1. Virtual environments, MOOs and Virtual agents

2. Virtual Environments Readings for this week: Peterson 1998 (VLE) Peterson 2004 MOO Morton and Jack 2005 Virtual agents Development of technology

3. Virtual environments Learning environment (Peterson 1998) Very familiar one these days Does not now seem novel

4. Construction of the learning environment Select a learning theory Cognitive processing model (Bialystock) Identify learner needs Needs analysis (questionnaire) (??) Choose website design tools Netscape Navigator Gold Browser/editor Hand-coding Dreamweaver etc.

5. Construction of the learning environment Instructional design/HCI (human-computer interface) issues Choice of number of links, font type and size, use of colour, arrangement of the page Links -- page 1 Cutting edge CALL Resources SchMOOze University Online English Grammar ESL Café

6. Construction of the learning environment Links -- page 2 Technical Writing Page Bilingual English-Japanese Online dictionary Online Writing Lab Online Thesaurus The Elements of Style etc. Links -- page 3 Presentation Resources The Virtual Presentation Assistant Briefing Notes on Giving Short Talks Giving a Scientific Talk

7. Virtual Learning Environment Site Evaluation Student feedback Lost in space -- Frames-based site More interactive materials needed More visual metaphors for navigation Online feedback link (email) Wider range of sites Site redesign

8. Many VLEs available Individual sites, like Peterson’s CMS sites (Course Management System) Moodle, Web CT Intuto.com -- local online learning company

9. Virtual Learning Environment Too static ?? Should be possible to create an individualised VLE Student types in requirements Web-page is generated based on those requirements

10. MOO MOOs and MUDs MOO -- multi-user object-oriented domain MOOs are virtual environments designed to facilitate synchronous text- based communication among users More permanent than chat rooms

11. SchMOOze University http://schmooze.hunter.cuny.edu/ Users log on (to a virtual domain such as a university) Create a nickname (and adopt an online persona ??) Users then interact, navigate and manipulate virtual objects Series of virtual rooms and objects

14. Advantages of MOOs Increased communication Reduced stress Anonymous user New persona Easy to make a contribution

16. Chatbots Original program -- Eliza Conversation with a psychiatrist (Rogerian type psychiatry) Designed to show that dumb programs could appear to be intelligent Eliza and chatbots http://www.cmr.fu- berlin.de/~mck/courses/lv00ss/PeKMan /team7/eliza.html http://www.cmr.fu- berlin.de/~mck/courses/lv00ss/PeKMan /team7/eliza.html

17. Chatbots Turing test -- a test to see if a computer is intelligent. Loebner prize -- annual competition for chatbots

18. Chatbot plus voice http://www.daden.co.uk/chatbots/pages/ 000067.html http://www.daden.co.uk/chatbots/pages/ 000067.html http://www.alicebot.org/

19. Visual agents Morton & Jack reading Avatars -- virtual beings -- extensions of humans in the virtual world. An avatar may be an virtual “you” Visual agents -- other beings in a virtual world

21. Spoken Electronic Language Learning SPELL -- Morton & Jack Includes speech recognition How good is speech recognition? How good is it with language learners Goal in this system is not to improve pronunciation, but to understand what the student says

22. Semantic representations My guess is that the system uses representations of meaning based on verbs and their arguments: Eat (I, hamburger) Want (I, (Eat (I, hamburger)) Want (I, (Eat (I, ??)) See (I, You)

23. Semantic representations Dialogue Question: What do you want to eat? Learner: I’d like a pizza Speech recognition has to decode the speech well enough to recognise hamburger or pizza etc. and create the meaning representation Want (I, (Eat (I, pizza)) This can then be used to continue with the dialogue -- what kind of pizza would you like Is the goal to have a dialogue or give feedback??

24. Desirable characteristics of speech interactive CALL Wachowicz and Scott 1999 Adopted by SPELL

25. Interactions in SPELL Learner and computer interact -- no learner input, no dialogue Constrained environment -- so that the learner contribution can be understood Scenarios Observational scenario One-to-one scenario Interactive scenario

26. Observational scenario

27. Clear situation Students listen to the interaction Subtitles available Stop/start/replay the dialogue Access to other materials

28. One-to-one

29. Virtual tutor agent asks the learner some questions About themselves About the dialogue What foods did the virtual people like? What foods does the learner like? Agents use pre-recorded audio files

30. Interactive scenario

31. Learner enters the scene If he orders water, the waitress will bring water. Constrained environment limits what the learner is likely to say Recognition grammar -- range of utterances that the customer might use

32. Interactive scenario Recognition grammar developed for each stage of the dialogue Possible learner “errors” are added to the recognition grammar The grammar is loaded into the program for each stage

33. Interactive scenario For each stage, there are assumed to be four types of response

34. Recasts

35. Recognition Grammar

36. Help for the learner -- reformulation

37. Error analysis Errors for each learner are logged

38. Prototype system Technical developments -- speech recognition of pronunciation Students want more “affective” behaviour from the visual agents (Eliza effect)

39. Virtual Reality MOOs are VR environments Text-based Active Worlds -- http://www.activeworlds.com/ http://www.activeworlds.com/ Education programs

40. Active Worlds

41. MOOs, Avatars,CMC Where is the learning? Issues?