Knowledge Acquisition as Tutorial Dialogue: Some Ideas

1USC Information Sciences Institute Yolanda GilFebruary 2001

Knowledge Acquisition as Tutorial Dialogue:Some Ideas

Yolanda Gil


Exploring Possible Synergies

Intelligent TutoringSystem

(ITS)

Intelligent StudiousSystem teaches

teaches

?

?

ITS

KA(RKF)


Exploring Possible Synergies: Dialogue

Intelligent TutoringSystem(ITS)

Intelligent StudiousSystem(ISS) teaches

teaches

?

?

Good tutoring

strategies

Good tutoring

strategies


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


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


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√

-----? -------? -----


Model Tracing Tutors [Anderson et al. 85]

Expert Model: how student should reason simple, precise, complete problem solving strategies

HB Interface: where student displays reasoning goal trees, explicating

Pedagogical Module: feedback and hints immediate feedback, hint sequences with increasingly more help

EXPERT MODEL

HIGH BANDWITH INTERFACE

PEDAGOGICAL MODULE

X X√

-----? -------? -----


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 Socratic dialogue approach Domain: physiology Used in classrooms in a non-experimental basis

ACLS (& others) @ UMass teaches a new concept when relevant during a simulation of

ER Many, many others: NEOMYCIN, SIERRA, CASCADE,

SOPHIE,...


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


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 […]


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


Fading and Deepening (II) [VanLehn et al. 2000]

Human tutors pursue deep learning

At most two nested strategies

e.g.: lesson on how acceleration opposes velocity when slowing down T: What is the definition of acceleration?

S: Velocity divided by time

T: Yes, it is the change of velocity divided by time

S: It’s the derivation of time

T: Well, forget about the definition of acceleration. Let’s try analogy. Suppose…

Tutor’s strategy: derive from definition

Almost right, tutor enters 2nd level strat.

Student is even more confused

Abandon top-level strategy for another one


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


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.)


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


Discussion: Opportunities

Intelligent Student Systems Student guides dialogue using good teaching strategies

Training human tutors Tutor uses ISS to learn good teaching strategies

Simulated student colleagues “I think the tutor meant …”

Documents

Knowledge Acquisition as Tutorial Dialogue: Some Ideas