Longitudinal engagement, performance, and social connectivity: A MOOC case study using exponential random graph models

Mengxiao Zhu, Yoav Bergner, Yan Zhang, Ryan Baker, Yuan Wang, Luc Paquette

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationLAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact
Subtitle of host publicationConvergence of Communities for Grounding, Implementation, and Validation
PublisherAssociation for Computing Machinery
Pages223-230
Number of pages8
ISBN (Electronic)9781450341905
DOIs
StatePublished - Apr 25 2016
Event6th International Conference on Learning Analytics and Knowledge, LAK 2016 - Edinburgh, United Kingdom
Duration: Apr 25 2016Apr 29 2016

Publication series

NameACM International Conference Proceeding Series
Volume25-29-April-2016

Other

Other6th International Conference on Learning Analytics and Knowledge, LAK 2016
CountryUnited Kingdom
CityEdinburgh
Period4/25/164/29/16

Keywords

  • ERGM
  • Exponential random graph model
  • Forum participation
  • Learning
  • MOOC
  • Network analysis

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Zhu, M., Bergner, Y., Zhang, Y., Baker, R., Wang, Y., & Paquette, L. (2016). Longitudinal engagement, performance, and social connectivity: A MOOC case study using exponential random graph models. In LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation (pp. 223-230). (ACM International Conference Proceeding Series; Vol. 25-29-April-2016). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883934