Personalization of Web Based Interactive Systems using Computational Intelligence techniques
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This is my presentation given at the 1st DCIT Technical Symposium on Computer Science Research at the University of the West Indies, Trinidad.
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Personalization of Web Based Interactive Systems using Computational Intelligence techniques
- 1. The University of the West Indies Personalization of Web Based Interactive Systems using Computational Intelligence techniques Tricia Rambharose 18/ 01/ 2011 1st DCIT Technical Symposium on Computer Science Research
- 2. The University of the West Indies Ms. Tricia Rambharose 2 of 14 Scope of research Learning Styles Personalization eLearning Personalization of web based interactive systems by computational intelligence Particle Swarm Optimization Neural Networks Web Usability Computational Intelligence neuro-swarm
- 3. The University of the West Indies Ms. Tricia Rambharose 3 of 14 • 2000-2010 rate of Internet growth was 445% [Source: Miniwatts Marketing Group, World Internet Users and Population Statistics] • Size and complexity of websites and web based interfaces increase. • User frustration! – Information overload. – Users’ different characteristics are ignored. – Users’ different needs and preferences are ignored. Problems addressed
- 4. The University of the West Indies Ms. Tricia Rambharose 4 of 14 What is Web personalization? “any set of actions that can tailor the Web experience to a particular user or set of users.” Source: [Anand, and Mobasher, 2007]
- 5. The University of the West Indies Ms. Tricia Rambharose 5 of 14 Intelligent techniques for personalization Swarm Intelligence 6. Particle Swarm Optimization (PSO) Computational intelligence 2. Fuzzy Logic (FL) 4. Neural Networks (NN) 1. Artificial Immune Systems (AIS) 3. Genetic Algorithms (GA) 5. Ant Colony Optimization (ACO) 7. Wasp Colony Optimization (WCO) 8. Bee Colony Optimization (BCO)
- 6. The University of the West Indies Ms. Tricia Rambharose 6 of 14 Processing user profiles Taxonomy: Personalization of Web based systems using intelligent techniques Creation of user profiles Personalization Profile Generation Profile Exploitation Navigation Content AIS ACO FL PSO GA NN GA FL NN BCO FL PSO GA WCO NN Source: [T. Rambharose, A. Nikov, 2009]
- 7. The University of the West Indies Ms. Tricia Rambharose 7 of 14 Personalization procedure based on taxonomy Profile Generation Profile Exploitation Create or update user profile Explicit and implicit user data User profile data User profile data User preferences Web based systemUser Personalized content and navigation
- 8. The University of the West Indies Ms. Tricia Rambharose 8 of 14 Comparison of intelligent techniques FL GA NN PSO ACO BCO WCO AIS FL- PSO FL- NN FL- GA GA- NN Simplicity Speed Sound theory Learning ability Well tested Good Not as goodNeuro-Swarm
- 9. The University of the West Indies Ms. Tricia Rambharose 9 of 14 Validation of Neuro-Swarm model Traditional Neural Network Neural Network with PSO 2000 runs to minimize error to 0.023 350 runs to minimize error to 0.001 Error relates to accuracy
- 10. The University of the West Indies Ms. Tricia Rambharose 10 of 14 Neuro-Swarm model on Mathworks.com [T. Rambharose, http://www.mathworks.com/matlabcentral/fileexchange/29565-neural-network-add-in-for-psort]
- 11. The University of the West Indies Ms. Tricia Rambharose 11 of 14 Neuro-Swarm model for determining students’ online Learning Style … Sequential/GlobalVisual/VerbalSensing/IntuitiveActive/Reflective NsgNvvNsiNar21 …21 …21 …21 Based on four learning style dimensions in the Felder Silverman Learning Style Model Total active/reflective behaviours Hidden Layer Input Layer Output Layer Total Sensing/Intuitive behaviours Total Visual/Verbal behaviours Total Sequential/Global behaviours
- 12. The University of the West Indies Ms. Tricia Rambharose 12 of 14 Neuro-Swarm structure for a simulated example Time on examples Time on exercises Time on content No. example visits No. exercise visits No. forum visits Visual/Verbal Learning style value between 0-1 Hidden Layer Inputs Output
- 13. The University of the West Indies Ms. Tricia Rambharose 13 of 14 Personalization of a course on Moodle Area before content Area after content
- 14. The University of the West Indies Ms. Tricia Rambharose 14 of 14 Future work • Comparison of Neuro-Swarm model to Bayesian Networks and Rule Based approach, for learning style determination, using real data. • Modify Neuro-Swarm model to minimize error. • Project: Personalization of a health-oriented distance learning system using a Neuro-Swarm model.
- 15. The University of the West Indies Tricia Rambharose Dept. Computing and Information Technology The University of the West Indies (UWI) Trinidad, W.I. tricia.rambharose@sta.uwi.edu www.tricia-rambharose.com & SUGGESTIONS
- 16. The University of the West Indies Ms. Tricia Rambharose Questions Neuro-Swarm model settings Neural Network settings Particle Swarm Optimization settings
- 17. The University of the West Indies Ms. Tricia Rambharose Questions Main contributions • Comparison and assessment of intelligence techniques for personalization and recommendation of using a Neuro- Swarm technique • New model for determining learning style -> more accurate user modeling -> more accurate personalization. • MATLAB add-in available on Mathworks.com • Dynamic and automatic personalization for individual students in a health oriented eLearning system. • Part of a larger personalization project.
- 18. The University of the West Indies Ms. Tricia Rambharose Questions Larger personalization project Student Questionnaire Create student model Student Model Adapt course Student learning Update model? Online Course Yes Yes No No Update student model Fill out questionnaire ?
- 19. The University of the West Indies Ms. Tricia Rambharose Questions 2. Student model 1. Personalized eLearning system 3. Neural Network Contribution to larger personalization project Learning styles ∑ ƒ w b n a NN input: Behaviors NN output: Learning style
- 20. The University of the West Indies Ms. Tricia Rambharose Questions Inputs of Neuro-Swarm model for determining Learning Styles Active/Reflective Sensing/Intuitive Visual/Verbal Sequential/Global content_visit content_stay outline_stay example_stay selfass_visit selfass_stay selfass_twice_wrong exercise_visit exercise_stay quiz_stay_results forum_visit forum_post content_visit content_stay example_visit example_stay selfass_visit selfass_stay exercise_visit ques_detail ques_facts ques_concepts ques_develop quiz_revisions quiz_stay_results content_visit ques_graphics ques_text forum_visit forum_stay forum_post outline_visit outline_stay ques_detail ques_overview ques_interpret ques_develop navigation_skip navigation_overview_visit navigation_overview_stay Source: [S. Graf, 2007]
- 21. The University of the West Indies Ms. Tricia Rambharose Questions Output range of Neuro-Swarm model for determining Learning Styles ActiveReflective 1.00.90.80.70.60.50.40.30.20.10 Moderate active Moderate reflective Balanced Strong reflective Strong active