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, Task domain: Designing recursive Lisp functions Reciprocal: Yes Communication: Weird Expert knowledge: Yes Evaluation: Underpowered

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

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

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 (2 nd 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

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?

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: Task domain: Boolean Algebra Reciprocal: user decides who will solve problem Communication: Editing agent’s knowledge Evaluation: Yes

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

Evaluation

Motivated vs. free

Results Underpowered: 8 per cell No significant differences between conditions

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

Schwartz, Chase, Chin et al. Pg 6 ff: Do students treat Betty as sentient & take responsibility for teaching her? – 5 th 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

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

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

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

Does SRL Betty help learning? 5 th 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

Results During training SRL Betty > Betty > Coach During transfer SRL Betty = Betty > Coach

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.