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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.

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Sung Young’s comments

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Sung young s comments

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


Kurt scomments

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


Daren s comments

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


Andre s comments

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


Robert s comments

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


Maria s comments

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


Javier s comments

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.


Teachable agents a mini lit review implemented systems only

Teachable agents: A mini-lit review (implemented systems only)

Kurt VanLehn

CPI 494, March 19, 2009


Near synonyms for teachable agent

(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


Framework for comparing teachable agents

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


Betty s brain

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


Steps

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


Palthepu greer mccalla 1991

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


Sung young s comments

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


Pal s evaluation

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


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