Towards Networked Learning Analytics – A concept and a tool(5)

Networked learning is much more ambitious than previous approaches of ICT-support in education. It is therefore more difficult to evaluate the effectiveness and efficiency of the networked learning activities. Evaluation of learners ’ interactions in netw

The structure of the paper will be the following: Section 1 has already stated the motivation of our work. Section 2 contains state-of-the-art information about web analytics tools. It will be shown that these tools cannot adequately support the task of analysis of learners’ behaviour in networked learning environments. This is why the need of developing new tools, called networked learning analytics tools, has arisen. Section 3 gives an overview of the networked learning analytics approach. The CoSyLMSAnalytics tool, will be used as a show case of the application of the networked learning analytics approach into real practice. Finally, the paper will contain concluding remarks and topics for future work in section 4.

FROM WEB ANALYTICS TO NETWORKED LEARNING ANALYTICS

Web Analytics is a more general term that mainly seeks to identify visitors’ navigational behavior and track their access patterns in the site’s Web pages. To do this, several tools have been developed in order to define users’ ‘click streams’ and period sessions depending on the time a visitor spend on each Web page. There are other related terms, such as Website Traffic Analysis, Web Log Analysis, Log File Analysis, and Web Mining.

A complete Website traffic analysis includes the categorization and pre-processing of Web data and the extraction of correlations between and across different such kind of data (Mobasher et al., 1996).

Website traffic analysis takes as input raw Web data in the form of a log file from the web server, and processes them in order to extract statistical information. A simple analysis in the server’s log files gives little or trivial information regarding unknown patterns of the user’s navigational behavior or even the usability of the Web server.

This information concerns usage statistics, such as average time spent on page, count of visits, most downloaded files etc. and it is used basically by Web administrators for improving the system performance, facilitating the site modification task and providing support for marketing decisions (Srivastava et al., 2000).

Web Usage Mining in more detail is the application of Data Mining techniques to large Web data repositories in order to discover hidden knowledge concerning users’ navigational behavior and extract meaningful patterns that go beyond simple queries and usage statistics.

Several algorithms have been developed seeking to identify visitor’s similar browsing behavior. Most of these algorithms are implementations of a number of Data Mining Methods like the following ones:

1.Clustering is used to group together items that have similar characteristics. In Web Usage Mining,

clusters are created so as to categorize number of users with common browsing patterns. The most

common clustering algorithms are K-Means, BIRCH, ROCK, Hierarchical Clustering Algorithm,

DBSCAN and TURN.

2.Classification is a process of mapping items into pre-defined classes. In the Web domain classes

usually represent different user profiles and classification is performed using selected features that

describe each user’s category. The most common classification algorithms are Decision Trees,

Naïve Bayesian Classifier and Neural Networks.

3.Association Rule Mining is a technique for finding frequent patterns, associations and correlations

among sets of items. Such rules indicate the possible relationship with a specified support that is

the number of data sequences that contain the pattern between Web pages that are often viewed

together even if they are not directly connected and can reveal associations among groups of users

with specific interests.

(Koutri et al., 2004) provide an overview of the state of the art in research of web usage mining, while discusses the most relevant criteria for deciding on the suitability of these techniques for building an adaptive web site. Among the techniques discusses are: Clusters of document references reflect pattern of common usage, Clusters of user visits, association rule and sequential patterns (see Table 1).

Currently there is a variety of Web log analysis tools available both commercial such as Net Genesis’ E-Metrics Solutions Suite, the Funnel Web by Quest, the NetTracker provided by Sane Solutions, WebTrends Log Analyzer software by WebTrends, as well as open source such as WUM and WEKA (Pierrakos et al., 2003). Web usage mining technique Data Mining Metrics Interpretation

Clustering web document references Groups of web document

references

Patterns of common usage

reflecting mentally related web

documents

Networked Learning 2006 3

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