Socio-Temporal Dynamics in Peer Interaction Events Bodong Chen, University of Minnesota Oleksandra Poquet, University of South Australia #LAK20, March 25, 2020 [Link to paper: bit.ly/lak20-rem] 

Social Participation in online/blended learning Photo Credit 

Social Participation Is linked to: Academic performance “Soft skills” Sense of belonging Social capital … Photo Credit (Jo, Park & Lee, 2017; Joksimović, Gasević, Kovanovic, Riecke & Hatala, 2015; Zheng & Warschauer, 2015) 

Social Participation & Peer Interaction Pedagogical models Digital tools, social media Informal spaces … Various attempts to foster Photo Credit (Dennen, 2005; Marbouti & Wise, 2016; Sadowski, Pediaditis & Townsend, 2017; Wang, 2005; Wise & Chiu, 2011) 

There is a participation gap in classroom discussions (Chen & Huang, 2019; Vaquero & Cebrian, 2013) 

Social Participation & Peer Interaction A methodological gap in analyzing Photo Credit 

A major, enduring question: What are the mechanisms of social interaction in asynchronous online discussions? Photo Credit 

Framework 

Research question: How does socio-temporal dynamics contribute to social interaction in asynchronous online discussions? Photo Credit 

Study Context Courses n n_posts n_activity 2711 Technology and Ethics 21 276 1137 2713 Info Systems 82 795 3470 2758 Operations Management 29 110 829 2768 IT and Solutions A 33 277 1031 2839 IT and Solutions B 20 189 652 2853 Innovation Management A 43 159 1379 3446 Innovation Management B 43 121 1389 a pin reactions replies A board 

Data Analysis 1) Temporal profiles of learners Logs each day of the week Finite mixture models Time-consistent posters Last-minute posters 

Sequential Structural Signatures (SSSs) 2) Relational Event Modeling (REM) i j i j i j i j t (Butts, 2008; Leenders et al., 2015; Pilny et al., 2016) Past relational events Actor attributes Exogenous factors The next event 

Effects Visualization Description Past relational events Nodal: Total Degree (NTDegRec) Dyadic: Familiarity (RSndSnd) Reciprocity (RRecSnd) Triadic: Outbounding shared partner (OSPSnd) Well-connected students to receive more interactions Past interactions to happen again Past interactions to be reciprocated Shared outbounding partners → direct interactions Modeled SSSs and Actor Attributes i j i j k i j t t’ i j t t’ 

Effects Description Past relational events (cont’d) Immediate Effects: Participation Shifts (Gibson, 2005): AB → BA AB → BY AB → AY AB → XB Reciprocation Pay-it-forward or hand-off Activity bursts, persistence of source Popularity, persistence of target Actor attributes (controls) Time-consistent Role Being time-consistent (vs. last-minute) Instructor (vs. student) Modeled SSSs and Actor Attributes (cont’d) 

Findings 

Course 1 How the interaction networks look like? 

Relational Event Models 

Key REM results (1) -- node/actor attributes ● Time-consistent learners → Positive tendency to reply to others (4/7) → Mixed tendency to receive replies (2+,1- / 7) ● Being the instructor → Positive tendency to reply to others (4/7) → Negative tendency to receive replies (6/7) 

Key REM results (2) -- socio-temporal structures ● Learner total degree → Positive tendency to receive replies (7/7) ● Past A→B ties → Positive tendency to A→B ties (7/7) → Positive tendency to B→A ties (6/7) ● A and B have an outbounding shared partner (e.g., A→C←B) → Only weak positive tendency to A→B ties (4/7) 

Key REM results (3) -- immediate effects ● One A→B tie → Strong positive tendency to have a A→Y tie immediately next (7/7) → Positive tendency to have a Y→B tie immediately next (6/7) → Positive tendency to have a B→A tie immediately next (4/7) → Mixed results about having a B→Y tie immediately next (3+,1- / 7) 

Key Takeaways ● Peer interaction is shaped by a range of factors. ● The analysis of peer interaction can benefit from focusing on relational events rather than relational states. ● Social dynamics and temporal dynamics in peer interaction networks are intertwined. 

Limitations and Future Work ● Data sources were limited to timestamped activity logs ○ No information about demographics ○ No information about post content ● Combine relational event modeling w/ discourse analysis ● Develop and contrast pedagogical strategies ● Novel statistics for learning analytics tools 

Thank You! Bodong Chen Email: [email protected] Twitter: @bod0ng Website: bodong.me Sasha Poquet Email: [email protected] Twitter: @choux Website: learningpoop.com