A Hands-on Workshop on A New Learning Analytics Tool
Heterogeneous Interaction Network Analysis (HINA)
April 28th, 2026
LAK'26 Bergen, Norway (In-person)
A Hands-on Workshop on A New Learning Analytics Tool
Heterogeneous Interaction Network Analysis (HINA)
April 28th, 2026
LAK'26 Bergen, Norway (In-person)
Welcome to HINA Workshop!
While learning analytics has advanced our ability to capture complex learning processes, methodological advancement is key in the field. This workshop will introduce a new learning analytics tool — HINA (Heterogeneous Interaction Network Analysis).
HINA offers a flexible technical solution to model and analyze interactions across diverse entities (e.g., students, behaviors, artefacts) as networked relationships. By transforming multimodal learning data into heterogeneous interaction networks (HINs), HINA enables researchers to uncover hidden patterns, test theoretical assumptions, and generate actionable insights about how learning unfolds.
This half-day workshop consists of two parts:
a hands-on tutorial introducing the HINA web tool together with its methodological foundations, empowering participants to apply network analytic techniques to their own learning datasets.
an interactive session, showcasing empirical applications through invited presentations, demonstrating HINA’s utility across contexts (e.g., collaborative problem solving, self-regulated learning, human-AI interactions). It will also feature lightning talks to spark discussion on new datasets and collaborations, concluding with a collaborative discussion about future directions for learning analytic tools’ development and adoption.
In this workshop, participants will gain the skills to analyze complex learning interactions using HINA, a versatile open-source tool specifically designed for learning analytics research. Participants will leave with practical, hands-on experience in network analysis and new connections to drive their own methodological research forward.
We are excited to meet you, receive your contributions, and collaborate to advance learning analytics research together!
Introduction of HINA:
HINA (Heterogeneous Interaction Network Analysis) is an open-source Python tool and web-based platform designed to model and analyze complex learning interactions through network science. Developed specifically for learning analytics research, HINA addresses a critical gap in existing methodologies by providing a framework to represent and study heterogeneous interactions—the multifaceted connections between diverse entities in learning environments, such as students, behaviors, artefacts, and multimodal data streams.
HINA’s modular design supports end-to-end analysis, including:
1) network construction that builds networks from learning process data
2) the individual-level analysis that includes two metrics for quantifying quantity and diversity of interaction patterns;
3) the dyadic-level analysis that identifies significant edges using null models;
4) the meso-level analysis that clusters nodes based on interaction patterns;
5) visualization & dashboard deployment that creates interpretable network diagrams and web-based dashboards
Users can access these functions via either of the following:
HINA webtool (hina-network.com).
HINA Python package (pip install hina) (https://hina.readthedocs.io/en/latest/index.html)
** The workshop will focus on introducing the HINA Webtool. There are no prerequisites for this workshop**
Organizing Committee:
Shihui Feng (University of Hong Kong, Hong Kong)
Baiyue He (University of Hong Kong, Hong Kong)
Mutlu Cukurova (University College London, UK)
Dragan Gasevic (Monash University, Australia)
Inquires about the workshop, please contact Shihui Feng, shihuife@hku.hk
Program:
Overview of the workshop
Get to know the participants
Introduce the methodological foundations of HINA
Hands-on tutorial of HINA Web Tool
Featured presentations of HINA applications:
Collaborative problem solving
Self-regulated learning
Human-AI collaboration
[Call For Contribution]
Lighting talks
Practical and participatory to analyze participants’ own dataset and co-develop solutions
Featured Presentations
Kester Yew Chong Wong, University College London
Title: Uncovering the Structural Effects of Scaffolding in Collaborative Problem Solving Processes using HINA
Collaborative Problem Solving (CPS) is a dynamic process between individuals and other members of the collective group involving concurrent engagement of cognitive, social, and affective domains of collaboration. Different studies have examined the impact of various forms of scaffolding to support learners during CPS. Here, we discuss the value of Heterogeneous Interaction Network Analysis (HINA) for expanding the range of methodological approaches that can be used to analyse CPS processes. In particular, the presentation will provide a practical demonstration on how HINA can be performed to analyse students' engagement and behaviours at the individual level in each distinct problem solving phase of CPS.
(Information will be updated as speakers are confirmed)
Call For Contribution
We are looking for two types of contributions:
New Challenges: A problem & a description of a dataset that you would like to explore using HINA. This track is for researchers new to HINA who want to assess its potential for their work. See the Template
Early Projects: A project that has used HINA with initial findings and seeks discussion. This track is for researchers who have started their HINA analysis and want to discuss their work and next steps. See the Template
Please submit a brief description (max two pages) of your challenge or project by 04 Dec 2025. Selected contributors will give a 5-minute lightning talk to frame the problem, followed by an extended group discussion to support the research development and identify solutions.
Submit here: Click LINK
Registration approach: through the LAK2026 official website.
For more information about HINA:
Feng, S., He, B., & Kirkley, A. (2025). HINA: A Learning Analytics Tool for Heterogenous Interaction Network Analysis in Python. Journal of Open Source Software, 10(111), 8299.
Example Studies Used Methods in HINA:
Feng, S., Gibson, D., & Gašević, D. . (2025). Analyzing Students’ Emerging Roles Based on Quantity and Heterogeneity of Individual Contributions in Small Group Online Collaborative Learning Using Bipartite Network Analysis. Journal of Learning Analytics, 12(1), 253-270. https://doi.org/10.18608/jla.2025.8431
Feng, S., Yan, L., Zhao, L., Maldonado, R. M., & Gašević, D. (2024, March). Heterogenous network analytics of small group teamwork: Using multimodal data to uncover individual behavioral engagement strategies. In Proceedings of the 14th learning analytics and knowledge conference (pp. 587-597).