TY - GEN
T1 - Longitudinal engagement, performance, and social connectivity
T2 - 6th International Conference on Learning Analytics and Knowledge, LAK 2016
AU - Zhu, Mengxiao
AU - Bergner, Yoav
AU - Zhang, Yan
AU - Baker, Ryan
AU - Wang, Yuan
AU - Paquette, Luc
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/4/25
Y1 - 2016/4/25
N2 - This paper explores a longitudinal approach to combining engagement, performance and social connectivity data from a MOOC using the framework of exponential random graph models (ERGMs). The idea is to model the social network in the discussion forum in a given week not only using performance (assignment scores) and overall engagement (lecture and discussion views) covariates within that week, but also on the same person-level covariates from adjacent previous and subsequent weeks. We find that over all eight weekly sessions, the social networks constructed from the forum interactions are relatively sparse and lack the tendency for preferential attachment. By analyzing data from the second week, we also find that individuals with higher performance scores from current, previous, and future weeks tend to be more connected in the social network. Engagement with lectures had significant but sometimes puzzling effects on social connectivity. However, the relationships between social connectivity, performance, and engagement weakened over time, and results were not stable across weeks.
AB - This paper explores a longitudinal approach to combining engagement, performance and social connectivity data from a MOOC using the framework of exponential random graph models (ERGMs). The idea is to model the social network in the discussion forum in a given week not only using performance (assignment scores) and overall engagement (lecture and discussion views) covariates within that week, but also on the same person-level covariates from adjacent previous and subsequent weeks. We find that over all eight weekly sessions, the social networks constructed from the forum interactions are relatively sparse and lack the tendency for preferential attachment. By analyzing data from the second week, we also find that individuals with higher performance scores from current, previous, and future weeks tend to be more connected in the social network. Engagement with lectures had significant but sometimes puzzling effects on social connectivity. However, the relationships between social connectivity, performance, and engagement weakened over time, and results were not stable across weeks.
KW - ERGM
KW - Exponential random graph model
KW - Forum participation
KW - Learning
KW - MOOC
KW - Network analysis
UR - http://www.scopus.com/inward/record.url?scp=84976491906&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84976491906&partnerID=8YFLogxK
U2 - 10.1145/2883851.2883934
DO - 10.1145/2883851.2883934
M3 - Conference contribution
AN - SCOPUS:84976491906
T3 - ACM International Conference Proceeding Series
SP - 223
EP - 230
BT - LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact
PB - Association for Computing Machinery
Y2 - 25 April 2016 through 29 April 2016
ER -