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 是非商业平台，专为教育与研究目的而提供，并将永久免费。
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）与 Epistemic Network Analysis（ENA）的理论及方法研究之上。我们郑重致谢美国威斯康星大学麦迪逊分校（University of Wisconsin–Madison）、David Williamson Shaffer 教授，以及位于 Wisconsin Center for Education Research 的 Epistemic Analytics Lab 的研究人员、软件开发者与合作者，感谢他们在 QE、ENA 和 webENA 工具的创立与持续发展中作出的贡献。
QE 将对人类活动的情境化、质性理解与定量分析结合起来。 (Shaffer, 2017)
ENA 通过分析编码元素在有意义情境中的共同出现，建立连接网络，并支持网络的可视化和统计比较。 (Shaffer et al., 2016)
Open ENA 以这些研究为理论与方法基础，将编码数据、模型设置和网络比较组织为浏览器中的研究流程；研究者仍需结合研究问题、编码依据及原始质性资料解释结果。 (Shaffer & Ruis, 2017)
Open ENA 团队在建立本平台的初始界面外观与布局时，使用并参考了 webENA 工具。我们明确承认，这一初始界面参考源自美国威斯康星州麦迪逊市的原始 ENA 研究团队及其 webENA 设计工作。
ENA 网络可视化也参考了视觉设计与解读方面的研究。 (Tan et al., 2022)
Open ENA 是由 Open ENA 团队独立维护的平台。