Significant predictors of learning from student interactions with online learning objects

L. Dee Miller, Leen Kiat Soh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Learning objects (LOs) are self-contained, reusable units of learning. Previous research has shown that using LOs to supplement traditional lecture increases achievement and promotes success for college students in the disciplines of engineering and computer science. The computer-based nature for LOs allows for sophisticated tracking that can collect metadata about the individual learners. This tends to result in a tremendous amount of metadata collected on LOs. The challenge becomes identifying the predictors of learning. Previous research tends to be focused on a single area of metadata such as the learning strategies or demographic variables. Here we report on a comprehensive regression analysis conducted on variables in four widely different areas including LO interaction data, MSLQ survey responses (that measure learning strategies), demographic information, and LO evaluation survey data. Our analysis found that a subset of the variables in each area were actually significant predictors of learning. We also found that several static variables that appeared to be significant predictors in their own right were simply reflecting the results from student motivation. These results provide valuable insights into which variables are significant predictors. Further, they also help improve LO tracking systems allowing for the design of better online learning technologies.

Original languageEnglish (US)
Title of host publication2013 Frontiers in Education Conference
Subtitle of host publicationEnergizing the Future, FIE 2013 - Proceedings
Pages203-209
Number of pages7
DOIs
StatePublished - 2013
Event43rd IEEE Annual Frontiers in Education Conference, FIE 2013 - Oklahoma City, OK, United States
Duration: Oct 23 2013Oct 26 2013

Publication series

NameProceedings - Frontiers in Education Conference, FIE
ISSN (Print)1539-4565

Conference

Conference43rd IEEE Annual Frontiers in Education Conference, FIE 2013
CountryUnited States
CityOklahoma City, OK
Period10/23/1310/26/13

Keywords

  • Learning objects
  • Predictors of learning
  • Regression analysis

ASJC Scopus subject areas

  • Software
  • Education
  • Computer Science Applications

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