Sung Young’s comments

1. Sung Young’s comments • Start up was rough. Asked Mr. Davis • SY was not able to enter whole content • Didn’ thave to learn whole content; could look up insead; would recommend closed book second session. • Learned the material by reading; The role of map was to force me to read ; not different from traditional

2. Kurt’ scomments • Semantics of the nodes “heat energy” • Asking Betty what a term means is not helpful • I didn’t feel like I was teaching

3. Daren’s comments • Darren: slow start. • Hates the voice • Felt good that Betty (not me) failing • Tried to put quiz into the net • The voice is a nice relief from (boring) reading, but the voice has to be better

4. Andre’s comments • Installatoin difficulties • Without knowing content, had hard start • Study guide was key • Switching screens was inconvenient • Gaming: Give her the quiz then put in corrective links • Needs direct instruction before • Believe that teaching reveals gaps

5. Robert’s comments • Another slow start • Read library; didn’t notice study guide • Putting underelined terms from library into graph • Found mattrix tab that showed % knowledge • Copied rows to graph; that didn’t work • Found that Mr. Davis’s suggestons weren’t helpful • Liked Betty’s logic; showing explanations • Ended up focussing on wrong answers from quiz • Cluttered graph; no zooming; auto format • Moving links insead of delete/receate • Popups annoying • How did they recognize terms e.g., mist vs. water vapor; spelling • Granularity of nodes wasn’t clear • Struggled with the “atmosphere” node’s meaning • Had to consciouisly engage in the teaching role; it supported that ; became more engaging • Mac vioice absent

6. Maria’s comments • Rought beginning • Need instruction on how to build a map • Pop ups annoying • One of Mr. Davis’ suggestons was incomprehensible • When Betty asks “does my answer seem right?” there is no way to answer. • Mr. Davis was not letting me do my own learning plan – draw first then later ask. Betty keeps asking me to expand inappropriately • Spelling check was nice • Nice that it checks for duplicates; but handles it poorly • What does the green line outside a node mean? • What is the purpose of the notes field on a node—where does it appear? • Saving and returning later? • Took labels from the matrix into the map nodes; • Worrying too much about passing quizes than learning climate change. • Betty’s is a tool/notation

7. Javier’s comments • Pop ups annoying, especially when not using Betty and when doing a task • Transferred labels from quiz to map; relationships too. Began to focus on answewring the quiz rather than learning • Quiz words were not the ones that I used; it needs to know synonyms. • Workspace is too small. Clutter. Losing nodes • How to control curves • Voice annoying; turned it off • Feel’s like Betty is teaching; reviewing my work as tutor • Betty’s comments about “now I’m underwstadning” feels like positive feedback.

8. Teachable agents: A mini-lit review (implemented systems only) Kurt VanLehn CPI 494, March 19, 2009

9. (Near) synonyms for “teachable agent” • Learning by teaching/tutoring system • Synonymous, but may not have image & persona • Simulated student • May be used for non-pedagogical purposes • Learning companion, Co-learner • User & agent are both learners • Knowledge acquisition system • User is assumed to be an expert already

10. Framework for comparing teachable agents • Same old nested loop structure: • Outer loop: select task, do task, repeat • Inner loop: prep step, do step, feedback, repeat • The human user plays role of tutor • Inner loop over steps – always • Outer loop over tasks – sometimes not • The agent plays role of tutee • May or may not have hidden domain knowledge • Role reversal? If yes, called reciprocal tutoring • Their communication is either or both of: • Unnatural: User can edit agent’s knowledge • Natural: Only observable actions and talk

11. Betty’s Brain • Task domains: stream ecology, climate change, body temperature regulation… • Reciprocal: No • Communication: Unnatural, Persona • User edits agent’s KR directly • User can ask questions, ask for traces of reasoning • Agent takes test • Expert knowledge: Yes • The scoring of the test • Evaluations: Next class meeting

12. Steps • Ur, S. & VanLehn, K. (1994). STEPS: A preliminary model of learning from a tutor. The Proceedings of the 16th Annual Conference of the Cognition Science Society. Hillsdale, NJ: Erlbaum. • Tasks: Solving physics problems • Reciprocal: No • Communication: Natural • User demonstrates solution while agent self-explains • Agent solves problems while user gives feedback on steps, including bottom-out hints • Expert knowledge: None • Evaluation: None

13. Palthepu, Greer & McCalla 1991 • Palthepu, S., Greer, J., & McCalla, G. (1991). Learning by teaching. The Proceedings of the International Conference on the Learning Sciences, AACE, 357-363. • Task: populating an inheritance hierarchy e.g., animals, dophins, legs, mamals, live births… • Reciprocal: No • Communication: Natural • Student tells agent facts • Agent asks the student questions “Do dolphins have legs?” • Expert knowledge: None • Evaluation: None

14. PAL • Reif, F., & Scott, L. A. (1999). Teaching scientific thinking skills: Students and computers coaching each other. American Journal of Physics, 67(9), 819-831. • Task: Solving physics problems • Reciprocal: Yes • Communication: Natural, no persona • Agent solves problems & user catches mistakes • User solves problems and agent catches mistakes • Expert knowledge: Yes • When user is tutoring, system corrects user’s tutoring • When agent is tutoring, then standard step-based tutoring • Evaluation: Yes • No tutoring < PAL = human tutoring

16. PAL’s evaluation • Class: did homework at home like usual • PAL: did homework in lab on PAL • Tutoring: did homework in lab with human tutors • Class < PAL d=1.01; Class < Tutoring d=1.31 • PAL = Tutoring • Confound? Class may not have been taught Reif’s method