Make epistemic connections visible in three dimensions.

Explore knowledge structures in a shared 3D rotation, compare groups, and trace ordered centroid paths to see how networks move over time.

TIME
Ordered centroids
2–3D
Linked views
jENA
PARITY for rENA

TRAJECTORY ANALYSIS

Follow change through time

CORE FEATURE
A three-dimensional ENA visualization with labeled points, group trajectories, and red, blue, and green SVD axes.

FROM DATA TO INTERPRETATION

A focused workflow for ENA research

Move from a validated ENA dataset to interpretable spatial, network, statistical, and longitudinal views without leaving the workspace.

  1. 01

    Load

    Build ENA from raw Excel or CSV data, or start with a reviewed sample or versioned .ena3d.json exchange file.

  2. 02

    Configure

    Choose ENA dimensions, groups, comparison settings, and plot controls.

  3. 03

    Interpret

    Inspect networks, differences, statistics, and ordered centroid trajectories.

DESIGNED FOR RESEARCH

A visual analytics workspace should make complex relationships easier to examine while keeping analytical choices visible.

3D ENA

Version 0.2.0-dev · Build 890cf5144a6543fca3c261df980d161db6682156

Build ENA from raw Excel or CSV

Upload coded rows, map participant, sequence, group, and code fields, then build the ENA model in this browser session.


Open prepared ENA data




Centroid trajectory

This filter changes only the displayed paths and unit points; all levels remain in the shared ENA rotation and computed analysis.
The paired comparison matches the same entity IDs within each time period and uses the bootstrap settings below.
Review the generated order, especially for labeled character values. Generated values use one line each so labels may contain commas. Every observed time value must appear exactly once. You may also add expected periods with no observations so gaps remain explicit.
Auto uses global clusters when eligible raw IDs overlap between groups; otherwise it preserves each group's sample size. Choose explicitly when the study's ID namespace is known. At least 80% of replicates and five expected replicates per confidence-interval tail are required. The hosted application defaults to 500 repetitions and accepts 200–500 per run; full-rotation bootstraps can take substantially longer.
Plot Tools scope

X/Y/Z axes and Camera Position apply here. The 3D trajectory view shows red, blue, and green positive-axis arrows from the origin, labeled with the selected ENA dimensions. Scale Factor, Edge Width Factor, grid, zero-line, and legacy axis-arrow controls do not alter trajectory coordinates.





Only tests compatible with the selected design are calculated. Adjusted p-values cover all displayed axes/tests in that family.
For a repeated design, results are computed only after matching both groups by this ID. Independent tests are disabled.
X-axis:
Effect size (Group 1 - Group 2):
Raw p-value:

Y-axis:
Effect size (Group 1 - Group 2):
Raw p-value:

Z-axis:
Effect size (Group 1 - Group 2):
Raw p-value:
X-axis:
Effect size (Group 1 - Group 2):
Raw p-value:

Y-axis:
Effect size (Group 1 - Group 2):
Raw p-value:

Z-axis:
Effect size (Group 1 - Group 2):
Raw p-value:
X-axis:
Effect size (Group 1 - Group 2):
Raw p-value:

Y-axis:
Effect size (Group 1 - Group 2):
Raw p-value:

Z-axis:
Effect size (Group 1 - Group 2):
Raw p-value:

PAPERS & CITATION

Cite the work behind 3D ENA.

If 3D ENA supports your analysis, cite the foundational method paper. The application studies below show how the approach has been used in educational research and political research.

Three verified references

Bibliographic details were checked against publisher and DOI records. View the source collection

02 APPLICATION · POLITICAL DISCOURSE

The Application of ENA to Political Discourse in Taiwan: A Case Study

Yu, J., Hamilton, E., Wang, C.-H., & Hu, D. (2024). The application of ENA to political discourse in Taiwan: A case study. In Y. J. Kim & Z. Swiecki (Eds.), Advances in quantitative ethnography (pp. 273–287). Springer. https://doi.org/10.1007/978-3-031-76332-8_22

03 APPLICATION · LEARNING RESEARCH

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

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

TEAM

Meet the 3D ENA Research Team

Nine scholars connect educational technology, learning analytics, learning sciences, artificial intelligence, mathematics and language education, pedagogy, and policy to make educational evidence more useful.

SCHOLARS

Meet the team.

An interdisciplinary group united by a shared interest in how people learn, how evidence is modeled, and how technology can support better educational decisions.

Portrait of Dr. Yun-Fang Tu

National Taiwan University of Science and Technology

Dr. Yun-Fang Tu

Dr. Tu studies how generative AI and digital learning shape learner perceptions, behavior, and outcomes. Her work combines educational data mining, visual and network analysis, and mobile-learning research to make learning processes more visible and actionable.

  • Generative AI in education
  • Digital and mobile learning
  • Educational data mining
  • Network analysis
Recent research
Portrait of Dr. Peter Hu Dongpin

Educational researcher · Application developer

Dr. Peter Hu Dongpin

Developer of 3D ENA Version 2.0

Dr. Hu develops theory-informed learning environments and analytical tools that connect educational research with practical technology. His work uses learning analytics, network analysis, and AI to explain and improve learning.

  • Technology-enhanced learning
  • Learning analytics
  • Artificial intelligence in education
  • Application development
More on About
Portrait of Mr. YU Jianxing

Quantitative Ethnography · Application Development

Mr. YU Jianxing

3D ENA Research Group · Hong Kong BSc Computer Science · MA Psychology

Mr. Yu combines computer science and psychology to develop interactive tools for epistemic network analysis and quantitative ethnography. He is the key developer of 3D ENA 1.0. His research applies network analysis to political discourse, social-media data, and three-dimensional ENA visualization.

  • Epistemic network analysis
  • Quantitative ethnography
  • Political discourse
  • Research software development
ENA 3D research
Portrait of Dr. Huang Lingyun

Assistant Professor · Curriculum and Instruction

Dr. Huang Lingyun

The Education University of Hong Kong PhD · McGill University

Dr. Huang designs intelligent, adaptive learning environments and uses AI-enabled learning analytics to uncover cognitive, metacognitive, behavioral, and emotional patterns in learning trajectories. His work translates learning-science evidence into more effective teaching and learning.

  • Technology-rich learning
  • AI-enabled learning analytics
  • Regulated learning
  • Teacher digital literacy
University profile
Portrait of Dr. Phoebe, KANG Xia

Senior Lecturer · Mathematics Education

Dr. Phoebe, KANG Xia

School of Mathematics and Information Science · Guangzhou University PhD · The University of Hong Kong

Dr. Kang researches how emotions, motivation, and culture shape mathematics learning. Her work combines educational measurement with quantitative and qualitative inquiry to understand differences in secondary students’ mathematical experiences.

  • Mathematics education
  • Achievement emotions
  • Educational measurement
  • Cross-cultural learning
Portrait of Dr. WU Yajun

Senior Lecturer · Applied Linguistics

Dr. WU Yajun

School of Humanities · Foshan University

Dr. Wu studies digital literacy and the motivational and relational foundations of English-language learning. His research examines how teacher support, learner grit, engagement, and technology use shape EFL achievement.

  • Teacher digital literacy
  • Applied linguistics
  • EFL motivation
  • Student engagement
Portrait of Dr. Cao Yuan

Postdoctoral Fellow · Curriculum and Instruction

Dr. Cao Yuan

The Education University of Hong Kong

Dr. Cao researches how digital and AI-supported self-assessment can strengthen reflection, engagement, and learning in higher education. Her work connects educational technology with evidence-based assessment design and data-informed teaching.

  • AI-supported self-assessment
  • Higher education assessment
  • Educational technology
  • Learner engagement
Portrait of Dr. LI Jun

Shadow Education · Education Policy

Dr. LI Jun

Member · HKU Shadow Education SIG PhD · The University of Hong Kong

Dr. Li studies shadow education and private tutoring through qualitative, social-network, and policy lenses. His work examines guanxi, brokerage, regulation, and educational equity within changing education systems.

  • Shadow education
  • Education policy
  • Social network research
  • Educational equity
Recent research

Dr. Peter Hu Dongpin

Educational researcher · Application developer · Developer of 3D ENA Version 2.0

Dr. Peter Hu develops theory-informed, evidence-based learning environments and analytical tools that connect educational research with practical technology.

His work asks how learning technology can improve outcomes and how evidence from learning processes can explain and predict that progress.

Visit academic profile
Portrait of Dr. Peter Hu Dongpin
Dr. Peter Hu Dongpin Educational researcher and application developer

EDUCATION

Interdisciplinary by design

  • PhD in Educational Technology, The University of Hong Kong
  • BSc in Computer Science (Machine Learning & AI), University of London

RESEARCH AREAS

Learning, technology, and evidence

  • Technology-enhanced learning
  • Learning analytics and network analysis
  • Artificial intelligence in education
  • Content and language integrated learning

3D ENA

Explore the research tool.

Move directly into the complete interactive 3D ENA workspace.

The 3D ENA Version 2.0 project is inspired by the previous 3D ENA Version 1.0. Dr. Peter Hu is charge of revolutionizing the 3D ENA tool since 2026 July 17. Welcome research collaboration worldwide.