You are here:

Exploring factors that explain possible needs of mobile devices integrated in elearning through learning profiling
PROCEEDING

, The University of Texas Rio Grande Valley, United States ; , University of Central Florida, United States ; , The University of Texas Rio Grande Valley, United States

Society for Information Technology & Teacher Education International Conference, in Savannah, GA, United States ISBN 978-1-939797-13-1 Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA

Full Text

Abstract

Profiling elearning students is a common practice in the field. It carries good intention. Which learner group requires more attention of the university administration in optimizing resources and creating incentives resulting into a social outcome that is efficient and makes all concerned parties better off? Results suggested that the learners who perceive higher in university’s CMS support, instructor instructional and communicational use of CMS, and affinity for technology may deserve better attention of the management.

Citation

Pan, C.C., Sivo, S. & Garcia, F. (2016). Exploring factors that explain possible needs of mobile devices integrated in elearning through learning profiling. In G. Chamblee & L. Langub (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 895-898). Savannah, GA, United States: Association for the Advancement of Computing in Education (AACE). Retrieved February 19, 2020 from .

© 2016 Association for the Advancement of Computing in Education (AACE)

Keywords

References

View References & Citations Map
  1. Filho, D., Rocha, E., Siliva Júnior, J., Paranhos, R., Silva, M., & Duarte, B. (2014). Cluster analysis for political scientists. Applied Mathematics, 5, 2408-2415.
  2. Pan, C., & Garcia, F. (2015). Using learner profiling technique to predict college students’ tendency to choose elearning courses: A two-step cluster analysis. HETS Online Journal, 5(2), 129-147.
  3. Schiopu, D. (2010). Applying twostep cluster analysis for identifying bank customers’ profile. BULETINUL, 62(3), 66-75.
  4. Shih, M.-Y., Jheng, J.-W., & Lai, L.-L. (2010). A two-step method for clustering mixed categorical and numeric data. Tamkang Journal of Science and Engineering, 13(1), 11-19.
  5. Yu, C.H., DiGangi, S., Jannasch-Pennell, A.K., & Kaprolet, C. (2008). Profiling students who take online courses using data mining methods. Online Journal of Distance Learning Administration, 11(2). Retrieved from http://www.westga.edu/~distance/ojdla/summer112/yu112.html
  6. Yukselturk, E., & Top, E. (2013). Exploring the link among entry characteristics, participation behaviors and course outcomes of online learners: An examination of learner profile using cluster analysis. British Journal of Educational Technology, 44(5), 716-728.

These references have been extracted automatically and may have some errors. Signed in users can suggest corrections to these mistakes.

Suggest Corrections to References

Also Read

Related Collections