Investigation of user’s preferences in interactive multimedia learning systems: a data mining approach<br />By K. Chrysoso...
Thesis<br />Human factors vary across users and greatly influence learning patterns, therefore computer users may prefer t...
Previous Studies<br />Previous studies have looked at what role pre-determined human factors have in preferences for inter...
Previous Studies<br />Problem with previous studies<br />Assumption driven statistical techniques are used to analyze the ...
What is data mining?<br />Analysis on data you already have, to extract patterns.<br />      (statistical, machine learnin...
How does data mining work?<br />Data is extracted, transformed, and loaded into storage (warehoused). Data comes first. <b...
Relationships sought in data mining<br />Classes - data arranged into predefined classes<br />Clusters - an algorithm grou...
Why use data mining?<br />Relies on information technology, statistical analyses, and mathematical science<br />Data drive...
How does data mining work in our paper?<br />In the field  of data mining, the knowledge discovery techniques are classifi...
Continued...<br />Paper Investigates Problem with Classification <br />Analysis of user preferences is based classified on...
Methodology Design - Participants<br />All students from a UK university were emailed an invitation to participate in the ...
Methodology Design – Research Apparatus<br />Questionnaire to identify users’ preferences <br />Two Interactive multimedia...
Methodology Design – System A<br />WYSIWYG (What You See Is What You Get) interaction style <br />Interface layout - Singl...
Methodology Design – System A<br />
Methodology Design – System B<br />WIMP (Windows Icons Menus Pointers) interaction style<br />Interface layout - Multiple ...
Methodology Design – System B<br />
Methodology Design -  Procedure<br />Group  1, one half of the participants completed the quiz in System A, then completed...
Methodology Design - Data Analyses<br />Pre-processing of data<br />Data that did not relate to user preference were exclu...
Methodology Design - Data Analyses<br />K-Modes Algorithm<br />(This paper assumes the reader already knows how K-means wo...
Results and Discussion - Interactive multimedia features<br />Clustering of users shows a definite division between their ...
Results and Discussion - The Effects of Human Factors<br />What is the role of human factors in determining the clusters?<...
Results and Discussion - Window Layouts<br />Computer experience significantly affects the users’ preference for interface...
Concluding Remarks:<br />Con =  Small scaled study. <br />Con =  Determination of users to be experts or novice technology...
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Paper Presentation: Data Mining User Preference in Interactive Multimedia

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Paper Presentation: Data Mining User Preference in Interactive Multimedia

  1. 1. Investigation of user’s preferences in interactive multimedia learning systems: a data mining approach<br />By K. Chrysosotomous, S. Chen and X. Liu<br />Presented by<br />Terry De Hoyos, Lauren Steele and Jeanette Howe<br />
  2. 2. Thesis<br />Human factors vary across users and greatly influence learning patterns, therefore computer users may prefer the design of interactive media learning systems differently.<br />Theoretical Background:<br />Proliferation of rich instructional multimedia learning systems<br />Rich environments that incorporate: text, images, audio, animation, and video<br />Provide advanced interface features such as: dynamic buttons, multiple windows, drop–down menus<br />
  3. 3. Previous Studies<br />Previous studies have looked at what role pre-determined human factors have in preferences for interactive multimedia technologies. Factors such as: age, gender, computer experience<br />Ex. Passig and Levin (1999) Tested specifically for gender differences in multimedia interface design preferences.<br />Results from 90 kindergarten students:<br />Boys like whole screens that change all at once, girls dislike this approach .<br />Boys prefer green and blue colors, girls prefer red and yellow.<br />
  4. 4. Previous Studies<br />Problem with previous studies<br />Assumption driven statistical techniques are used to analyze the empirical data in which the hypotheses is formulated and then tested against the data.<br />The scope of the results is restricted by the hypothesis. <br />Findings from data themselves may be ignored.<br />
  5. 5. What is data mining?<br />Analysis on data you already have, to extract patterns.<br />      (statistical, machine learning, or neural networks)<br /> Data mining = knowledge discovery (patterns, associations, relationships among data provide information)<br />Centuries old technique - new approaches due to technology innovation and improvement advances in data capture, processing, transmission & storage allow centralization of data - "warehousing“ advances in software analysis allow better access to data.<br />
  6. 6. How does data mining work?<br />Data is extracted, transformed, and loaded into storage (warehoused). Data comes first. <br />Data is stored and managed in an accessible fashion. Data is made usable.<br />A user makes an "open-ended" query (not a hypothesis). Data is accessed.<br />Analysis is applied to available data. Data is analyzed.<br />Relationships between data are sought. Data presented in useful format relative to query.<br />
  7. 7. Relationships sought in data mining<br />Classes - data arranged into predefined classes<br />Clusters - an algorithm groups data into classes (not predefined)<br />Associations - looks for associations between variables.<br />Sequential patterns - looks for sequential patterns between variables<br />
  8. 8. Why use data mining?<br />Relies on information technology, statistical analyses, and mathematical science<br />Data driven <br />Do not need an initial formulation of hypothesis <br />Data discovery leads to patterns and relationships <br />Data mining = knowledge discovery<br />
  9. 9. How does data mining work in our paper?<br />In the field  of data mining, the knowledge discovery techniques are classified by the terms unsupervised learning and supervised learning. <br />These terms come from machine learning, in which an algorithm (the "machine") is trained. The "teacher" in supervised learning is the algorithmic structure which compares what the "student" (the algorithm/machine) is predicting to what it should predict (the predefined class) and thereafter corrects the student to better predict in the future. <br />Supervised learning (classification)<br /> - objects are assigned to predefined categories or classes.<br />Unsupervised learning (clustering)  <br /> - data is divided and grouped into similar objects called clusters. Similar between themselves and dissimilar to clusters of other groups.<br />
  10. 10. Continued...<br />Paper Investigates Problem with Classification <br />Analysis of user preferences is based classified on a particular human factor (age, gender, computer experience) instead of the users' preferences.<br />Solution <br />Use clustering because it shows how human factors are linked with users’ preferences in interactive multimedia learning systems. <br />
  11. 11. Methodology Design - Participants<br />All students from a UK university were emailed an invitation to participate in the study, 80 volunteered <br />Prerequisite - basic computing skills <br />Human factors: age, gender, level of expertise, study level <br />Participant ages:<br /> 17% (16-20) 33% (21-25) 24% (26-30) 8% (31-35) 6% (36-40) 12%  (40+)<br />Gender = 50% male, 50% female <br />Level of expertise = 55% novice, 45% experts <br />Study level = 38% undergraduate, 23% postgraduate, 18% doctorate, <br /> 21% other qualifications.<br />
  12. 12. Methodology Design – Research Apparatus<br />Questionnaire to identify users’ preferences <br />Two Interactive multimedia learning systems, System A and System B <br />Same content , same quiz-like format, different interaction styles<br />The main differences between System A and System B lie within the interface layout, button types, color scheme, multimedia elements, and menu formats. <br />
  13. 13. Methodology Design – System A<br />WYSIWYG (What You See Is What You Get) interaction style <br />Interface layout - Single window<br /> Button types = Static, no color change when clicked, no embedded icons<br /> Color scheme = Multiple colors, effect of blending one color into another<br /> Multimedia elements = Images, graphics, audio and video<br /> Menu format = Without drop-down menus<br />
  14. 14. Methodology Design – System A<br />
  15. 15. Methodology Design – System B<br />WIMP (Windows Icons Menus Pointers) interaction style<br />Interface layout - Multiple windows <br />Button types = Dynamic, changes color or form when clicked, has embedded icons <br />Color scheme = Few standard colors <br />Multimedia  elements = Images, graphics, audio <br />Menu format = Drop-down menus to access help, images and audio.<br />
  16. 16. Methodology Design – System B<br />
  17. 17. Methodology Design - Procedure<br />Group  1, one half of the participants completed the quiz in System A, then completed the quiz in System B. <br />Group 2, other half of the participants completed the quiz in System B, then completed the quiz in System A. <br />After the quizzes, participants answered the questionnaire.<br />
  18. 18. Methodology Design - Data Analyses<br />Pre-processing of data<br />Data that did not relate to user preference were excluded <br />Final set of features:<br /> 1. Layout of the interface<br /> 2. Button type preferred by users<br /> 3. Use of icons embedded within buttons<br /> 4. The use of menus<br /> 5. User’s preferred color scheme.<br />
  19. 19. Methodology Design - Data Analyses<br />K-Modes Algorithm<br />(This paper assumes the reader already knows how K-means works & relies on reader knowledge about K-means to intuit K-modes analysis. Therefore, we will try to simplify.)<br />K-means algorithm - widely known and used technique for grouping objects with similar characteristics.<br /> K-modes algorithm - extension of K-means, used to cluster data containing mixed numeric and categorical values<br /> Uses a simple matching dissimilarity measure to deal with categorical objects by replacing the means of clusters with modes…<br /> - then, uses a frequency-based method to update the modes in the clustering process<br /> - which minimizes the clustering cost function.<br /> - it is useful for analyzing data because the data from the questionnaire<br /> is categorical.<br />
  20. 20. Results and Discussion - Interactive multimedia features<br />Clustering of users shows a definite division between their preferences of interactive multimedia features. <br />Because cluster 2 is the largest, single color scheme is most popular with users <br />In cluster 4, all are females, and prefer color scheme w/effects<br />
  21. 21. Results and Discussion - The Effects of Human Factors<br />What is the role of human factors in determining the clusters?<br />Used ANOVA to obtain statistical significance of age, studying level, computer expertise, and gender differences. <br />Results indicate that computer experience was a significant factor in determining the clusters representing users’ preferences <br />Majority of experts appeared in Cluster 2 and 4<br />
  22. 22. Results and Discussion - Window Layouts<br />Computer experience significantly affects the users’ preference for interface layout <br />Novices prefer a single window layout <br />Experts prefer a multiple window layout<br />Results and Discussion - Navigation Tools<br />Computer experience has significant effects on users’ preferences of dynamic/static buttons & drop-down menus. <br />Majority of experts favor using dynamic buttons and drop-down menus <br />Novices like static buttons & dislike drop-down menus.<br />
  23. 23. Concluding Remarks:<br />Con =  Small scaled study. <br />Con =  Determination of users to be experts or novice technology users (perhaps too vague?)<br />Pro = Data mining approach is a discovery of knowledge method with no predetermined categories to correspond with a fixed hypothesis to prove. <br />Pro = Findings about user preferences may be useful in designing future multimedia learning systems. <br />Pro = Findings may be useful in designing future studies.<br />

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