Investigating Synchronous Interaction Behaviours Among Open University Students: A Latent Profile Analysis and Epistemic Network Analysis

ABSTRACT Background Online education plays a pivotal role in contemporary higher education systems, particularly for distance learners at open universities, who often face challenges such as low learner engagement and perceived social isolation. However, the heterogeneity of interaction behaviours among open university students—a population whose learning ecology is shaped by work‐study conflicts and constrained synchronous availability—remains critically underexplored. Objectives This study aimed to investigate synchronous and asynchronous interaction behaviours among open university students, identify distinct interaction profiles, and provide evidence‐based, profile‐specific insights for differentiated instructional design. Methods A mixed‐methods approach was employed, combining latent profile analysis (LPA) and epistemic network analysis (ENA). Unlike prior studies, this study uses LPA to identify interaction profiles grounded in theoretically derived dimensions of teacher‐student and student–student interactions across synchronous and asynchronous modalities, and ENA to uncover the cognitive discourse structures underlying those profiles, thereby bridging self‐reported behavioural frequencies with actual epistemic engagement. Data were collected from 372 students at a Chinese open university over 12 weeks. Results Three distinct interaction profiles emerged: (1) Teacher‐student interaction enthusiasts (TIE): preferred real‐time interactions with instructors; (2) selective asynchronous collaborators (SAC): Balanced synchronous and asynchronous peer collaboration; and (3) high interaction achievers (HIA): Actively engaged in all interaction types. ENA revealed varying cognitive and social engagement patterns across profiles. One‐way ANOVA confirmed significant performance differences among the three groups ( F (2, 369) = 107.14, p < 0.001, η 2 = 0.367), with HIA > SAC > TIE. Conclusions The findings underscore the importance of learner‐centric instructional design and provide actionable profile‐specific strategies: scaffolded real‐time responses for TIE, flexible blended designs for SAC, and leadership roles for HIA. These evidence‐based profiles advocate for inclusive digital education models that accommodate cognitive diversity in open university settings.

Authors

Institutions

Publication Details

Journal
Journal of Computer Assisted Learning
Published
2026-09-24
DOI
https://doi.org/10.1002/jcal.70339
Primary Topic
Innovative Teaching and Learning Methods
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Investigating Synchronous Interaction Behaviours Among Open University Students: A Latent Profile Analysis and Epistemic Network Analysis

Jin Zhu, Liu Jinglu, Ran Wang
Journal of Computer Assisted Learning
Innovative Teaching and Learning Methods
article

Investigating Synchronous Interaction Behaviours Among Open University Students: A Latent Profile Analysis and Epistemic Network Analysis

Jin Zhu, Liu Jinglu, Ran Wang
article en

Abstract

ABSTRACT Background Online education plays a pivotal role in contemporary higher education systems, particularly for distance learners at open universities, who often face challenges such as low learner engagement and perceived social isolation. However, the heterogeneity of interaction behaviours among open university students—a population whose learning ecology is shaped by work‐study conflicts and constrained synchronous availability—remains critically underexplored. Objectives This study aimed to investigate synchronous and asynchronous interaction behaviours among open university students, identify distinct interaction profiles, and provide evidence‐based, profile‐specific insights for differentiated instructional design. Methods A mixed‐methods approach was employed, combining latent profile analysis (LPA) and epistemic network analysis (ENA). Unlike prior studies, this study uses LPA to identify interaction profiles grounded in theoretically derived dimensions of teacher‐student and student–student interactions across synchronous and asynchronous modalities, and ENA to uncover the cognitive discourse structures underlying those profiles, thereby bridging self‐reported behavioural frequencies with actual epistemic engagement. Data were collected from 372 students at a Chinese open university over 12 weeks. Results Three distinct interaction profiles emerged: (1) Teacher‐student interaction enthusiasts (TIE): preferred real‐time interactions with instructors; (2) selective asynchronous collaborators (SAC): Balanced synchronous and asynchronous peer collaboration; and (3) high interaction achievers (HIA): Actively engaged in all interaction types. ENA revealed varying cognitive and social engagement patterns across profiles. One‐way ANOVA confirmed significant performance differences among the three groups ( F (2, 369) = 107.14, p < 0.001, η 2 = 0.367), with HIA > SAC > TIE. Conclusions The findings underscore the importance of learner‐centric instructional design and provide actionable profile‐specific strategies: scaffolded real‐time responses for TIE, flexible blended designs for SAC, and leadership roles for HIA. These evidence‐based profiles advocate for inclusive digital education models that accommodate cognitive diversity in open university settings.

Journal of Computer Assisted LearningVol. 42(6)
Open University of China (CN), Beijing Open University (CN), China Development Bank (CN), Bank of China (CN)
Quality Education
Openalex Percentile: Top 5%
Innovative Teaching and Learning Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.