Our group meets on Thursdays at 11:00 a.m., FL 246, Beckett Conf. Room.
Dates and topics for this semester are as follows:
- Sept 8
- AIRG Organizational Meeting (Coordinator: DCB)
- Sept 15
- Mike Macasek
- Intelligent Tutoring System demonstration
- Sept 22
- Dave Brown
- The Relationship between Function and Affordance
- Sept 29
- Mingyu Feng
- Longitudinal Data Analysis on Assistment Data
- Oct 6
- Iain Boyle
- CAFixD: A case-based reasoning fixture design method
- Oct 13
- Marc DiNino, Jim Kazmierczak, Mary Plunkett
- WITS Tutoring system MQP
- (in cooperation with SEU and NASA GSFC)
- Oct 20
- {no mtg}
- Oct 27
- Aparna Varde
- LearnMet: Learning Domain-Specific Distance Metrics for Plots of
Scientific Functions
- PhD Progress report
ABSTRACT: Scientific experimental results are often depicted as plots of functions
to aid their visual analysis and comparison. In computationally comparing
these plots using techniques such as similarity search and clustering, the
notion of similarity is typically distance. However, it is seldom known
which distance metric(s) best preserve(s) semantics in the given domain.
It is thus desirable to learn such metrics for domain-specific comparison
of plots. This is the goal of our proposed technique, LearnMet. The input
to LearnMet is a training set with correct clusters of plots. These are
iteratively compared with clusters over the same plots obtained using an
arbitrary but fixed clustering algorithm. Using a guessed initial metric
for clustering, adjustments are made to the metric in each epoch based on
the error between the obtained and correct clusters until the error is
minimal or below a given threshold. The metric giving the lowest error is
the learned metric. The proposed LearnMet technique and its refinements
are discussed in this paper. The primary application of LearnMet is
clustering plots in the Heat Treating domain. Hence it is rigorously
evaluated using Heat Treating data. Given distinct test sets for
evaluation, clusters of plots predicted using the learned metrics are
compared with given actual clusters over the same plots. The extent to
which the predicted and actual clusters match each other denotes the
quality of the learned metrics.
- Nov 3
- Terrence Turner
- Intelligent Tutoring
- (MS Thesis presentation)
- Nov 10
- Goss Nuzzo-Jones
- Common Tutor Object Platform -
An Intelligent Tutoring System Software Development Strategy
- (MS Thesis presentation)
- Nov 17
- John Hayward
- Mining Oncological Data
- (MS Thesis proposal presentation)
- Nov 24
- Thanksgiving Break
- Dec 1
- Jay Walonoski
- Gaming Detection and Prevention in Intelligent Tutoring Systems
- (MS Thesis presentation)
ABSTRACT:
A major issue in Intelligent Tutoring Systems is off-task student
behavior, especially performance-based gaming, where students
systematically exploit tutor behavior in order to advance through a
curriculum quickly and easily, with as little active thought directed
at the educational content as possible.
This talk will focus on two main areas of research related to gaming
behavior, using the Assistments system as the experimental test bed.
The first area is the detection of gaming behavior within the
Assistments system, using machine learning techniques and methods
pioneered in prior studies in related systems. The second area is
our initial attempt to develop a non-intervening mechanism for the
determent and prevention of off-task gaming behavior.
- Dec 8
- Kevin Menard
- A study of voluntary feedback
- (in cooperation with Microsoft)
- Dec 15
- No meeting
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