Towards Networked Learning Analytics – A concept and a tool(7)

Clustering user visits Groups of user visits Patterns of common navigational

behaviour reflecting users actions

and motivations

Association rule mining Associations among web

document references Patterns of common usage reflecting related web documents, as well as the notion of antecedents and consequents

Sequential pattern mining Associations among sequences

of web document references

over time Patterns of typical browsing behaviours over time

Table 1. Interpretation of Web usage mining techniques [Source: Koutri et al., 2004]

Up to our knowledge none of these systems has been used for analysing learners’ interaction within a networked learning environment. This does not mean that there are not any attempts for qualitative evaluation measurements based on log files. On the contrary, interaction analysis using quantitative approaches is still an open R&D topic in the area of computer supported collaborative learning (CSCL), despite the various attempts already made. A lot of resources about Interaction analysis in CSCL can be found in the web site of the Kaleidoscope project ().

Moore (1989) suggested three kinds of interactions in a networked learning environment. These are: learner–content interactions, learner–instructor interactions, and learner–learner interactions. These three together have been used for trying to understand how learners construct knowledge in online networked learning environments. Several research groups developed coding schemes that categorize interactions according to models of knowledge construction in an effort to perform interaction analysis more efficiently. Gunawardena and colleagues (1997) developed a model and coding scheme for online interaction with five phases of knowledge construction:

i.sharing/comparing of information;

ii.discovery and exploration of dissonance or inconsistency among ideas, concepts, or statements;

iii.negotiation of meaning/co-construction of knowledge;

iv.testing and modification of proposed synthesis or co-construction; and

v.agreement statement(s)/applications of newly constructed meaning.

Shute and Glaser (1990) propose a technique that enables the evaluator to derive global learner differences on the basis of learner interaction measures. The approach by Shute and Glaser can be summarized as involving 1) counting frequencies of actions, 2) categorizing them into ‘meaningful’ units, and 3) making comparisons across groups.

Gaßner et al, (2003) illustrate how log files can be captured, codified and analysed for providing statistics of interaction as well as activity patterns. The MatchMaker TNG tool offers a framework for activity logging. As they claim “using the MatchMaker TNG log files we can access the complete structure of the activities that took place in a session”. An analysis method like the one that appears in Mühlenbrock (2004), can help evaluators in identifying collaborative activity patterns and reflecting them as feedback. Most of the times, interaction data concern the number of the messages read, the postings to a discussion board, the file up-loads, the annotations to the uploaded files, etc. Examination of the frequency of interactions across the individual learner or among members of groups can lead to identifying patterns of contributions.

Apart from quantitative measurements, analysis of participants' postings (content analysis) should be performed in sequel, so as to reveal many of the behaviors associated with collaborative learning situations (Curtis & Lawson, 2001).

Recently, CSCL research has focused on the use of Social Network Analysis (SNA) (Wortham, 1999), as an extension to the common technique of descriptive statistics of the messages or length of contributions (Benbunan-Fich & Hiltz, 1999).SNA creates graphical scheme of the contributions where factors such as “centrality” and “density” can be used for describing the learners’ groups cohesion. For example, “density” is a

Networked Learning 2006 4

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