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Computational Intelligence Methods for Data Analysis and Mining of eLearning Activities

Pavla Dráždilová, Gamila Obadi, Kateřina Slaninová, Shawki Al-Dubaee, Jan Martinovič and Václav Snášel

Abstract: Enhancing the the effectiveness of web-based eduction has become one of the most important concerns within both educational engineering and information system fields. The development of information technologies has contributed to the growth in elearning as an important education method. This learning environment enables learners to participate in ’any time, any place’ personalized training. It has been known that the application of data mining and computational intelligent approaches can provide better learning environments, and in their effort to participate in this field, the authors introduced this study which consists in its first part of a survey of the applications of data mining and computational intelligence in web based education during (2004-2009), and the second part is a case study that aims to analyze students’ activities performed in a Learning Management System.

Citration: Pavla Drázdilová, Gamila Obadi, Katerina Slaninová, Shawki A. Al-Dubaee, Jan Martinovic, Václav Snásel: Computational Intelligence Methods for Data Analysis and Mining of eLearning Activities. Computational Intelligence for Technology Enhanced Learning 2010: 195-224 [DOWNLOAD]
 

Using Spectral Clustering for Finding Students' Patterns of Behavior in Social Networks

Gamila Obadi, Pavla Drazdilova, Jan Martinovic,  Katerina Slaninova and Vaclav Snasel

Abstract: The high dimensionality of the data generated by social networks has been a big challenge for researchers. In order to solve the problems associated with this phenomenon, a number of methods and techniques were developed. Spectral clustering is a data mining method used in many applications; in this paper we used this method to find students' behavioral patterns performed in an elearning system. In addition, a software was introduced to allow the user (tutor or researcher) to define the data dimensions and input values to obtain appropriate graphs with behavioral pattens that meet his/her needs. Behavioral patterns were compared with students' study performance and evaluation with relation to their possible usage in collaborative learning.

Citation: Gamila Obadi, Pavla Drázdilová, Jan Martinovic, Katerina Slaninová, Václav Snásel: Using Spectral Clustering for Finding Students' Patterns of Behavior in Social Networks. DATESO 2010: 118-130  [DOWNLOAD]
 

Creation of Students' Activities from Learning Management System and their Analysis

Pavla Drazdilova, Katerina Slaninova, Jan Martinovic, Gamila Obadi, Vaclav Snasel

Abstract: The growth of eLearning systems popularity motivates researchers to study these systems intensively. Users of eLearning systems form social networks through the different activities performed by them (sending emails, reading study materials, chat, taking tests, etc.). This paper focuses on searching of latent social networks from eLearning systems data. This data consists of students activity records where latent ties among actors are embedded. The social network studied in this paper is represented by groups of students who have similar contacts, and interact in similar social circles, where the interest in performing similar tasks among users determines the groups with similar interactions. Different methods of data clustering analysis were applied to these groups and the findings show the existence of latent ties among the group members. The second part of this paper focuses on social network visualization. Graphical representation of social net- work can describe its structure very efficiently. It can enable social network analysts to determine the network degree of connectivity. Analysts can easily determine individuals with a small or large amount of relation- ships and determine the amount of independent groups in a given network.

Citation: Pavla Drázdilová, Katerina Slaninová, Jan Martinovic, Gamila Obadi, Václav Snásel: Creation of Students' Activities from Learning Management System and their Analysis. CASoN 2009: 155-160 [DOWNLOAD]

 

User Segmentation Based on Finding Communities with Similar Behavior on the Web Site

Katerina Slaninova, Radim Dolak, Martin Miskus, Jan Martinovic, Vaclav Snase

Abstract: Web log analysis can be helpful in gaining information about the usability of the web site, web performance, for marketing purposes, or for development of business intelligence tools in e-commerce systems. User segmentation is one of the problems solved in marketing and e-commerce sphere. Various software was developed to support web analysis. However, most of them provide only information through the tools based on statistics. User behavior and interaction with the web site is usually presented by measurement of click through rates, or by identification and sometimes visualization of popular paths only. User segmentation for further analysis (e.g. campaign analysis in marketing, web recommendation, web usage optimization) is usually allowed with the manual selection (often with variable setting). In this paper is presented the automatic user segmentation (clustering) based on the similar user's behavior on the web site. The user's behavior and behavioral patterns are extracted using process mining techniques; further user segmentation is provided by finding communities with similar behavior through two-step hierarchical clustering.

Citation: Katerina Slaninová, Radim Dolak, Martin Miskus, Jan Martinovic, Václav Snásel: User Segmentation Based on Finding Communities with Similar Behavior on the Web Site. Web Intelligence/IAT Workshops 2010: 75-78 [DOWNLOAD]

 

Web Site Community Analysis Based on Suffix Tree and Clustering Algorithm

Katerina Slaninova, Jan Martinovic, Tomas Novosad, Pavla Drazdilova, Lukas Vojacek and Vaclav Snasel

Abstract: Web site community analysis is one of the most valuable tools which can be used for user segmentation in webmarketing sphere. The user segmentation is successfully used in campaign analysis, for web/product/service recommendation, or for web usage optimization. This type of analysis can be helpful in web performance analysis, web usability or accessibility as well. Various software is available for user behavior analysis or for analysis of user interaction with the web site. However, most of them have the user segmentation based only on statistical measurement of such information like click-through rates, identification of popular paths and others. In this paper there is presented the web site community analysis oriented to the user segmentation. The analysis is based on the users' similar behavior on the website. For the identification of similar behavioral patterns was proposed the algorithm based on sequential pattern mining method combined with clustering using generalized suffix tree data structure.

Citation: Katerina Slaninova, Jan Martinovic, Tomas Novosad, Pavla Drazdilova, Lukas Vojacek and Vaclav Snasel, Web Site Community Analysis Based on Suffix Tree and Clustering Algorithm, WI-ITA 2011, Lyon, France.