Towards Networked Learning Analytics – A concept and a tool(9)

Thus, we have proposed an approach, supported by a specialised tool, for the automatic classification of learners in a according to their access patterns and behaviour in a LMS. It would be beneficial to understand what learners and lecturers are doing alone and together and how learning goals learners are accomplishing.

A NEW APPROACH FOR ANALYSING LEARNERS’INTERACTION IN A NETWORKED LEARNING ENVIRONMENT

The main aim of our approach supported by the tool is to gather information concerning learners’ access patterns as well as to extract correlations among their learning paths. The first step is to get a general view of students’ log entries and then produce usage statistics such as count of visits, average time interval spent for performing an activity.

More importantly, this approach makes a Path Analysis. It gives us the ability to form all clustering groups of learners that perform an activity of a specific activity type (e.g. study an example, solve exercise, read a case study, post a message etc.) during one or more online sessions. Thus, we can “discover” groups of learners with similar browsing behaviour. This is a first indication of learners’ access patterns, before proceeding to deeper analysis by creating even more complex queries and extracting interesting association rules from the learning paths.

Our approach is supported by an analysis tool which is called CoSyLMSAnalytics (see Figure 1). This tool has been developed in Visual Basic and its current situation has been tested for analysing learners’ behavior in Moodle LMS [Spyros thesis 2005]. More specifically, it can:

?Produce usage statistics such as count of visits, average time interval spent on an activity, and offer then in various formats such as cross tabs and charts.

?Provide more detailed information regarding discussion forum statistics.

?Exploit learners’ sequential patterns by drawing the exact paths being followed by each learner individually or in groups

?Show deviations of individual learners from the typical series of activities performed by their group ?Perform Path Analysis with the creation of more complex queries that reveal interesting correlations and association rules among students’ learning paths.

The tool exports data in a specialized format for being used by the SPSS statistical package. Our approach suggests the utilization of the Two Step Clustering Algorithm in order to group students based on their overall behavior in networked learning environment. This algorithm is designed to reveal natural groupings or clusters within a data set that would otherwise not be apparent. It has several desirable features that differentiate it from traditional clustering techniques, such as:

?Handling of both categorical and continuous variables

?Automatic selection of number of clusters

?Scalability that deals efficiently with large volume of data

Networked Learning 2006 5

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