Use of Data Mining Tools in Examining and Developing the Quality of E-Learning Imre Balogh Hungary 10/19-20/09 LOGOS OPEN ...
Introduction <ul><li>R esearch by </li></ul><ul><li>Budapest University of Technology and Economics </li></ul><ul><li>and ...
Content <ul><li>Interpretation of web mining </li></ul><ul><li>Quality Management of e-learning </li></ul><ul><li>Tools  o...
Data Mining <ul><li>“ Data Mining is also called  Knowledge discovery  in databases (KDD). It is commonly defined as the  ...
Web Mining <ul><li>„ Web mining aims to discover useful information or knowledge from Web hyperlink structure, page conten...
Quality Management <ul><li>Satisfying the demands of consumers </li></ul><ul><li>Value based approach </li></ul>10/19-20/0...
EQO metadata model (2004) 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
EQO <ul><li>C lose correlation  </li></ul><ul><li>-  quality of the  e-learning </li></ul><ul><li>-  ”digital footprint”  ...
Examination tools <ul><li>D ata only available in digital form on computers </li></ul><ul><li>Tool :  SPSS CLEMENTINE </li...
Two examples <ul><li>User Activity Focus </li></ul><ul><li>Propensity Analysis </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE...
User Activity Focus <ul><li>Users ’  attention on  topics while  online </li></ul><ul><li>Users’ i nterest  in certain  ar...
User Activity Focus 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
Filter 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
Most popular activities 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
Web of events and focus associations 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
Propensity Analysis <ul><li>Predictive analysis methods have been applied to the problems of </li></ul><ul><ul><li>detecti...
Advanced visit segmentation <ul><li>It can be used for the following purposes:  </li></ul><ul><ul><li>To identify a set of...
Advanced User Segmentation <ul><li>It may be used for the following purposes:  </li></ul><ul><ul><li>To identify a set of ...
Propensity Analysis (preparation) 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
Propensity Analysis (model) 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
Summary <ul><li>1. e-learning analysis  -> added value  </li></ul><ul><li>2.   A im :  increase the efficiency of learning...
Next  Research  Project(s) <ul><li>L inking MOODLE  &  SPSS Web Mining </li></ul><ul><li>Analyz ing  MOODLE site online ac...
<ul><li>[email_address] </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
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  • Current study reports on the research by the Ergonomics and Psychology Department of Budapest University of Technology and Economics and supported by the Technical and Information Management Department of the University of West Hungary aiming at the examination of quality management of Virtual Learning Environment with the use of web mining tools.
  • Current study reports on the research by the Ergonomics and Psychology Department of Budapest University of Technology and Economics and supported by the Technical and Information Management Department of the University of West Hungary aiming at the examination of quality management of Virtual Learning Environment with the use of web mining tools.
  • Introduction of the applied interpretation of web mining Quality Management of Virtual Learning Environment D efinition of the aim of the research Presentation of a stream suitable to illustrate possibilities of linking MOODLE and Web Mining.
  • The most widespread version of the various possible definitions of quality is currently the following satisfying the demands of consumers. Mention also must be made of another, so-called value based approach. Set attributes of products or services for set price. F or correct
  • Considering the fact, that we do not strive for analyzing the EQO model, we will not examine certain parts of the model, but rather aim to draw attention to the obvious idea suggested by the model that there is a close correlation between the quality of the Virtual Learning Environment and certain data (”digital footprint”) evolving during the use of this environment. The data in question can be classified into two groups according to the above: data gained from documentation and open publications and data generated by actual users. Our examinations primarily have focused on data belonging to the second group, though we are currently working out the frames of a project aiming at the examination of data belonging to the first above mentioned group.
  • Considering that a significant part of the data evolving during the use of the Virtual Learning Environment is only available in digital form on computers, their examination needs tools that are applicable and suitable for such activity. One such possible tool is SPSS CLEMENTINE used in our research. The relevant part of the Virtual Learning Environment – yet today – is represented by the computer screen; information transfer happens via web that is why the research group uses SPSS CLEMENTINE Web Mining in examining the digitalized data generated by the person sitting in front of the computer. As regards the part of the Virtual Learning Environment examined by our group is exclusively ensured by the web, we can use those implementations of streams developed for e-business in our examinations, which are abundantly included in SPSS CLEMENTINE Web Mining CAT.
  • This analysis looks at what and where users focus their attention during their time online. Focus analysis can give us clues as to the interests of the user and it can also tell us those areas of the site that are attracting and retaining users. Focus analysis is particularly useful for content sites and content-heavy events because it quickly highlights those content areas that are valuable in the eyes of each user. Note that Focus analysis is not the same as hit analysis. With Focus analysis, we are not simply reporting the areas of the site that are getting the most traffic; we are reporting the areas of the site that the user is using in preference to other areas. This is an important distinction because areas of the site that get a lot of traffic are popular, but areas of the site with a high focus are successful. By combining Focus analysis with user visit frequency it will be possible to determine those areas of the site that regular users focus heavily on. These are the functions of the site that attract the users because they are successful. These are the marketable features of the site.
  • It is obvious without express emphasis how serious added value this examination means in case of analyzing the Virtual Learning Environment, thus the specific aim here is to also increase the efficiency of learning along with the increased user activity. The results received can be used by both the study material development expert and the organizer of the education training to improve the quality effectively.
  • To finish with, it is important to mention one of our next research projects aiming to examine the possibility of linking MOODLE and SPSS Web Mining and its efficiency. If there is a need to analyze the MOODLE site online activity of a MOODLE system user by the tool of SPSS CLEMENTINE Web Mining, the only available source of data to rely on is the web server data content used by MOODLE. The process may have two directions from MOODLE to CLEMENTINE or the other way around.
  • Use of Data Mining Tools in Examining and Developing the ...

    1. 1. Use of Data Mining Tools in Examining and Developing the Quality of E-Learning Imre Balogh Hungary 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    2. 2. Introduction <ul><li>R esearch by </li></ul><ul><li>Budapest University of Technology and Economics </li></ul><ul><li>and </li></ul><ul><li>University of West Hungary </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    3. 3. Content <ul><li>Interpretation of web mining </li></ul><ul><li>Quality Management of e-learning </li></ul><ul><li>Tools of research </li></ul><ul><li>Summary </li></ul><ul><li>Linking MOODLE and Web Mining </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    4. 4. Data Mining <ul><li>“ 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) </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    5. 5. Web Mining <ul><li>„ Web mining aims to discover useful information or knowledge from Web hyperlink structure, page content, and usage data“ (Bing Liu, 2007, p 6) </li></ul><ul><li>Types of web mining: </li></ul><ul><ul><li>Web structure mining </li></ul></ul><ul><ul><li>Web content mining </li></ul></ul><ul><ul><li>Web usage mining </li></ul></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    6. 6. Quality Management <ul><li>Satisfying the demands of consumers </li></ul><ul><li>Value based approach </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST Questions: Answers: Who is the customer? The learning user Product or service? The service itself is the product
    7. 7. EQO metadata model (2004) 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    8. 8. EQO <ul><li>C lose correlation </li></ul><ul><li>- quality of the e-learning </li></ul><ul><li>- ”digital footprint” </li></ul><ul><li>Data gained from documentation </li></ul><ul><li>Data generated by actual users </li></ul><ul><li>O ur primary focus </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    9. 9. Examination tools <ul><li>D ata only available in digital form on computers </li></ul><ul><li>Tool : SPSS CLEMENTINE </li></ul><ul><li>Information transfer happens via web </li></ul><ul><li>Streams developed for e-business : </li></ul><ul><li>SPSS CLEMENTINE Web Mining CAT </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    10. 10. Two examples <ul><li>User Activity Focus </li></ul><ul><li>Propensity Analysis </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    11. 11. User Activity Focus <ul><li>Users ’ attention on topics while online </li></ul><ul><li>Users’ i nterest in certain areas of the site that are most often visited </li></ul><ul><li>Focus analysis ≠ hit analysis. </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    12. 12. User Activity Focus 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    13. 13. Filter 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    14. 14. Most popular activities 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    15. 15. Web of events and focus associations 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    16. 16. Propensity Analysis <ul><li>Predictive analysis methods have been applied to the problems of </li></ul><ul><ul><li>detecting fraud </li></ul></ul><ul><ul><li>arresting churn </li></ul></ul><ul><ul><li>targeting marketing campaigns </li></ul></ul><ul><ul><li>(Only the first and second are interesting for us) </li></ul></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    17. 17. Advanced visit segmentation <ul><li>It can be used for the following purposes: </li></ul><ul><ul><li>To identify a set of visit categories representing the different stages of a user’s visit </li></ul></ul><ul><ul><li>To help to understand the reasons why users visit a site </li></ul></ul><ul><ul><li>To track changes in the visit segments over time, in order to identify the weaker or stronger elements of the Web site </li></ul></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    18. 18. Advanced User Segmentation <ul><li>It may be used for the following purposes: </li></ul><ul><ul><li>To identify a set of user categories </li></ul></ul><ul><ul><li>To understand the reasons why users visit </li></ul></ul><ul><ul><li>To track changes in user behavio u r over the history of a user </li></ul></ul><ul><ul><li>To provide a high-level business description of the user population </li></ul></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    19. 19. Propensity Analysis (preparation) 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    20. 20. Propensity Analysis (model) 10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    21. 21. Summary <ul><li>1. e-learning analysis -> added value </li></ul><ul><li>2. A im : increase the efficiency of learning along with the increased user activity </li></ul><ul><li>3 . Feedback: SMDE, organizer </li></ul><ul><li>4. Result: effectively improve d quality </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    22. 22. Next Research Project(s) <ul><li>L inking MOODLE & SPSS Web Mining </li></ul><ul><li>Analyz ing MOODLE site online activity by SPSS CLEMENTINE </li></ul><ul><li>Two possibilities </li></ul><ul><ul><li>To develop a “MOODLE” node in CLEMENTINE </li></ul></ul><ul><ul><li>To build a SQL code for MOODLE </li></ul></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
    23. 23. <ul><li>[email_address] </li></ul>10/19-20/09 LOGOS OPEN CONFERENCE BUDAPEST
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