This study used a data mining approach to investigate user preferences in interactive multimedia learning systems without predetermined hypotheses. 80 participants used two systems that differed in interface design and were clustered based on their preferences. The largest cluster preferred a single color scheme. Computer experience significantly affected preferences - experts preferred multiple windows and dynamic buttons while novices preferred single windows and static buttons. The findings provide insights into user interface design without restricting results with predefined hypotheses.

1. Investigation of user’s preferences in interactive multimedia learning systems: a data mining approach By K. Chrysosotomous, S. Chen and X. Liu Presented by Terry De Hoyos, Lauren Steele and Jeanette Howe

2. Thesis Human factors vary across users and greatly influence learning patterns, therefore computer users may prefer the design of interactive media learning systems differently. Theoretical Background: Proliferation of rich instructional multimedia learning systems Rich environments that incorporate: text, images, audio, animation, and video Provide advanced interface features such as: dynamic buttons, multiple windows, drop–down menus

3. Previous Studies 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 Ex. Passig and Levin (1999) Tested specifically for gender differences in multimedia interface design preferences. Results from 90 kindergarten students: Boys like whole screens that change all at once, girls dislike this approach . Boys prefer green and blue colors, girls prefer red and yellow.

4. Previous Studies Problem with previous studies Assumption driven statistical techniques are used to analyze the empirical data in which the hypotheses is formulated and then tested against the data. The scope of the results is restricted by the hypothesis. Findings from data themselves may be ignored.

5. What is data mining? Analysis on data you already have, to extract patterns. (statistical, machine learning, or neural networks) Data mining = knowledge discovery (patterns, associations, relationships among data provide information) 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.

6. How does data mining work? Data is extracted, transformed, and loaded into storage (warehoused). Data comes first. Data is stored and managed in an accessible fashion. Data is made usable. A user makes an "open-ended" query (not a hypothesis). Data is accessed. Analysis is applied to available data. Data is analyzed. Relationships between data are sought. Data presented in useful format relative to query.

7. Relationships sought in data mining Classes - data arranged into predefined classes Clusters - an algorithm groups data into classes (not predefined) Associations - looks for associations between variables. Sequential patterns - looks for sequential patterns between variables

8. Why use data mining? Relies on information technology, statistical analyses, and mathematical science Data driven Do not need an initial formulation of hypothesis Data discovery leads to patterns and relationships Data mining = knowledge discovery

9. How does data mining work in our paper? In the field of data mining, the knowledge discovery techniques are classified by the terms unsupervised learning and supervised learning. 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. Supervised learning (classification) - objects are assigned to predefined categories or classes. Unsupervised learning (clustering) - data is divided and grouped into similar objects called clusters. Similar between themselves and dissimilar to clusters of other groups.

10. Continued... Paper Investigates Problem with Classification Analysis of user preferences is based classified on a particular human factor (age, gender, computer experience) instead of the users' preferences. Solution Use clustering because it shows how human factors are linked with users’ preferences in interactive multimedia learning systems.

11. Methodology Design - Participants All students from a UK university were emailed an invitation to participate in the study, 80 volunteered Prerequisite - basic computing skills Human factors: age, gender, level of expertise, study level Participant ages: 17% (16-20) 33% (21-25) 24% (26-30) 8% (31-35) 6% (36-40) 12% (40+) Gender = 50% male, 50% female Level of expertise = 55% novice, 45% experts Study level = 38% undergraduate, 23% postgraduate, 18% doctorate, 21% other qualifications.

12. Methodology Design – Research Apparatus Questionnaire to identify users’ preferences Two Interactive multimedia learning systems, System A and System B Same content , same quiz-like format, different interaction styles The main differences between System A and System B lie within the interface layout, button types, color scheme, multimedia elements, and menu formats.

13. Methodology Design – System A WYSIWYG (What You See Is What You Get) interaction style Interface layout - Single window Button types = Static, no color change when clicked, no embedded icons Color scheme = Multiple colors, effect of blending one color into another Multimedia elements = Images, graphics, audio and video Menu format = Without drop-down menus

14. Methodology Design – System A

15. Methodology Design – System B WIMP (Windows Icons Menus Pointers) interaction style Interface layout - Multiple windows Button types = Dynamic, changes color or form when clicked, has embedded icons Color scheme = Few standard colors Multimedia elements = Images, graphics, audio Menu format = Drop-down menus to access help, images and audio.

16. Methodology Design – System B

17. Methodology Design - Procedure Group 1, one half of the participants completed the quiz in System A, then completed the quiz in System B. Group 2, other half of the participants completed the quiz in System B, then completed the quiz in System A. After the quizzes, participants answered the questionnaire.

18. Methodology Design - Data Analyses Pre-processing of data Data that did not relate to user preference were excluded Final set of features: 1. Layout of the interface 2. Button type preferred by users 3. Use of icons embedded within buttons 4. The use of menus 5. User’s preferred color scheme.

19. Methodology Design - Data Analyses K-Modes Algorithm (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.) K-means algorithm - widely known and used technique for grouping objects with similar characteristics. K-modes algorithm - extension of K-means, used to cluster data containing mixed numeric and categorical values Uses a simple matching dissimilarity measure to deal with categorical objects by replacing the means of clusters with modes… - then, uses a frequency-based method to update the modes in the clustering process - which minimizes the clustering cost function. - it is useful for analyzing data because the data from the questionnaire is categorical.

20. Results and Discussion - Interactive multimedia features Clustering of users shows a definite division between their preferences of interactive multimedia features. Because cluster 2 is the largest, single color scheme is most popular with users In cluster 4, all are females, and prefer color scheme w/effects

21. Results and Discussion - The Effects of Human Factors What is the role of human factors in determining the clusters? Used ANOVA to obtain statistical significance of age, studying level, computer expertise, and gender differences. Results indicate that computer experience was a significant factor in determining the clusters representing users’ preferences Majority of experts appeared in Cluster 2 and 4

22. Results and Discussion - Window Layouts Computer experience significantly affects the users’ preference for interface layout Novices prefer a single window layout Experts prefer a multiple window layout Results and Discussion - Navigation Tools Computer experience has significant effects on users’ preferences of dynamic/static buttons & drop-down menus. Majority of experts favor using dynamic buttons and drop-down menus Novices like static buttons & dislike drop-down menus.

23. Concluding Remarks: Con = Small scaled study. Con = Determination of users to be experts or novice technology users (perhaps too vague?) Pro = Data mining approach is a discovery of knowledge method with no predetermined categories to correspond with a fixed hypothesis to prove. Pro = Findings about user preferences may be useful in designing future multimedia learning systems. Pro = Findings may be useful in designing future studies.