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Use of Data Mining Tools in Examining and Developing the Quality of E-
Learning
Imre BaloghHungary
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Introduction
Research byBudapest University of Technology and EconomicsandUniversity of West Hungary
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Content
• Interpretation of web mining• Quality Management of e-learning• Tools of research• Summary• Linking MOODLE and Web Mining
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Data Mining
“Data Mining is also called Knowledge discovery in databases (KDD). It is commonly defined as the process of discovering useful patterns or knowledge from data sources, e.g. databases, texts, the Web, etc. The patterns must be valid, potentially useful and understandable. Data mining is a multi-disciplinary field involving machine learning, statistics, databases, artificial intelligence, information retrieval, and visualization.“ (Bing Liu, 2007, p 6)
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Web Mining
• „Web mining aims to discover useful information or knowledge from Web hyperlink structure, page content, and usage data“ (Bing Liu, 2007, p 6)
• Types of web mining:– Web structure mining– Web content mining– Web usage mining
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Quality Management
Satisfying the demands of consumersValue based approach
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Questions: Answers:Who is the customer? The learning userProduct or service? The service itself
is the product
EQO metadata model (2004)
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EQO
Close correlation - quality of the e-learning- ”digital footprint”
Data gained from documentation Data generated by actual users
Our primary focus
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Examination tools
• Data only available in digital form on computers
• Tool: SPSS CLEMENTINE• Information transfer happens via web• Streams developed for e-business:
SPSS CLEMENTINE Web Mining CAT
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Two examples
• User Activity Focus
• Propensity Analysis
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User Activity Focus
• Users’ attention on topics while online
• Users’ interest in certain areas of the site that are most often visited
• Focus analysis ≠ hit analysis.
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User Activity Focus
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Filter
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Most popular activities
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Web of events and focus associations
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Propensity Analysis
Predictive analysis methods have been applied to the problems of• detecting fraud• arresting churn• targeting marketing campaigns
(Only the first and second are interesting for us)
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Advanced visit segmentation
It can be used for the following purposes:
• To identify a set of visit categories representing the different stages of a user’s visit
• To help to understand the reasons why users visit a site
• To track changes in the visit segments over time, in order to identify the weaker or stronger elements of the Web site
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Advanced User Segmentation
It may be used for the following purposes:
• To identify a set of user categories• To understand the reasons why users visit• To track changes in user behaviour over the
history of a user• To provide a high-level business description of the
user population
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Propensity Analysis (preparation)
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Propensity Analysis (model)
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Summary
1. e-learning analysis → added value
2. Aim: increase the efficiency of learning along with the increased user activity
3. Feedback: SMDE, organizer
4. Result: effectively improved quality
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Next Research Project(s)
• Linking MOODLE & SPSS Web Mining
• Analyzing MOODLE site online activity by SPSS CLEMENTINE
• Two possibilities• To develop a “MOODLE” node in CLEMENTINE• To build a SQL code for MOODLE
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