Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
Niels Pinkwart, Vincent Aleven, Kevin Ashley, and Collin Lynch
Carnegie Mellon UniversityUniversity of Pittsburgh
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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An ITS for legal argumentation Problem: legal argumentation is an ill-
defined domain ITS approach: Engage students in analyzing
& reflecting about examples of expert Socratic reasoning
Application of collaborative filtering and social navigation principles: “Standard” activities: markup, “tagging”
resources, recommending objects created by peers
Novel function: indirect, results employed as tools to generate better feedback in ITS
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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US Supreme Court Oral Arguments
Important part of decision process Attorneys propose a decision rule
(“test”) to determine how to decide a case
Justices challenge these tests, often by posing hypothetical scenarios
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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An Example Case Example: Lynch v. Donnelly 465 U.S. 668
(1984) Facts: The city of Pawtucket annually erected
a Christmas display located in the city's shopping district. The display included such objects as a Santa Claus house, a Christmas tree, a banner reading "Seasons Greetings," and a nativity scene.
Question: Did this violate the constitutional separation of Church and State?
MR. DE LUCA: With the possible exception of the cross, the nativity scene is one of the most powerful religious symbols in this country, and most certainly one of the most powerful Christian religious symbols in this country. (…) Pawtucket's purchase, the maintenance, and the erection of the fundamental Christian symbol involves government in religion to a profound and substantial degree. (…)
JUSTICE: Now, if the city did not own the crèche itself, so that everything that was contributed to the display, including the crèche, were privately owned, it wouldn't violate the First Amendment, the fact that it was right next door to the City Hall, would it?
MR. DE LUCA: I think that in understanding that the city owns all of the symbols and all of the artifacts that are contained in this display, and assuming that the crèche were purchased and paid for privately without any other explanation that it is private, then I think it would still violate the establishment clause for the First Amendment, because there is no indication to anyone looking at that that the display or the crèche is not part of the broader display which is put up and sponsored by the city. (…)
JUSTICE: Would you regard the prayer that I spoke of to your friend in the House or the Senate or in any state legislature as purely symbolic, or is it a matter of substance?
Example: Tests and Hypotheticals
Test
Hypo
TestModif.
Hypo
A Tool For Graphical Argument Visualization
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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An Example Diagram(Result of Pilot Study)
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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Intelligent Support How help students
analyze the argument transcript? navigate the interlinked information spaces?
Automated diagram analysis allow for: Graph structure inspection
general argumentation principles, e.g. “there should be at least one test”
Checks of links between graph and transcript case specific “important passages”
Not subject of this talk ( ITS 2006)
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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Content Analysis?
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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Content WeaknessesIdea:
Make use of peer students working on the same task
Have students rate peer solutions as part of their working with the system
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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Content Weaknesses Exploit that the system
knows what part of the graph refers to certain important parts of text…
Student A
Student B
Student C
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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Content Weaknesses … and use these
relations for generating the dialogs.
Student A
Student B
Student C
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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Principle for quality rating q: weighted average of base rating and evaluation rating (0=poor, 1=excellent)
Base rating Based on how student rates other solutions Serves as initial score heuristic, immediately
available Assumption: having good solution correlates to
recognizing good solutions
n
kii
k
ii qq
nb
11
)1(1
Content Weaknesses
Recommended items Non-recommended items
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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Evaluation rating Based on recommendations a student’s
answer receives (or not), and by whom Develops over time Takes peer opinions into account Assumption: measures actual quality
j
ii
p
ii
q
qe
1
1
Content Weaknesses
Actual recommenders
All possible recommenders
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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Feedback: Self Explanation Prompts Use detected
weaknesses as tailored self explanation prompts
Offer opportunities for reflection about specific parts of Socratic reasoning examples
Present if quality rating below specific threshold (e.g. 0.3)
Does the System provide Feedback when appropriate?
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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Conclusion and OutlookUse collaborative filtering methods to generate a quality
heuristic for important parts of argument diagrams
Filter for quality Aim is not to
show only “best” or “most matching” argument descriptions
get to very precise rating Instead: use implicitly to generate adaptive feedback
in the ITS
Pilot studies with single users successful, studies with small groups to come
Larger lab studies to evaluate the ITS: Fall 2006
Niels PinkwartAH 2006
Using Collaborative Filtering in an Intelligent Tutoring System for Legal Argumentation
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Please visit our project website:
http://www.cs.cmu.edu/~hypoform
Email: