Knowledge Acquisition as Tutorial Dialogue:
Some Ideas
Yolanda Gil
USC Information Sciences Institute
February 2001
Yolanda Gil
1
Exploring Possible Synergies
ITS
KA
(RKF)
Intelligent
Tutoring
System
(ITS)
Intelligent
Studious
System
?
USC Information Sciences Institute
?
teaches
teaches
February 2001
Yolanda Gil
2
Exploring Possible Synergies: Dialogue
Intelligent
Tutoring
Good
System
tutoring
(ITS)
strategies
Intelligent
Studious
System
(ISS)
?
Good
tutoring
strategies
USC Information Sciences Institute
?
teaches
teaches
February 2001
Yolanda Gil
3
What ITS community has

Mountains of example tutoring dialogues




Can be analyzed for strategies, misconceptions, hints and help
E.g., http://www.pitt.edu/~circle/Archive.htm
Many and diverse tutoring system have been built
Raised grades by 1.0 standard deviation units

Best humans raise grades by 2.0
USC Information Sciences Institute
February 2001
Yolanda Gil
4
Main Approaches to ITS



Coached practice and review
Socratic dialogue: questions discover student
misconceptions, avoid telling students what they need to
know
Critiquing student solutions
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February 2001
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5
Model Tracing Tutors [Anderson et al. 85]

Contain a model of the cognition designers want students
to engage
EXPERT MODEL
HIGH
BANDWITH
INTERFACE
-----?
------?
-----
PEDAGOGICAL
MODULE
X
X
√
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February 2001
Yolanda Gil
6
Model Tracing Tutors [Anderson et al. 85]
EXPERT MODEL
HIGH
BANDWITH
INTERFACE
-----?
------?
-----
PEDAGOGICAL
MODULE
X
X
√

Expert Model: how student should reason


HB Interface: where student displays reasoning


simple, precise, complete problem solving strategies
goal trees, explicating
Pedagogical Module: feedback and hints

immediate feedback, hint sequences with increasingly more help
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February 2001
Yolanda Gil
7
Key Research Projects

CIRCLE Research Center @ CMU


PACT Geometry tutor, Ken Koedinger
Andes Physics tutor, Kurt VanLehn
– Model tracing approach

CST: CIRCSIM-Tutor, from Illinois Institute of Technology




ACLS (& others) @ UMass


Socratic dialogue approach
Domain: physiology
Used in classrooms in a non-experimental basis
teaches a new concept when relevant during a simulation of ER
Many, many others: NEOMYCIN, SIERRA, CASCADE,
SOPHIE,...
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February 2001
Yolanda Gil
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Interactive Directive Lines of Reasoning
[Rose et al. 2000]
Instead of mini-lessons, which require that students have
prior knowledge and motivation
 Tutor starts by presenting student with a scenario and
lesson overview (“advanced organizer”)





Useful to draw prior knowledge (e.g., stating an analogy)
Useful to detect missing prior knowledge
Useful to give context to the new knowledge
Tutor asks detailed questions
Once student provides the desired answers, tutor ends
with a summary
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February 2001
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Interactive Directive Lines of Reasoning:
An Example
Tutor: Let’s think about the difference between speed and
velocity. A closely related distinction is that of the
difference between distance traveled and displacement
from the origin. Take as an example a bumblebee flying
from point A to point B by means of a curvy path. If you
draw a vector from point A to point B, you will have
drawn the bee’s displacement vector. What does the
displacement vector represent?
Student: The bee’s distance.
[…]
Tutor: So the equation for speed is the length of the path
traveled by the body divided by […], even if the path […]
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February 2001
Yolanda Gil
10
Fading and Deepening (I) [VanLehn et al. 2000]

Human tutors start with lots of scaffolding that later fades,
while ITS tools are quite rigid:

support one strategy
– st mix steps from different strategies
– st wonders what to do next, tool’s advice seems random (but he was!)


force students to enter information they hold in memory
provide too much scaffolding in detecting errors and hinting solns
– st looked for the last hint in the sequence that says what to enter
– hints are not bad, but may not make sense within student’s context
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February 2001
Yolanda Gil
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Fading and Deepening (II) [VanLehn et al. 2000]

Human tutors pursue deep learning
e.g.: lesson on how acceleration opposes velocity when slowing down
T: What is the definition of acceleration?
Tutor’s strategy: derive from definition
S: Velocity divided by time
Almost right, tutor enters 2nd level strat.
T: Yes, it is the change of velocity divided by time
S: It’s the derivation of time
Student is even more confused
T: Well, forget about the definition of acceleration.
Let’s try analogy. Suppose…

Abandon top-level strategy for another one
At most two nested strategies
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February 2001
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Fading and Deepening (III) [VanLehn et al. 2000]

Deep learning through knowledge construction dialogues

Teach a domain principle
– Three main KC types: from definition, analogy, contradiction

Teach to do right thing for right reasons (no guessing of actions)
– Tutor should ask to justify actions

Teach domain language
– Tutor should ask to say “I applied <principle> to <objs> because <goal>”

Emphasize basic approach instead of details
– Tutor should ask student to state basic approach

Qualitative skills, not just quantitative
– Tutor should ask qualitative questions during lesson
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February 2001
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Why do only some tutorial events cause
learning? [VanLehn et al. 98]

Analysis of tutorial dialogues showed that depending on
what is the rule being learned:




Students that make an error (reach impasse) tend to gain
Students that hear a generalization of a rule tend to gain
Students that produce incorrect equation gained when explained
why it was wrong (though not when using calculus)
Suggested strategies for ITS:


Tutors should let students make mistakes instead of avoiding that
by giving them strong hints
Different rules may require different kinds of tutorial explanations
(e.g., stating generalization, showing why wrong, etc.)
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February 2001
Yolanda Gil
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Discussion: Differences



ISS does not suffer lack of motivation
ISS can be built with a lot more initiative and participation
than a human student
ISS does not need “cognitive tricks”:

Eg, incremental hints, they can just be given the solution
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February 2001
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15
Discussion: Opportunities

Intelligent Student Systems


Training human tutors


Student guides dialogue using good teaching strategies
Tutor uses ISS to learn good teaching strategies
Simulated student colleagues

“I think the tutor meant …”
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February 2001
Yolanda Gil
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