一結果表明，智慧 QAC 可以幫助 EG 學生更成功地測量精確物體。而且，EG的錯誤率低於CG-A，在自由探索活動中達到顯著差異。這意味著我們提出的智慧 QAC 機制增強了真實環境中的幾何理解和能力。更重要的是，透過智慧QAC，學生對Smart 3D-UG展現了積極的態度和高度的意願。因此，增強的智慧 QAC 機制有可能在課堂上應用，以支援真實環境中更複雜的幾何學習。

摘要(英)

Geometry learning is important. However, usually, using textbooks in class makes students lose motivation because of too much emphasis on memorizing and understanding geometry formulas without applying them to solve daily life problems in their surroundings.
Based on the aforementioned issues, in this study, we proposed a geometry learning application focusing on 3D objects, namely Smart-3D UG with the Smart QAC Mechanism. This application was developed on a mobile device with two proposed main functions: (1) Augmented Reality (AR) feature for enabling 3D visualization of real geometry objects in authentic context (i.e., surrounding area); (2) Smart Questions and Clarifications (QAC) mechanism with Artificial Intelligence (AI) to generate meaningful questions and clarifications related to student’s geometry learning practice. In addition, we enabled multiple representations of the proposed Smart QAC, i.e., visual representation, logical representation, and linguistic representation.
We designed three stages of learning activities (i.e., contextualization, personalization, and socialization). In each stage, students need to answer the geometry-related questions before they receive the clarifications from AI. To investigate the influence of the Smart QAC Mechanism on student learning achievements, such as geometry ability (GA), spatial ability (SA) and estimation ability (EA), we conducted one quasi-experiment with 76 fifth-grade students. They were divided into three groups, i.e., the experimental group (EG) and two control groups (CG-A and CG-B). There were two tasks in the experiment. I.e., teacher design and free Exploration. The experiment lasted for nine weeks, and mixed-method analysis was used.
The results revealed some interesting findings. First, the EG significantly outperformed the CG-A and CG-B on learning achievement related to GA, SA, and EA. Second, The Smart QAC mechanism was strongly correlated with learning achievement. Furthermore, EG demonstrated higher measurement precision than CG-A, particularly in teacher design activities and free exploration. This result indicates that the smart QAC helps EG students be more successful at measuring precise objects. Moreover, the error rate of EG is less than CG-A and reaches a significant difference in free exploration activity. It implied that our proposed smart QAC mechanism enhances geometry understanding and ability in authentic contexts. More importantly, students showed a positive attitude and high intention toward Smart 3D-UG with the smart QAC. Therefore, enhanced smart QAC mechanisms can potentially be applied in class to support more complex geometry learning in authentic contexts.

關鍵字(中)

★ 幾何學習
★ 真實情境
★ 智慧 QAC 機制

關鍵字(英)

★ Geometry learning
★ authentic context
★ smart QAC mechanism

論文目次

List of Contents
Abstract i
Acknowledgments iv
List of Contents v
List of Figures vii
List of Tables viii
Chapter I Introduction 1
1.1 Research Background and Motivation 1
1.2 Purposes and Research Questions 4
Chapter 2 Literature Review 6
2.1 Multiple Representations Approach to Reduce Misconceptions in Geometry
Learning. 6
2.2 Using Authentic Context and Collaboration to Support Multiple Representations. 9
2.3 Augmented Reality to Support Geometry Learning. 12
2.4 Posing Question with GPT to Stimulate Deeper Thinking for Geometry Learning 13
2.5 Smart Posing Question for Contextualization, Personalization, and Socialization 17
Chapter 3 System Design and Implementation 19
3.1 System Design 19
3.2 Main Feature 19
3.3 The Smart QAC Mechanism for Three Stages Learning Activity in an Authentic Contexts 21
Chapter 4 Methodology 27
4.1. Participants 27
4.2. Research Framework 27
4.3 Experimental Procedure 32
4.4 Learning Activities 35
4.5. Experimental Instruments 36
4.6 Data Analysis Approach 38
Chapter 5 Results 40

5.1. Analysis of Learning Achievement 40

5.2 Comparison of QAC Behaviors for EG in Task 1 and Task 2 50
5.3 Comparison of Learning Behaviors between EG and CG-A Task 1 and Task 2 54
5.4 Relationship between Smart QAC Behaviors and Learning Achievements EG in Task 1 and Task 2. 57
5.5 Prediction of the Dependent Variables to Learning Achievements in the middle test 70
5.6 Prediction of the Dependent Variables to Learning Achievements in Posttest 71
5.7 Students′ Perception of Smart 3D-UG with Smart QAC Mechanism 73
Chapter 6 Conclusion 82
Reference 86
Appendix A: Pretest 95
Appendix B: Middle-Test 110
Appendix C: Post-Test 125
Appendix D: TAM Questionnaire 141
Appendix E: Semi-Structured Interview Question 146

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指導教授

Yuin Hwang(Wu Yuin Hwang)

審核日期

2024-1-17

推文