Global Themes and Future Trends of Mobile Learning: Data Mining of Publications in AACE EDITLib Digital Library Database

ID: 34104 Type: Full Paper: Conceptual & Empirical Study
  1. Ke Zhang, Wayne State University, United States
  2. Juilong Hung, Boise State University, United States

Thursday, June 30 10:30 AM-11:00 AM Location: Room 4 - Faculty of Letters Building

No presider for this session.

This session reports a data mining study investigating the longitudinal trends of mobile learning (ML) publications as indexed in AACE EDITLib Digital Library Database. A total of 634 proceeding articles were retrieved in key words searches on mobile learning, and all retrieved abstracts were then mined and analyzed. SAS Enterprise Miner 6.1 was employed to conduct text mining. The overall time trends and top prolific countries of ML publications were identified. In addition, a three-level, twelve-cluster hierarchical taxonomy of ML was generated to illustrate the themes and sub-themes of ML publications. Findings were further analyzed by comparing against the most recent research review on ML publication indexed in SCI/SSCI database (Hung & Zhang, in press). Rich discussions follow to further address the global themes and future trends of mobile learning research and publications.

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