2 research outputs found
Computer-Aided Personalized Education
The shortage of people trained in STEM fields is becoming acute, and
universities and colleges are straining to satisfy this demand. In the case of
computer science, for instance, the number of US students taking introductory
courses has grown three-fold in the past decade. Recently, massive open online
courses (MOOCs) have been promoted as a way to ease this strain. This at best
provides access to education. The bigger challenge though is coping with
heterogeneous backgrounds of different students, retention, providing feedback,
and assessment. Personalized education relying on computational tools can
address this challenge.
While automated tutoring has been studied at different times in different
communities, recent advances in computing and education technology offer
exciting opportunities to transform the manner in which students learn. In
particular, at least three trends are significant. First, progress in logical
reasoning, data analytics, and natural language processing has led to tutoring
tools for automatic assessment, personalized instruction including targeted
feedback, and adaptive content generation for a variety of subjects. Second,
research in the science of learning and human-computer interaction is leading
to a better understanding of how different students learn, when and what types
of interventions are effective for different instructional goals, and how to
measure the success of educational tools. Finally, the recent emergence of
online education platforms, both in academia and industry, is leading to new
opportunities for the development of a shared infrastructure. This CCC workshop
brought together researchers developing educational tools based on technologies
such as logical reasoning and machine learning with researchers in education,
human-computer interaction, and cognitive psychology.Comment: A Computing Community Consortium (CCC) workshop report, 12 page
The Teaching Dimension of Linear Learners
Teaching dimension is a learning theoretic quantity that specifies the
minimum training set size to teach a target model to a learner. Previous
studies on teaching dimension focused on version-space learners which maintain
all hypotheses consistent with the training data, and cannot be applied to
modern machine learners which select a specific hypothesis via optimization.
This paper presents the first known teaching dimension for ridge regression,
support vector machines, and logistic regression. We also exhibit optimal
training sets that match these teaching dimensions. Our approach generalizes to
other linear learners