Observing Learning Strategies in Online Learning Environments with the Optimal Matching Algorithm
Author(s):
Christian Swertz (presenting / submitting)
Conference:
ECER 2013
Format:
Symposium Paper

Session Information

06 SES 13, New Challenges of Analysing Learning Paths as Processes

Symposium

Time:
2013-09-13
11:00-12:30
Room:
D-406
Chair:
Stefan Iske

Contribution

The assumption of the research reported in this talk is, that in an open ontology based learning environment learners do not show a chaotic learning behavior, but develop an individual learning strategy. A learning environment for an introduction to academic work has been developed. About 25 topics with 120 pages were made available. The material was produced according to the requirements of the web-didactic ontology, thus offering different learning pathways. The learning management system was reconfigured and reprogrammed to avoid any hidden suggestions for certain pathways. The material was applied in four seminars. 67 students seminars participated in the study. 1221 learning sequences have been recorded. These learning sequences were analysed with two methods: (1) An optimal matching analysis with a subsequent cluster analysis was performed for all learning sequences. (2) An individual interpretation of single learning sequences was performed for typical cases from the identified clusters. Results: Despite the fact that many learning pathways were possible and no suggestions were made, only three different learning pathways were found in the cluster analysis. The pathways were described as orientation only, example oriented and theory oriented.

Method

Austria

Author Information

Christian Swertz (presenting / submitting)
University of Vienna

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