Dependencies between E-Learning Usage Patterns and Learning Results

  • Authors:

  • Felix Mödritscher;Monika Andergassen;Gustaf Neumann

  • Affiliations:

  • Institute of Information Systems and New Media, Vienna University of Economics and Business, Augasse 2-6, 1090 Vienna, Austria, +43-1-31336-5277;Institute of Information Systems and New Media, Vienna University of Economics and Business, Augasse 2-6, 1090 Vienna, Austria, +43-1-31336-4431;Institute of Information Systems and New Media, Vienna University of Economics and Business, Augasse 2-6, 1090 Vienna, Austria, +43-1-31336-4671

  • Venue:
  • Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies
  • Year:
  • 2013

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Abstract

Recent studies come to the conclusion that Learning Management System (LMS) usage variables explain a higher variation in students' final grades than traditional student characteristics. Referring to such findings from literature our research aims at exploring dependencies between e-learning usage patterns and achieved learning results on the basis of LMS log-files from courses in different knowledge domains, and by analyzing courses not in isolation but by taking potential dependencies with students' activities in other courses into account. We examine correlations between usage variables and the students' performance in three blended learning courses with different topics based on large cohorts of students (n=883, n=389, n=578). In this context, an extended set of variables, including LMS usage beyond the three courses and usage patterns of students, are examined for interdependencies. Our results indicate that specific indicators, such as the number of active learning days and topic views, have a positive influence on learning results. In general, they show that at-risk students can be differentiated from well-performing students by their usage behavior. Moreover, we try to identify some significant patterns of LMS usage amongst the students. The paper shows how these patterns differ in our observations depending on the course domains.