TY - GEN
T1 - Socio-temporal dynamics in peer interaction events
AU - Chen, Bodong
AU - Poquet, Oleksandra
PY - 2020/3/23
Y1 - 2020/3/23
N2 - Asynchronous online discussions are broadly used to support peer interaction in online and hybrid courses. In this paper, we argue that the analysis of online peer interactions would benefit from the focus on relational events that are temporal and occur due to a range of factors. To demonstrate the possibility, we applied Relational Event Modeling (REM) to a dataset from online discussions in seven online classes. Informed by a conceptual model of social interaction in online discussions, this modeling included (a) a learner attribute capturing aspects of temporal participation, (b) social dynamics factors such as preferential attachment and reciprocity, and (c) turnby-turn sequential patterns. Results showed that learner activity and familiarity from recent interactions affected their propensity to form ties. Turn-by-turn sequential patterns, that capture individual posting in bursts, explain how two-star network patterns form. Since two-star network patterns could further facilitate small group formation in the network, we expected the models to also capture communication in triads (i.e. triadic closure). Yet, models, devoid of the content of exchanges, did not capture the social dynamics well, and failed to predict patterns for communication across triads. By bringing in discourse features, future work can investigate the role of knowledge building behaviours in triadic closure of digital networks. This study contributes fresh insights into social interaction in online discussions, calls for attention to micro-level temporal patterns, and motivates future work to scaffold learner participation in similar contexts.
AB - Asynchronous online discussions are broadly used to support peer interaction in online and hybrid courses. In this paper, we argue that the analysis of online peer interactions would benefit from the focus on relational events that are temporal and occur due to a range of factors. To demonstrate the possibility, we applied Relational Event Modeling (REM) to a dataset from online discussions in seven online classes. Informed by a conceptual model of social interaction in online discussions, this modeling included (a) a learner attribute capturing aspects of temporal participation, (b) social dynamics factors such as preferential attachment and reciprocity, and (c) turnby-turn sequential patterns. Results showed that learner activity and familiarity from recent interactions affected their propensity to form ties. Turn-by-turn sequential patterns, that capture individual posting in bursts, explain how two-star network patterns form. Since two-star network patterns could further facilitate small group formation in the network, we expected the models to also capture communication in triads (i.e. triadic closure). Yet, models, devoid of the content of exchanges, did not capture the social dynamics well, and failed to predict patterns for communication across triads. By bringing in discourse features, future work can investigate the role of knowledge building behaviours in triadic closure of digital networks. This study contributes fresh insights into social interaction in online discussions, calls for attention to micro-level temporal patterns, and motivates future work to scaffold learner participation in similar contexts.
KW - Digital peer networks
KW - Relational event modelling
KW - Temporality
UR - http://www.scopus.com/inward/record.url?scp=85082398778&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082398778&partnerID=8YFLogxK
U2 - 10.1145/3375462.3375535
DO - 10.1145/3375462.3375535
M3 - Conference contribution
AN - SCOPUS:85082398778
T3 - ACM International Conference Proceeding Series
SP - 203
EP - 208
BT - LAK 2020 Conference Proceedings - Celebrating 10 years of LAK
PB - Association for Computing Machinery
T2 - 10th International Conference on Learning Analytics and Knowledge: Shaping the Future of the Field, LAK 2020
Y2 - 23 March 2020 through 27 March 2020
ER -