• DocumentCode
    3079009
  • Title

    Correlation between Course Tracking Variables and Academic Performance in Blended Online Courses

  • Author

    Che-Cheng Lin ; Chiung-Hui Chiu

  • Author_Institution
    Grad. Inst. of Inf. & Comput. Educ., NTNU, Taipei, Taiwan
  • fYear
    2013
  • fDate
    15-18 July 2013
  • Firstpage
    184
  • Lastpage
    188
  • Abstract
    The purpose of this research was to identify which course tracking variables correlate significantly with academic performance in blended asynchronous online courses through an empirical analysis of Learning Management System (LMS) data. In this study, course tracking variables refers to number of online sessions, number of original posts created, number of follow-up posts created, number of content pages viewed and number of posts read. Academic performance defined as how well a student´s final grad is. These five variables were collected from 15 undergraduate courses in the first semester of academic year 2012 at one national university in Taiwan. A total of 528 related final scores were transformed to z score and analyzed to investigate the correlation between course tracking variables and academic performance. A multiple regression analysis was used to evaluate how well course tracking variables measure predicted academic performance. Results indicated that approximately 16.4% of the variability in academic performance was accounted for by student´s course tracking variables measure, and three of the five variables were statistically significant.
  • Keywords
    computer aided instruction; educational courses; LMS data; Taiwan national university; academic performance; asynchronous online courses; blended online courses; content pages; course tracking variables; learning management system; undergraduate courses; Computers; Correlation; Data mining; Educational institutions; Least squares approximations; Regression analysis; Academic performance; ICT; academic analytics; data mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Learning Technologies (ICALT), 2013 IEEE 13th International Conference on
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/ICALT.2013.57
  • Filename
    6601900