15069

15069

General Session - Conference Presentation Only (20 minutes, no formal paper)

Cheng ChangPAN, University of Texas at Brownsville, Brownsville, Texas, USA, sam.pan@utb.edu Clair Goldsmith, University of Texas at Brownsville, Brownsville, Texas, USA, Clair.Goldsmith@utb.edu Francisco Garcia, University of Texas at Brownsville, Brownsville, Texas, USA, Francisco.Garcia@utb.edu
 * Identifying elearning student needs through learner profiling: An exploratory, two-step cluster analysis study **

Profiling elearning students is becoming a common practice in the field (e.g., Yukselturk & Top, 2013). Using a Web survey or questionnaire, an increasing number of learner characteristics and demographics can be studied in a form of data. Given this easy access to the collected data, researchers have attempted to take into account multiple (i.e., two- or more-way) profiling variables (e.g., gender and grade level) at once, in lieu of dealing with one variable at a time. This attempt makes the design of their study more sophisticated and more versatile. It also assists the researchers in finding hidden patterns of the learners and their behaviors (Shih, Jheng, & Lai, 2010). Most importantly, their study results enable the top management team to make informed decisions. One major advantage of two-step cluster analysis is it allows researchers to consider both continuous/numerical and categorical/nominal variables at a time as other clustering techniques, such as K-Means Cluster and Hierarchical Cluster in SPSS, are limited respectively, as Şchiopu (2010) pointed out. In this phase of the investigation, we plan to (a) follow up on the recommendation for further research we stated in an earlier study on learner preference in the type of the elearning course and their use of social networking tools and (b) explore plausible patterns of learner characteristics/behaviors in relation to their use of social networking tools and choices over the elearning course types. That is, the secondary or archival data with a sample size of approximately 2,000 (undergraduate students of a U.S. southern state university) will be analyzed for the present quantitative study. The data were initially collected in a joint effort of the participating university and EDCAUSE Center for Applied Research (ECAR) in 2013. This short general session is intended to benefit university distance education management team members and other related policy makers. References: Pan, C., Sivo, S., García, F., Goldsmith, C., & Cornell, R. A. (2014, October). Technology and me--what do students think? Paper presented at the 64th International Council for Educational Media (ICEM 2014) Conference, Eger, Hungary. Şchiopu, D. (2010). Applying twostep cluster analysis for identifying bank customers’ profile. BULETINUL, 62(3), 66-75. 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. Yukselturk, E., & Top, E. (2013). Exploring the link among entry characteristics

<span style="font-family: 'Times New Roman',serif; font-size: 12pt; line-height: 1.5;">All Audiences <span style="font-family: 'Times New Roman',serif; font-size: 12pt;">Two step cluster analysis; learner profiling; elearning