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 centroid trajectory with colored time points connected by blue path segments across the SVD1, SVD2, and SVD3 axes.
  • Ordered nodes
  • Direction
  • Group comparison

FROM DATA TO INTERPRETATION

A focused workflow for exploratory 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 de0e80c7100dae0164dcd0bf9a363d5bd1bdc588

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

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. Scale Factor, Edge Width Factor, grid, zero-line, and axis-arrow controls apply only to the legacy model views. Trajectory coordinates are intentionally never rescaled by display controls.

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:

Dr. Peter Hu Dongpin

Educational researcher · Application developer · Developer of 3D ENA Version 0.2.0

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

RESEARCH AGENDA

Technology that supports learning — and evidence that explains why.

His research integrates experiment-based inquiry, learning analytics, and application development to study meaningful educational change.

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 0.2.0 project is inspired by the previous 3D ENA Version 0.1.0. Dr. Peter Hu is charge of revolutionizing the 3D ENA tool since 2026 July 17. Welcome research collaboration worldwide.