Foundations of Adaptive Hypermedia Heiko Spallek, DMD, PhD Center for Dental Informatics University of Pittsburgh, Schoo

1. Foundations of Adaptive Hypermedia Heiko Spallek, DMD, PhD Center for Dental Informatics University of Pittsburgh, School of Dental Medicine, Pittsburgh, PA March 30, 2005 Dental Informatics Seminar (2202)

3. Agenda Background e-learning hype reality check current status of dental online courses Adaptive Hypermedia definitions, concepts usage in education, information retrieval, kiosk & help systems Conceptual architecture Technical implementation In-class assignment Evaluation Conclusions

4. Hype About E-learning General number of online courses 50,000  100,000 between 1998 and 1999 * online learning market: $2.2 billion in 2001, estimated: $11.4 billion by 2003 * e-learning conference in Europe attracted 10,000 attendees last year Dentistry 1998: 158 online continuing dental education (CDE) courses from 32 providers 2001: just DentalxChange.com: 428 courses in 27 different categories * International Data Corp.

5. Reality check “E-learning is the revenge of the nerds.” evaluation of 158 online continuing dental education uncovered major deficiencies in existing Web-based CDE courses * no author indicated relation between course length and credit hours varies market for course shells immature no standards → risk of losing content no peer-review process for online learning resources → no academic credit usually no formative or summative evaluation high drop-out rate (based on meta analysis of multiple studies) in dentistry/medicine: no one has reported profits yet (break even for online course: 300-500 participants) * Schleyer T, Pham T. Online continuing dental education. Journal of the American Dental Association 1999;130(June):848-854,877-878

6. Current state-of-the-art dental online course collection of static Web pages translations of existing lecture notes existing course manuals existing papers =first generation online course model Course material is still implicitly oriented for a traditional on-campus audience!

7. Generations of online courses 1. Generation static pages 2. Generation adapted by the instructor to different audiences (static versions of the same course saved at different locations) 3. Generation adaptive to the individual learner

8. Adaptive Hypermedia (AH) new direction of research crossroads of hypermedia and user modeling model of the goals, preferences, knowledge of each individual user use of model throughout interaction with user adapt to the needs of user Brusilovsky 1996, Brusilovsky 2001

9. Definition User Model - “mental state” of the user * AH system keeps a model of the user to adapt teaching sequence, presentation for each Knowledge Item (KI) individual user model stores some value estimation of the user’s knowledge level of this concept ** * De Bra and Calvi 1998 ** Brusilovsky 1996

10. AH ― Areas of Application educational hypermedia online information systems online help systems information retrieval hypermedia institutional hypermedia systems for managing personalized views in information spaces Brusilovsky, P. (2001) Adaptive hypermedia. User Modeling and User Adapted Interaction, Ten Year Anniversary Issue (Alfred Kobsa, ed.) 11 (1/2), 87-110

11. Adaptive hypermedia technologies Adaptive Multimedia presentation Natural language adaptation Inserting/ Removing fragments Adaptive text presentation Canned text adaptation Altering fragments Adaptation of modality Strechtext Direct guidance Sorting fragments Adaptive link sorting hiding Dimming fragments Adaptive link hiding disabling Adaptive link generation removal Map annotation Taxonomy of AH Technologies Adaptive presentation Adaptive navigation support The updated taxonomy of adaptive hypermedia technologies; Brusilovsky, P. (2001) Adaptive hypermedia. User Modeling and User Adapted Interaction, Ten Year Anniversary Issue (Alfred Kobsa, ed.) 11 (1/2), 87-110

12. Curriculum Sequencing active implies a learning goal best individual path to achieve the goal passive reactive technology offers the user a subset of available learning material  fill the gap in student's knowledge

13. AH Technologies in Web-based Education adaptive navigation support technology - changing the appearance of visible links = generalization of curriculum sequencing technology in a hypermedia context - direct guidance, adaptive link annotation, adaptive link hiding adaptive presentation technology - adapt the content of a Web page to the user's goals, knowledge and other information stored in the user model - pages are adaptively generated or assembled from pieces for each user

14. Conceptual architecture What features of the learner can be used as a source of the adaptation? What can be adapted in a technical feasible manner?

15. 1. What features of the learner can be used as a source of the adaptation? user’s knowledge- pre-test results- completion of quizzes throughout the course user’s interests/goals- selection of a specific learning goal user’s traits- learning style

16. 2. What can be adapted in a technical feasible manner? user’s learning goal  compilation of KIs  individual curriculum (active sequencing) system’s estimation of the learner’s knowledge level of each KI stored modifies content (adaptive presentation)

17. Adaptiveness adaptive text presentation inserting/removing of fragments altering fragments stretch text adaptive navigation support adaptive links: hiding adaptive link annotation sitemap adaptation admission to final quiz only after mastering of all offered KIs forced repetition of KI example-based learning versus concept-based learning

18. Demographic Information LearningGoal PrivacyInformation AccessRights Knowledge Assessment Learning Style User AH system compiles pre-test AH system adjusts user model AH system compiles KIs AH system compiles curriculum AH system compiles final Estimation of user’s knowledge AH system evaluates final AH system stores information AH system adjusts KIs performes pre-test provides traits provides enrollment information User Info User Model Course Info L Estimation of user’s knowledge Eligibility for Final Test Demographic O Learning style J A G B D E Access H T Test history G H E J Privacy A Final test B O A G B D E H O J L T T Learning Goal  Knowledge Items Test results Test results are used for revising the user model. Final test used for adaptation effect

19. Authoring Tool Technical Implementation Learner Environment

20. Authoring Tools general course setting views of KIs abbreviated version of the core explanation (short) for novice users (basic) very detailed high-level textual explanation (main) for graduate level knowledge (advanced) multiple-choice questions fill-in-the-blank questions descriptive / online examples prerequisites KI metadata: keywords, semantic Teaching Tools student registration, performance, question analysis

22. Learner Environment General design approach follows Mark Weiser’s idea: “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” Scientific American, 265(3):94--104, September 1991

23. Learner Environment Overview page background about the subject of the course explanation about the concept of AH introduction to personal tutor acknowledgements

24. Learner Environment Emotional Intelligence? show emotional behavior (e.g. the persona-effect, help relieve frustration, recover to a positive emotional state)  beneficial for tutoring agents and learning software C Elliot, J.C. Lester, J. Rickel. Integrating affective computing into animated tutoring agents In Proceedings of the IJCAI Workshop on Animated Interface Agents Nagoya, Japan, 1997, 113–121 improve user acceptance J. Klein. Computer Response to User Frustration. TR480, MIT, 1998

25. Learner Environment user model is dynamic and is continuously revised not based on the content reviewed Beaumont 1994 Boyle and Encarnacion 1994 Vassileva 1996 Henze et al. 2002

26. LearningGoal User AH system re-compiles curriculum User Info User Model Course Info L Estimation of user’s knowledge Demographic Learning style D Access Test history G H E J Privacy A B O A U F K M R S T Learning Goal  Recompile Knowledge Items New curriculum User changes learning goal.

27. Annotations feedback is encouraged annotations = informal continuous formative evaluation *  all KIs are annotatable by learners * Henze et al. 2002

28. Design cross platform, all browsers screen resolutions with a minimum of 800x600 pixel Guidelines of the Design of Educational Software * * ANSI Standards Committee on Dental Informatics

29. Technology

30. Anno KI Anno Users KI Descript Ex Users Descriptive Examples Prerequisites KI Annotation User Model Questions KI Questions Questions Log

31. PHP Scripting // check what is up with prerequisites if($prereq!="disregard"){ $queryPrereq = "select Requires from AH_Prerequisites where KI='$KIID'"; $dbPrereq->query($queryPrereq); if($dbPrereq->num_rows()){ $querygetKI = "select AH_Knowledge_Items.KI_title, AH_Knowledge_Items.KIID from AH_Prerequisites, AH_Knowledge_Items where AH_Prerequisites.KI='$KIID' and AH_Prerequisites.Requires=AH_Knowledge_Items.KIID"; $dbgetKI->query($querygetKI); $KItitlewithlink=""; while($dbgetKI->next_record()): $vKI_title = $dbgetKI->Record["KI_title"]; $vKIID = $dbgetKI->Record["KIID"]; $score=getscore($vKIID, $UserID);

32. Evaluation initial review (cognitive walkthroughs ) alpha-test (task-oriented) formative evaluation (heuristic evaluation & end-user testing) summative evaluation (end-user testing) [planned]

33. Cognitive Walkthroughs Goal: solve anticipated problems before bringing the course to users for testing - performed independent and under observation of the developer Results (encountered problems were fixed immediately ) - confusing labels of fields - confusing options during enrollment - missing guidance on start page - repetition of test questions - inadequate feedback for wrong answers

34. Alpha Test task-oriented alpha test complete the course determine deficiencies (educational, instructional design, technical, design and layout) send an unstructured informal feedback Results (encountered problems were fixed immediately ) - general feedback was positive - personal tutor concept was beneficial - 12 specific usability, functionality issues were discovered (regarding: change of setting, annotations, quiz, flow, sequencing)

35. Formative Evaluation I Usability Inspection: Heuristic Evaluation general heuristic for UI design (Jakob Nielsen) heuristic extracted from ANSI standard “Guidelines for the Design of Education Software” http://di.dental.pitt.edu/edswstd/

36. In-Class Assignment Evaluate the pre- and post-questionnaires Problems/Critique Order of questions Comparing terms Formatting Disclaimer

37. Formative Evaluation II End-user testing separation between delivery (media) and instructional method use of a validated instrument  Evaluation of participants’ reactions achievement of program objectives changes in student learning values motivations ability to transfer knowledge acquired outside the instructional setting

38. Conclusions applying of established principles of AH research state-of-the-art programming environment separation of content and design Web-based delivery system For the first time, a dental educational delivery system adapts itself dynamically using active and passive curriculum sequencing and adaptive presentation. authoring process = challenge * * Brusilovsky, Eklund, and Schwartz 2002 * De Bra and Calvi 1998