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Virtual environments, MOOs and Virtual agents Slide 2 Virtual Environments Readings for this week: Peterson 1998 (VLE) Peterson 2004 MOO Morton and Jack 2005 Virtual agents Development of technology Slide 3 Virtual environments Learning environment (Peterson 1998) Very familiar one these days Does not now seem novel Slide 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. Slide 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 Caf Slide 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 Slide 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 Slide 8 Many VLEs available Individual sites, like Petersons CMS sites (Course Management System) Moodle, Web CT Intuto.com -- local online learning company Slide 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 Slide 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 Slide 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 Slide 12 Slide 13 Slide 14 Advantages of MOOs Increased communication Reduced stress Anonymous user New persona Easy to make a contribution Slide 15 Slide 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 Slide 17 Chatbots Turing test -- a test to see if a computer is intelligent. Loebner prize -- annual competition for chatbots Slide 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/ Slide 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 Slide 20 Slide 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 Slide 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) Slide 23 Semantic representations Dialogue Question: What do you want to eat? Learner: Id 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?? Slide 24 Desirable characteristics of speech interactive CALL Wachowicz and Scott 1999 Adopted by SPELL Slide 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 Slide 26 Observational scenario Slide 27 Clear situation Students listen to the interaction Subtitles available Stop/start/replay the dialogue Access to other materials Slide 28 One-to-one Slide 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 Slide 30 Interactive scenario Slide 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 Slide 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 Slide 33 Interactive scenario For each stage, there are assumed to be four types of response Slide 34 Recasts Slide 35 Recognition Grammar Slide 36 Help for the learner -- reformulation Slide 37 Error analysis Errors for each learner are logged Slide 38 Prototype system Technical developments -- speech recognition of pronunciation Students want more affective behaviour from the visual agents (Eliza effect) Slide 39 Virtual Reality MOOs are VR environments Text-based Active Worlds -- http://www.activeworlds.com/ http://www.activeworlds.com/ Education programs Slide 40 Active Worlds Slide 41 MOOs, Avatars,CMC Where is the learning? Issues?