- 學位論文
使用認知網絡分析探討混成式自主學習課程
Exploring Blended Self-directed Learning Courses using Epistemic Network Analysis
摘要
全面評估專業技能和有效分工合作已成為現代社會中一個重要的研究課題，不論 是個人或群體社會，均需要整體共同運作的思維與模式。在大數據時代，此課題進一 步與教育科學分析相結合，成為一個重要的研究產物。認知網絡分析法(Epistemic Network Analysis, ENA)通過擷取對話中的思維和想法，經過量化加工和認知元素的結 合，形成一個直觀的動態網絡模型，並能進行深度思維分析。 ENA 能夠捕捉學習者之間的互動關係，揭示他們在知識、概念和任務之間是如何 連結的，這對於深入理解學習者的學習策略、知識結構和知識傳遞模式至關重要，而 且 ENA 的優勢不僅限於單一領域，它在教育、心理學和認知科學等多個領域都有著廣 泛的應用潛力，教學者可以利用 ENA 來研究學生在不同學科和學習環境下的表現，從 而優化教學策略和課程設計，幫助學生更好地掌握知識和技能。 研究中結合了 STEM 教育和混成式自主學習的教學模式，以促進學生在學習過程 中的問題解決能力和創新思維。學生透過網上平台進行自主學習，包括影片觀看、課 程討論、小組線上線下討論等，探索 STEM 領域的知識和應用。同時，他們也參與實 體教室中的實驗和設計活動，與同學和教師進行互動。 本研究運用 ENA 方法，總結出四種不同層次的認知網絡動態模型，包括個體認知 模型、群體認知模型、個體認知轉變行為軌跡模型、以及整體認知轉變行為軌跡模型。 我們運用這四個模型對台灣北部某大學通識課程中 12 名學生在一個學期內提交的個人 文書作業進行建模，並分析其結果。
並列摘要
Assessing professional skills comprehensively and promoting effective collaboration in specialized tasks has become a crucial research topic in modern society. Whether at an individual or societal level, there is a need for holistic and cooperative thinking patterns. In the era of big data, this issue has further intertwined with the analysis of educational science, evolving into a significant research outcome. Epistemic Network Analysis (ENA), by extracting thoughts and ideas from conversations, employs quantitative processing and combines cognitive elements to form an intuitive dynamic network model. It enables in-depth analysis of thought processes and serves as a valuable tool in the big data era. ENA has the ability to capture interaction relationships among learners, revealing how they are connected in terms of knowledge, concepts, and tasks. This is crucial for a profound understanding of learners' learning strategies, knowledge structures, and knowledge transmission patterns. Moreover, the advantages of ENA are not confined to a single domain; it holds broad potential applications in various fields such as education, psychology, and cognitive science. Educators can leverage ENA to study students' performance in different subjects and learning environments, optimizing teaching strategies and curriculum design to assist students in mastering knowledge and skills more effectively. The research integrates STEM education with a hybrid self-directed learning instructional model to enhance students' problem-solving skills and innovative thinking during the learning process. Students engage in self-directed learning through online platforms, including watching videos, participating in course discussions, and engaging in both online and offline group discussions, to explore knowledge and applications in the STEM field. Simultaneously, they participate in experiments and design activities in physical classrooms, interacting with peers and teachers. This study applies the Epistemic Network Analysis (ENA) method to summarize four different levels of dynamic cognitive network models. These include individual cognitive models, group cognitive models, individual cognitive transition trajectory models, and overall cognitive transition trajectory models. We utilize these four models to analyze and model the personal essay assignments submitted by 12 students in a general education course at a university in northern Taiwan over the course of one semester, and analyze the results.