Issues and topics derived from learning experience design job announcements: A latent Dirichlet allocation (LDA) analysis

• In Event: RTD- Issues and topics derived from learning experience design job announcements: A latent Dirichlet allocation (LDA) analysis

Short Description

There is a clear demand for learning experience design (LXD) practitioners in the labor market. However, given the recency of the LXD phenomenon, how to prepare students to fill these positions remains unclear. The purpose of the current study was to examine LXD-related job announcements using the latent Dirichlet allocation text mining approach. This study included 682 qualified job announcements from LinkedIn.com and Indeed.com. The topic modeling results unveiled five primary topics, allowing for insights into the nature of advertised LXD positions.

Authors

• Presenter: Matthew Schmidt, University of Florida

• Presenter: Albert Dieter Ritzhaupt, University of Florida

• Presenter: Xiaoman Wang, University of Florida

• Presenter: Rui (Tammy) Huang, College of Education, University of Florida

• Presenter: Jie (Jennifer) Lu, University of Florida

• Presenter: Minyoung Lee, University of Florida

• Contributor: Li Cheng, University of Florida