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Tuesday, January 24 • 4:30pm - 6:00pm
Poster: Dealbreaker: A Nonlinear Latent Variable Model for Educational Data

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Statistical models of student responses on assessment
questions, such as those in homeworks and
exams, enable educators and computer-based personalized
learning systems to gain insights into
students’ knowledge using machine learning. Popular
student-response models, including the Rasch
model and item response theory models, represent
the probability of a student answering a question
correctly using an affine function of latent factors.
While such models can accurately predict student
responses, their ability to interpret the underlying
knowledge structure (which is certainly nonlinear)
is limited. In response, we develop a new,
nonlinear latent variable model that we call the
dealbreaker model, in which a student’s success
probability is determined by their weakest concept
mastery.We develop efficient parameter inference
algorithms for this model using novel methods for
nonconvex optimization. We show that the dealbreaker
model achieves comparable or better prediction
performance as compared to affine models
with real-world educational datasets. We further
demonstrate that the parameters learned by the
dealbreaker model are interpretable—they provide
key insights into which concepts are critical
(i.e., the “dealbreaker”) to answering a question
correctly. We conclude by reporting preliminary
results for a movie-rating dataset, which illustrate
the broader applicability of the dealbreaker
model.

Tuesday January 24, 2017 4:30pm - 6:00pm
BioScience Research Collaborative Event Hall 6500 Main Street, Houston, TX 77030-1402

Attendees (1)