The Future of Education

The aim of this study is to examine the learning management system (LMS) usage behaviors of the learners in Anadolu University Open Education System. Today, learning management systems allow to record big volumes of data regarding learners’ learning behaviors and digital footprints. Analyzing these data using data mining and machine learning methods and techniques can provide valuable information with regard to the learning processes undertaken by learners. Anadolum eCampus, which is the learning management system used to deliver courses in Anadolu University Open Education System, is utilized by an average of 600,000 open and distance learners each semester. The courses at Anadolum eCampus are unit based, approximately 15 different learning resources are offered in each unit of a course. In the context of this study, the LMS usage behaviors of approximately 300,000 learners enrolled in ‘The Introduction to Information Technologies’ course throughout the 2018-2019 academic year will be examined. The study will examine the learners' frequency and duration of access to the system in addition to their access to the learning resources and the paths they use while accessing the resources. Additionally, this study aims to reveal the learning behaviors of these learners. In this process, association rules, sequence patterns and cluster analysis will be used. Association rules are a descriptive analysis used to discover patterns that show strong associations in data [1]. In sequence patterns, the relationship between data is defined depending on time [2]. In cluster analysis, the data are grouped according to their similar features. Cluster analysis is based on the principle of identifying the vectors that will represent the data in the best way and encoding all data with these new vectors [3]. Cluster analysis, in which naturally existing groups are revealed in the data set, is a type of unsupervised learning and the data used are unlabelled [4]. Within this regard, the LMS usage behaviors of the learners using Anadolum eCampus will be revealed by making relevant analysis, and suggestions will be offered.

Keywords: Open and distance learners, learning management systems, learning paths, learning behaviors, educational data mining, learning analytics.

References:
[1] Aydın, S. (2007). Veri madenciliği ve Anadolu Üniversitesi uzaktan eğitim sisteminde bir uygulama. Yayınlanmamış Doktora Tezi. Eskişehir: Anadolu Üniversitesi.
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ftp://ftp.software.ibm.com/software/analytics/spss/documentation/modeler/14.2/en/ModelingNodes.pdf.
[3] Han, J., Kamber, M. (2006). Data mining: concepts and techniques (2nd ed.). San Francisco, CA: Morgan Kaufmann.
[4] Dunham, M. H. (2003). Data mining: introductory and advanced topics. New Jersey: Pearson Education.