1. Adaptivity, Personalisation and Assistive Technologies Hugh Davis

2. @ 2 The Research Questions  Does adaptation/personalisation improve learning? (How could we tell?)  In what ways can we adapt/personalise information?  How can we develop user-models to represent the real goals and current state of knowledge of users?  How can we adapt to deal with accessibility issues?

3. @ 3 Personalisation  Personalisation is the technology of presenting information to the user that is selected and presented in a manner that is chosen based on some understanding of the user’s needs.  The technology derives historically from “Intelligent Tutoring Systems”  Adaptive Hypertext is the business of personalising hypertext (nodes and links) in order to improve the user’s access to appropriate information.  Recommender Systems are programs which attempt to predict items (media, news, shopping, web pages) that a user may be interested in, given some information about the user's profile.  Assistive Technologies are a special case of personalisation where the user’s needs are particularly concerned with the physical environment

4. @ 4 A Generalised Model of an Adaptive Hypertext System (adapted from Motta et al. 2003)Motta et al. 2003

5. @ 5 Taxonomy of Adaptive Hypermedia Techniques From Bailey at al. (2001) “Link Augmentation: A Context-Based Approach to Support Adaptive Hypermedia” updating Brusilovsky, P. (1996). “Methods and Techniques of Adaptive Hypermedia”.

6. @ 6 What can we adapt to?  User knowledge  Cognitive properties  (learning style, personality, etc.)  User goals and plans  User mood and emotions  User preferences

7. @ 7 User Modelling  Static vs Dynamic  Overlay Model  The User Model is assumed to be a subset of the domain model. Success is achieved when the user model is the same as the domain model ( or at least the subset required). Adaptation will be based on tracking the user’s learning and leading the user towards  Stereotypes  The user is characterised (by themselves or by some test) as one of a number of stereotype users (e.g. beginner, intermediate, expert), and adaptation is based on providing materials that will be suitable for that stereotype.

8. @ 8 Recommender Systems –Collaborative Filtering  A collaborative filtering system suggests “Users like you bought/searched for things like this…”  This suggests that the system has a model of you (and the other users)  The model may be as simple as what you have bought, in which case this becomes (item-based CF) “Users who bought these things also bought ……”  But there are many more complex models possible – particularly in eLearning where we may have much more information about our users than simply the documents they have searched for previously

9. @ 9 Recommender Systems – Content Filtering  Content based recommender systems use information about the item to find other items similar. “If you liked this book/film/music you may also like these…” (based on genre/subgenre classifications) “If you found this learning resource useful then you might also find these useful” (based on lexical similarity)  Content based methods have the advantage that the we don’t need to have a large case history before it can be applied  Hybrid methods are popular.

10. @ 10 Assistive Technology  Assistive technology devices are aids which substitute for or enhance the function of some physical or mental ability that is impaired.  *We* are concerned with hardware and software products that enable people with disabilities to access, interact with, and use computers.  Alternative Input Devices  alternative and adaptive keyboards, alternative and ergonomic mouse/pointing systems, head-operated pointing devices, Eyeglaze pointing devices, voice input systems, cursor enlargement software, etc.  Alternative Output Devices  Usually to enable Blind and Vision impaired persons to use or interact with a computer. Includes Braille display/output devices, Braille embosser/printers, screen reading software, screen magnification/enlargement software, large print monitor, etc.

11. @ 11 Accessible Software  A quick and straightforward treatement of accessibility issues can be found at http://www.mardiros.net/univ-accessible.htmlhttp://www.mardiros.net/univ-accessible.html  The IMS Guidelines for Developing Accessible Learning Applications are on notes. These provide guidelines…on notes  For People who are Blind  For People with Low-Vision  For People with Color Blindness  For People Who Are Hard-of-Hearing or Deaf  For People with Physical Disabilities  For People with Language or Cognitive Disabilities  General Accessibility Improvements

12. @ 12 Principles for Accessibility in Online Learning  Allow for customization based on user preference.  Provide equivalent access to auditory and visual content based on user preference.  Provide compatibility with assistive technologies and include complete keyboard access.  Provide context and orientation information.  Follow IMS specifications and other relevant specifications, standards, and/or guidelines.  Consider the use of XML.

13. @ 13 SENDA and Website Conformance  The Special Education Needs and Disabilities Act (SENDA) came into being in September 2002 and is an extension of the 1995 Disabilities Discrimination Act (DDA).  Companies and institutions are required by law to provide equal access to information and services for all.  It is against the law to treat a disabled person "less favourably" than an able bodied person.  In practical terms, this means an obligation to make "reasonable adjustments".  Institutions are required to anticipate the needs of their disabled students and not wait for the need for change to arise.  However, you are not required to lower educational standards to achieve equal access.  If information cannot be made accessible, the institution must provide information in an alternative format so as not to disadvantage any individual.

14. @ 14 Follow-up study  In what ways could Adaptive Hypertext techniques be used to provide an appropriate learning environment for a student with very poor sight?  A student has written a recommender system which produces recommendations for alternative learning resources to the ones being viewed based on a students preferred learning style. She claims this produces good recommendations and it improves learning. What sort of experiments could she conduct to prove her claim?  What is the difference between Simple Sequencing and Adaptive Hypertext Navigation?