Data-Informed Learning Design: Social Network Interaction Development in Online Discussion

• In Event: Learning analytics: practice and applications

Short Description

“This proposal is submitted to the strand ‘Artificial Intelligence in Education” co-sponsored by International Division”

Data-Informed Learning (DIL) can support instructors and students in making responsive adjustments to their teaching and learning at the moment and lead to iterative improvements in learning designs. This study empirically investigated: How various aspects of students’ prominence (i.e., betweenness centrality, closeness centrality, eigenvector centrality, & PageRank) in the social network development of online discussion will change over time? And how may social learning analytics impact students’ social network interaction of online discussion over time. This study concluded students’ prominence in the social network interaction of discussion did not change, but declined closeness trend, over time, even it demonstrated strong community development. Based on the design context, instructions, graphs, communication, and personalization, the students suggested several strategies for effective DIL design.

Authors

• Presenter: Hoda Harati, Northern Arizona University

• Presenter: Chih Hsiung Tu, Northen Arizona University

• Contributor: Cherng-Jyh Yen, Old Dominion University

• Presenter: Benning Tieke, Northern Arizona University

• Contributor: Laura Sujo-Montes, Northern Arizona University