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: Convergence of Communities for Grounding, Implementation, and Validation
PublisherAssociation for Computing Machinery
Pages223-230
Number of pages8
Volume25-29-April-2016
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

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

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

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 (Vol. 25-29-April-2016, pp. 223-230). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883934

Longitudinal engagement, performance, and social connectivity : A MOOC case study using exponential random graph models. / Zhu, Mengxiao; Bergner, Yoav; Zhang, Yan; Baker, Ryan; Wang, Yuan; Paquette, Luc.

LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. Vol. 25-29-April-2016 Association for Computing Machinery, 2016. p. 223-230.

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

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. vol. 25-29-April-2016, Association for Computing Machinery, pp. 223-230, 6th International Conference on Learning Analytics and Knowledge, LAK 2016, Edinburgh, United Kingdom, 4/25/16. https://doi.org/10.1145/2883851.2883934
Zhu M, Bergner Y, Zhang Y, Baker R, Wang Y, Paquette L. 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. Vol. 25-29-April-2016. Association for Computing Machinery. 2016. p. 223-230 https://doi.org/10.1145/2883851.2883934
Zhu, Mengxiao ; Bergner, Yoav ; Zhang, Yan ; Baker, Ryan ; Wang, Yuan ; Paquette, Luc. / Longitudinal engagement, performance, and social connectivity : A MOOC case study using exponential random graph models. LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. Vol. 25-29-April-2016 Association for Computing Machinery, 2016. pp. 223-230
@inproceedings{263c5517d4c54db8a5c571126cfa8731,
title = "Longitudinal engagement, performance, and social connectivity: A MOOC case study using exponential random graph models",
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.",
keywords = "ERGM, Exponential random graph model, Forum participation, Learning, MOOC, Network analysis",
author = "Mengxiao Zhu and Yoav Bergner and Yan Zhang and Ryan Baker and Yuan Wang and Luc Paquette",
year = "2016",
month = "4",
day = "25",
doi = "10.1145/2883851.2883934",
language = "English (US)",
volume = "25-29-April-2016",
pages = "223--230",
booktitle = "LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation",
publisher = "Association for Computing Machinery",

}

TY - GEN

T1 - Longitudinal engagement, performance, and social connectivity

T2 - A MOOC case study using exponential random graph models

AU - Zhu, Mengxiao

AU - Bergner, Yoav

AU - Zhang, Yan

AU - Baker, Ryan

AU - Wang, Yuan

AU - Paquette, Luc

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

VL - 25-29-April-2016

SP - 223

EP - 230

BT - LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation

PB - Association for Computing Machinery

ER -