TY - JOUR
T1 - What do you mean by collaboration analytics?
T2 - A conceptual model
AU - Martinez-Maldonado, Roberto
AU - Gašević, Dragan
AU - Echeverria, Vanessa
AU - Fernandez Nieto, Gloria
AU - Swiecki, Zachari
AU - Shum, Simon Buckingham
N1 - Funding Information:
partly funded by Jacobs Foundation.
Publisher Copyright:
© 2021, UTS ePRESS. All rights reserved.
PY - 2021/4/8
Y1 - 2021/4/8
N2 - Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.
AB - Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.
KW - Collaborative learning
KW - CSCL
KW - CSCW
KW - Group work
KW - Learning design
KW - Multimodal learning analytics
KW - Teamwork
UR - http://www.scopus.com/inward/record.url?scp=85107493670&partnerID=8YFLogxK
U2 - 10.18608/jla.2021.7227
DO - 10.18608/jla.2021.7227
M3 - Article
AN - SCOPUS:85107493670
SN - 1929-7750
VL - 8
SP - 126
EP - 153
JO - Journal of Learning Analytics
JF - Journal of Learning Analytics
IS - 1
ER -