January 2012 Volume 15 Number 1 - Educational Technology ...

Conclusion
Conducted research showed that adaptation of e-courses leads to better results. Proposed method for course
adaptation is based on adapting presentation of content, communication methods, and organization of online
activities to students’ learning styles and preferences. Analyses of e-learning system adaptivity have shown that
cognitive characteristics of students, such as learning style, are of the greatest importance for successful adaptation.
Described method for course adaptation is based on using data mining techniques to classify students into clusters
with regards to Felder-Silverman learning styles model. Type of presentation of teaching materials had the highest
impact on clustering and classification. E-learning courses were adapted using relations between Felder-Silverman’s
learning style model and activities in Moodle learning management system. Research results proved that teaching
resources and activities adapted to learning styles lead to significant improvement in learning results.
Analysis of results showed that students who attended adapted online courses achieved better results than students
who attended nonadapted online course. T-test showed that there was statistically significant difference in results
achieved by students in experimental group on post-test compared to results of students in control group. Higher
number of students passed the test, and they achieved higher grades.
Analyses of satisfaction of students in experimental group showed that they had positive attitude towards adapted
online course, and they thought that course materials and activities had been adapted with respect to their learning
styles.
One of the greatest advantages of described method for course adaptation is that it does not require programming
new software. Adaptation can be performed without any programming knowledge, and teachers can create adapted
courses by adjusting teaching materials and activities in Moodle LMS. The authors of this paper believe that this
approach can easily be modified for application in other LMSs as well.
The biggest disadvantage of the described approach is that it does not enable real time adaptation. Therefore, it is
necessary for teachers to monitor students’ progress and move them to another cluster, if necessary. This type of
adaptation requires more teachers’ effort in both creating adaptive course and exploitation.
In this research, course personalization has been realized with respect to learning styles as only criterion. Future
work will be directed toward providing further data about students with respect to different characteristics, such as
pre-knowledge, expectations, etc. Future work will also be directed towards qualitative analysis of described method,
as well as suitability for teaching other subjects and application in other learning management systems,
Acknowledgement
The authors of this paper are grateful to MNTRS for financial support grant no. 174031.
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