Using Learning Styles and Neural Networks as na Approach to eLearning Content Layout Adaptation

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Using Learning Styles and Neural Networks as na Approach to eLearning Content Layout Adaptation - Presentation Transcript

  1. Using Learning Styles and g g y Neural Networks as na Approach to eLearning Content Layout Adaptation Keywords: Adaptative Hypermedia; eLearning; Adaptative Educational Systems; Navigation Support; user modelling Systems; Navigation Support; user DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 1
  2. Agenda g • Introduction • Problem Statement • Hipotesis • Learning Styles • Personalized and Dynamic Content Presentation and N i ti d Navigation • Prototype CeLIP – Cesae eLearning Intelligent Player • Conclusions and Future Work DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 2
  3. Introduction 1/2 L d h eLearning´s trend is changing: ´ Content has become the key issue. issue Learners don´t identify their g y own learning style with most of actual eLearning content. High impact use of technology in support of learning, like : fl lk Web 2.0 technologies and services and social networks. Microlearning become a Research Topic. DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 3
  4. Introduction 2/2 eLearning´s trend is changing: New oportunities with SCORM 2004 (LO, adaptative navigation paths) USA and south A d h America i elearning market is growing very quick 60% of American corporations and large companies have already applied these solutions [www.elearning- online.com, 5-2-2008] DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 4
  5. Problem Statement The eLearning in Portugal, as in many other countries, is not yet so widely used as an alternative i i id l d l to other forms of training: as is the case of traditional classroom. classroom DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 5
  6. Hipotesis This is because learners don’t identify their own learning style in the way the presentation of education content are done in the majority of eLearning material produced today, or not feel enough customization in the content to their own needs. From our point of view, p the appropriate modeling of the learner’s needs and preferences, representation of pedagogical strategies, learning designs and assets as well as the runtime reconciliation of these elements, are the key issues to solve some problems of this generation eLearning. This can be done with the help of some kind of learning styles classification and a mechanism to produce personalized content. DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 6
  7. Learning Styles g y We use a learner model based on Kolb learning  W l d lb d K lb l i style inventory classification[1] . Some other work in this field: ALE Fraunhofer Inst. for Applied Information Technology Felder ‐Silverman INSPIRE Honey & Munford TANGOW Felder & Soloman DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 7
  8. Kolb Learning Styles g y Kolb set out four preferences for learning: Feeling (“Concrete Experience” – CE) Watching (“Reflective Observation” – RO) Thinking (“Abstract Conceptualization” – AC) Doing (“Active Experimentation – AE) The Combination of this styles gives us DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 8
  9. Kolb Learning Styles g y Four Learning Styles or Types: Reflector (Watching and Doing, Concrete‐Reflective) Theorist (Watching and Thinking, Abstract Reflective) Theorist (Watching and Thinking, Abstract‐Reflective) Pragmatist(Thinking and Doing, Abstract‐Active) Activist (Doing and Feeling,  Concrete –Active) RO: 13 AC: 14 1º ACTIVIST 1º ACTIVIST AE: 17 Predominant area CE: 17 DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 9
  10. Kolb Learning Styles g y RO: 13 In his research Kolb AC: 14 concludes that no learner no learner AE: 17 1º ACTIVIST CE: 17 has one single style Our implementation of  Kolb’s self‐administered  K lb’ lf d i i t d questionnaire[9]. DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 10
  11. Personalized and Dynamic Content Presentation and Navigation In our design, we use two types of adaptability: Adaptive Presentation Adaptive Navigation p g For the first type we use three methods of adaptivity: Kolb yp p y Learning Styles, individual and global performance and user’s preferences. For the second type we use a subject matter pre-test mapped to each l d h learning object in the b h repository. DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 11
  12. Personalized and Dynamic Content Presentation and Navigation Learning element Sequencing Recomended topics Optional topics LS Kolb Adaptivity in learning experience is  actual User Preferences accomplished by choosing the  learning paths that suit the  values knowledge level and the acquired  competencies of the learner. DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 12
  13. Personalized and Dynamic Content Presentation and Navigation Depending on the subject,  their topic might have more  or less presentation layout  options. Other important  p sequencing strategy is  imposed by the kind of  hidden options dictated  by the initial diagnosis  and ontological maps  representing the  curriculum DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 13
  14. Personalized and Dynamic Content Presentation and Navigation Content/Presentation  Adaptation We use four type of pedagogical layout strategies mapped to the four basic  main styles defined by Kolb. We use sets of didactical elements composed in a  main styles defined by Kolb We use sets of didactical elements composed in a way that the learner “feels at home” in their learning style. AUDITIVE – Verbal instructions; Reading; Debate; Brainstorming; oral  presentations; Music, Video, Television; Repeat, audio recordings; audio books; This learners retain knowledge by using the language …  VISUAL – Watching, observing; remenber things using visual images and h b b h l d patterns, learn using color codes; understand better using shapes, drawings,  tables, oral instructions must be accompanied by video, graphics, images. “TACTIL‐CINESTÉTICA” – Learn with practical activities, and Ph i ll “TACTIL CINESTÉTICA” L ith ti l ti iti d Physically envolved in projects or activities. Better learning performance using movement. (example:  listening a mp3 Learning Object while walking). DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 14
  15. Prototype (ongoing work) CeLIP ‐Cesae eLearning Intelligent Player CeLIP architecture integrates new principles and  tools in the field of Learning Design and some  Artificial Intelligence. This player uses a MLP ‐ Artificial Intelligence. This player uses a MLP Multilayer Perceptron neural network to predict  the next presentation layout.  We use Three steps :p 1 – Train the network  (feed‐forward repeated until   MSE (mean‐squared error is suffficiently small) 2 – Use the network to predict the best  layout   presentation for learner. 3 – Retrain on‐the‐fly??? the network with a  backpropagation algorithm (backpropagate error by algorithm (backpropagate error by  adjusting weights through the hidden layer and back  to the input layer) DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 15
  16. Prototype (ongoing work) On‐the‐fly learning with portions of Backpropagation Algorithm. To include the ability to learn during the use of the neural  network, we use portions of Backpropagation Algoritm network, we use portions of Backpropagation and the delta (∆), value obtained from pre and post‐test  (performance) and the actual choice made by the learner.  DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 16
  17. Prototype (ongoing work) Player Prototype. Test it DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 17
  18. Prototype (ongoing work) Example Workflow Firstly, CeLIP determines and y employs a diagnosis in order to create a structure of LO’s (Learning Objects) to cover the unit of study study. For each step advance in the g navigation structure, CeLIP searches and finds learning objects that best suit learning style of the learner, their preferences and performance performance. DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 18
  19. Results and Future Work Results : We had implemented a first prototype of  CeLIP and we are now  producing a course for central region of Portugal local authorities that become the  first eLearning content using this technologies  in Portuguese Language. Future Work : Neuronal network learning parallelism;  Neuronal network learning parallelism; IMS LIP compatibility (by IMS Global Learning Consortium Inc.) ; Multi model approaches to model learner;  time based learning (historical); short and long time learning duality.  time based learning (historical); short and long time learning duality. Some authors expressed skepticism concerning the viability and validity of  g g y p p g using learning style of the learner to adapt or personalize a learning  environment to suit the needs of the learner [ Melis, E.M., Rachada, They  Call It Learning Style But It's So Much More. 2004] DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 19
  20. Conclusions In the current presentation  we have described the implementation of  possible  adaptive methods for content sequencing and adaptive presentation based on: learning styles preferences.  l l f adaptive hiding result of a diagnosis test. an AI engine using a neuronal network that process the predictions of the best  presentation layout for the next LO (learning Object) in the navigation sequence.  t ti l t f th t LO (l i Obj t) i th i ti This architecture is currently in the phase of implementation. In our first public presentation for  local authorities we received good feedback In our first public presentation for “local authorities”, we received good feedback  from them. DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 20
  21. Conclusions THANK YOU Questions? Q ti ? museu8bits@gmail.com jm@mail.cesae.pt l DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 21

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