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Chan & Chou’s system

Chan & Chou’s system. Chan, T.-W., & Chou, C.-Y. (1997). Exploring the design of computer supports for reciprocal tutoring. International Journal of Artificial Intelligence and Education, 8, 1-29. Task domain: Designing recursive Lisp functions Reciprocal: Yes Communication: Weird

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Chan & Chou’s system

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  1. Chan & Chou’s system • Chan, T.-W., & Chou, C.-Y. (1997). Exploring the design of computer supports for reciprocal tutoring. International Journal of Artificial Intelligence and Education, 8, 1-29. • Task domain: Designing recursive Lisp functions • Reciprocal: Yes • Communication: Weird • Expert knowledge: Yes • Evaluation: Underpowered

  2. User interface for tutee role • Base case vs. recursive case • Syntax handled by GUI • Steps, but no immediate feedback; must submit/ask

  3. User interface for tutor role • Shows correct code & tutee’s code • User must localize tutee’s bug by descending through a “fault tree” • If user tries to descend to wrong node, its blocked by the system • When a leaf is reach, user selects which hint to give the tutee • Points are taken off for giving too specific a hint

  4. Evaluation’s conditions • 5 forms of single-user instruction • User is tutor & agent is tutee (teachable agent) • User is tutee & agent is tutor (tutoring system)  most motivating? Especially if mostly tutee early, like model scaffold fade theory. • User is tutee & agent is tutor (2nd version of tutor) • They switch roles periodically (reciprocal tutoring) • User works without help (no agent)  worst gains • 2 forms of two-user instruction • User1 is tutor, user2 is tutee & agent guides tutor • User1 is tutor & user2 is tutee (no agent)  gains

  5. Evaluation results • 5 students per condition  under powered • Teachable agent is worst condition • User is tutor & agent is tutee • Users reported that it was very easy to walk down the fault tree, but they didn’t learn much • Caution • Giving immediate feedback on tutoring actions invites gaming and no learning • Did this occur with PAL?

  6. LECOBA • Ramirez Uresti, J.A. and B. du Boulay (2004). “Expertise, Motivation, and Teaching in Learning by Teaching Systems, International Journal of Artificial Intelligence in Education 14: 67-106. • Task domain: Boolean Algebra • Reciprocal: user decides who will solve problem • Communication: Editing agent’s knowledge • Evaluation: Yes

  7. Editing the agent’s knowledge • User can change order of rules & how they are applied.

  8. Evaluation

  9. Motivated vs. free

  10. Results • Underpowered: 8 per cell • No significant differences between conditions

  11. Findings • The teachable agent sometimes rejected the user’s suggestions • If the agent thinks it knows a rule & the user suggests a different one, it will reject the user • This irritated the users • The teachable agent forgot sometimes • This surprised and irritated the users

  12. Schwartz, Chase, Chin et al. • Pg 6 ff: Do students treat Betty as sentient & take responsibility for teaching her? • 5th graders using Gameshow • Contestant is either Betty or user • Code attributions of K as self vs. Betty • When given opporutnity to prepare some more, TA group did and Student group did not

  13. How to do this study better? • More coding of transcripts for computer talk • Tutoring an agent vs. tutoring a person • Wizard of Oz; menu based communitcation • Turing test in detail Physiological measures e.g., pupil dialation

  14. Does TA reflect student knowledge? • High correlation between student answers to all possible questions and Betty’s answers. • Potential alternative to standard tests

  15. Does the TA make a difference in learning gains? • Using Betty vs. using just a concept map editor pg. 13 ff • Students in Betty’s reasoning method in that they became better at answering long inference chain questions • On simple short chain questions, no difference • On long chain questions, Betty gets better gradually. • Intact classes

  16. Does SRL Betty help learning? • 5th grades on river ecosystem for 7 class periods • SRL Betty • Mr. Davis prompts • Betty refuses to take quiz until taught enough • Betty • Mr. Davis provided direct hints after quiz • Intelligent Coach • Same as Betty without the cover story

  17. Results • During training SRL Betty > Betty > Coach • During transfer SRL Betty = Betty > Coach

  18. What did they do differently? • During training, SRL Betty forced students to do more debugging of their maps, so much more time on that than Betty and Coach groups • During transfer, SRL Betty group continued to do more debugging.

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