Identifying Learners' Interaction Patterns in an Online Learning Community.

Area of Science:

• Online learning communities
• Educational technology
• Social network analysis

Background:

• Collaborative reflection (co-reflection) is crucial in online learning.
• Understanding learner roles and interaction patterns in co-reflection is limited.
• Previous research explored learner roles and co-reflection levels but not interaction timing and partners.

Purpose of the Study:

• To examine interaction patterns of diverse roles among learners with different co-reflection levels.
• To identify when and with whom learners at various co-reflection levels interact.
• To provide implications for facilitating online co-reflection based on role-based interactions.

Main Methods:

• Deep learning technique to assess co-reflection levels.
• Social network analysis (SNA) to identify emergent learner roles.
• Lag sequence analysis (LSA) to reveal interaction patterns.

Main Results:

• Most learners achieved upper-middle co-reflection levels but played inactive roles.
• Higher-level learners engaged in more diverse dialogues and self-rethinking.
• Higher-level learners initiated interactions with peers before engaging with instructors.

Conclusions:

• Learner roles significantly influence co-reflection dynamics in online communities.
• Facilitating interaction patterns, especially for higher-level learners, can enhance co-reflection.
• Targeted interventions based on emergent roles can improve collaborative learning outcomes.