A small team. An open horizon.
Open ENA brings together learning science, quantitative ethnography, artificial intelligence, and educational technology.
We are building a transparent and approachable path into Epistemic Network Analysis—one where methods, interface decisions, and future capabilities can be inspected rather than hidden.
Openness is more than access.
It means making the path from evidence to interpretation visible: what the model receives, what the computation does, what the figure shows, and what it cannot claim.
Open ENA 是非商業平台，專為教育與研究目的而提供，並將永久免費。
The Education University of Hong Kong
Educational researcher · Application developer
His work spans technology-enhanced learning, learning analytics, artificial intelligence in education, network analysis, and content and language integrated learning.
EdUHK profile
3D ENA Research Group · Hong Kong
ENA variants · Dimensionality-reduction algorithms
He has played a pioneering role in advancing ENA variants such as 3D ENA and dimensionality-reduction algorithms for ENA. As the key developer of 3D ENA 1.0, he combines computer science and psychology to develop interactive research tools.
3D ENA team profile
National Taiwan University of Science and Technology
Assistant Professor · Graduate Institute of Digital Learning and Education
Her work spans artificial intelligence in digital learning, cognitive and social networks, data literacy, data visualization, and graph analysis.
Taiwan Tech profile
Meredith College, North Carolina, USA
Open ENA team
A member of the Open ENA team affiliated with Meredith College, North Carolina, USA.
Forthcoming conference paper · 2026
Hu, D., Hamilton, E., Tu, Y. F., & Xu, Q. (2026, November). Design and development from rENA to jENA: Accelerating the creation of web-based Open ENA tools. [Conference paper]. International Conference on Quantitative Ethnography.
ICQE 2026 · Publication details forthcoming
Journal article · 2025
Tu, Y. F., Hwang, G. J., & Hu, D. (2025). Effects on the learning achievement, approaches to learning, and multi-stage reflection quality of students with different levels of digital self-efficacy in a data literacy course: An ARCS-based self-reflective online learning model. Computers & Education, 238, 105397. https://doi.org/10.1016/j.compedu.2025.105397
View journal article
Conference paper · 2024
Yu, J., Hu, D., & Wang, C. H. (2024). Development of ENA 3D: A tool for epistemic network analysis in three-dimensional space. In Y. J. Kim & Z. Swiecki (Eds.), Advances in quantitative ethnography (Communications in Computer and Information Science, Vol. 2278, pp. 152–165). Springer. https://doi.org/10.1007/978-3-031-76335-9_11
View conference paperOpen ENA 建立在 Quantitative Ethnography（QE）與 Epistemic Network Analysis（ENA）的理論及方法研究之上。我們鄭重致謝美國威斯康辛大學麥迪遜分校（University of Wisconsin–Madison）、David Williamson Shaffer 教授，以及位於 Wisconsin Center for Education Research 的 Epistemic Analytics Lab 的研究人員、軟體開發者與合作者，感謝他們在 QE、ENA 和 webENA 工具的創立與持續發展中作出的貢獻。
QE 將對人類活動的情境化、質性理解與定量分析結合起來。 (Shaffer, 2017)
ENA 透過分析編碼元素在有意義情境中的共同出現，建立連結網絡，並支援網絡的視覺化和統計比較。 (Shaffer et al., 2016)
Open ENA 以這些研究為理論與方法基礎，將編碼資料、模型設定和網絡比較組織為瀏覽器中的研究流程；研究者仍須結合研究問題、編碼依據及原始質性資料解釋結果。 (Shaffer & Ruis, 2017)
Open ENA 團隊在建立本平台的初始介面外觀與版面配置時，使用並參考了 webENA 工具。我們明確承認，這一初始介面參考源自美國威斯康辛州麥迪遜市的原始 ENA 研究團隊及其 webENA 設計工作。
ENA 網絡視覺化亦參考了視覺設計與解讀方面的研究。 (Tan et al., 2022)
Open ENA 是由 Open ENA 團隊獨立維護的平台。