Looking for Usage Patterns in e-Learning Platforms - A Step Towards Adaptive Environments

This paper studies the student view of functionality offered by a research-based design of a blended learning environment. The course in question is a Software Engineering course at the Cooperative State University of Baden-Wurttemberg in Karlsruhe. At our University, students alternate between study and work in a quarter-based system, completing their study in three years. Based on findings over the last years, the course is currently using an e-learning platform (coursesites) to enhance the classroom. For this paper, students were asked to evaluate the components of the platform and the functionalities offered. The results of the survey (77 students) shows which features of the platform are most used. Correlating the answers with theoretical model of types and platform offerings shows that functionality use can be a predictor of learner type. We show that the student body is highly fragmented between learner types with the main groups consisting of 38% avoidant, 27% collaborative/participant, and 10% competitive learner types. A single platform will not cover any mixed group of students and configurable views need to be considered in future.

[1]  E. Deci,et al.  Motivation, personality, and development within embedded social contexts: An overview of self-determination theory. , 2012 .

[2]  Peter Shea,et al.  Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments , 2010, Comput. Educ..

[3]  Alexander Schober,et al.  Impact factors for learner motivation in Blended Learning environments , 2012, 2012 15th International Conference on Interactive Collaborative Learning (ICL).

[4]  Michael Derntl,et al.  The role of structure, patterns, and people in blended learning , 2005, Internet High. Educ..

[5]  D. Garrison,et al.  Blended learning: Uncovering its transformative potential in higher education , 2004, Internet High. Educ..

[6]  E. Deci,et al.  Self‐determination theory and work motivation , 2005 .

[7]  A. Maslow.  A Theory of Human Motivation , 1943 .

[8]  Anthony F. Grasha,et al.  A practical handbook for college teachers , 1983 .

[9]  Kay M. Berkling,et al.  Understanding the Challenges of Introducing Self-driven Blended Learning in a Restrictive Ecosystem - Step 1 for Change Management: Understanding Student Motivation , 2013, CSEDU.

[10]  Teklu Abate Bekele,et al.  Motivation and Satisfaction in Internet-Supported Learning Environments: A Review , 2010, J. Educ. Technol. Soc..

[11]  James B. Rebitzer,et al.  Extrinsic Rewards and Intrinsic Motives: Standard and Behavioral Approaches to Agency and Labor Markets , 2010, SSRN Electronic Journal.

[12]  Ian A. Waitz,et al.  Adoption of active learning in a lecture-based engineering class , 2002, 32nd Annual Frontiers in Education.

[13]  Sarmad Mohammad,et al.  Confidence -Motivation -Satisfaction- Performance (CMSP) Analysis of Blended Learning System in the Arab Open University Bahrain , 2012 .

[14]  Anthony F. Grasha,et al.  A Matter of Style: The Teacher as Expert, Formal Authority, Personal Model, Facilitator, and Delegator , 1994 .

[15]  Greg Kearsley,et al.  Online Education: Learning and Teaching in Cyberspace , 1999 .

[16]  M. Dembo,et al.  The Relationship Between Self-Regulation and Online Learning in a Blended Learning Context , 2004 .

[17]  Susan A. Santo.  Relationships between Learning Styles and Online Learning , 2008 .

[18]  Edward L. Deci,et al.  Beyond the intrinsic-extrinsic dichotomy: Self-determination in motivation and learning , 1992 .

[19]  Kay Berkling,et al.  Redesign of a Gamified Software Engineering Course Step 2 Scaffolding: Bridging the Motivation Gap , 2013 .