Publications of Sarah Schott
%% Papers Published
@article{fds368209,
Author = {Schott, S and Slate Young and E and Bookman, J and Hash,
P},
Title = {An Examination of Factors that Support Sustainable Cultural
and Curricular Change in STEM Teaching and
Learning},
Journal = {Journal of Mathematics and Science: Collaborative
Explorations},
Volume = {18},
Year = {2022},
Abstract = {Using a mixed-methods design, this body of work from the
SUMMIT-P consortium explores possible effective conditions
for the sustainable reform of STEM teaching and learning at
the collegiate level. A model of catalysts for successful
and sustainable change is proposed, based on five years of
data collection and observations. These catalysts include
institutional support, intrinsic and extrinsic motivation of
faculty involved, measures of student success, institution
size, prior faculty experience, faculty buy-in, and
institutional culture. The discussion ends with a delve into
the potential broader impacts of this work. For example,
this model may help institutions better understand how to
implement curricular change more effectively.},
Doi = {10.25891/901k-qq89},
Key = {fds368209}
}
@article{fds353798,
Author = {Schott, S and Slate Young and E and Bookman, J and Peterson,
B},
Title = {Evaluating a Large-Scale Multi-Institution Project:
Challenges Faced and Lessons Learned},
Journal = {The Journal of Mathematics and Science: Collaborative
Explorations (JMSCE)},
Volume = {16},
Number = {1},
Year = {2020},
Abstract = {SUMMIT-P consists of nine participating institutions working
toward common goals but from unique perspectives. Evaluating
such a large-scale project with diverse stakeholders has
presented challenges. For one, evaluation on this scale
necessitates a team effort rather than a single evaluator.
Communication is key among the evaluators as well as among
the project players at large. Participation and reliable,
timely feedback from participants are perhaps the most
important issues while also posing some of our greatest
challenges. We present strategies we developed to counteract
these challenges. In particular, we discuss the development
of an assessment tracking system used to not only monitor
responses but to also promote an increase in on-time
responses. We conclude with a discussion of some lessons
learned about evaluating large-scale, multi-site projects to
share with other evaluators and PIs alike.},
Doi = {10.25891/5e14-nf34},
Key = {fds353798}
}
@article{fds296295,
Author = {Bookman, and Bar-On, R and Cooke, B and Schott, S},
Title = {(Re)discovering SoTL through a Fundamental Challenge:
Helping Students Transition to College Calculus},
Journal = {MAA Notes: Guide to the Scholarship of Teaching and Learning
in Mathematics},
Year = {2012},
Month = {Fall},
Key = {fds296295}
}
@article{fds368210,
Author = {Bar-On, R and Bookman, J and Cooke, B and Hall, D and Schott,
S},
Title = {BRINGING STUDENTS INTO THE PELOTON: LEVELING THE PLAYING
FIELD AT HIGHLY SELECTIVE UNIVERSITIES},
Journal = {EDULEARN12: 4TH INTERNATIONAL CONFERENCE ON EDUCATION AND
NEW LEARNING TECHNOLOGIES},
Pages = {1260-1270},
Publisher = {IATED-INT ASSOC TECHNOLOGY EDUCATION A& DEVELOPMENT},
Editor = {Chova, LG and Torres, IC and Martinez, AL},
Year = {2012},
Month = {January},
Key = {fds368210}
}
@article{fds296294,
Author = {Huber, M and Schott, S},
Title = {Random Construction of Interpolating Sets for High
Dimensional Integration},
Journal = {Journal of Applied Probability},
Volume = {51},
Number = {1},
Pages = {92-105},
Publisher = {Cambridge University Press (CUP)},
Year = {2012},
Abstract = {Computing the value of a high-dimensional integral can often
be reduced to the problem of finding the ratio between the
measures of two sets. Monte Carlo methods are often used to
approximate this ratio, but often one set will be
exponentially larger than the other, which leads to an
exponentially large variance. A standard method of dealing
with this problem is to interpolate between the sets with a
sequence of nested sets where neighboring sets have relative
measures bounded above by a constant. Choosing such a
well-balanced sequence can rarely be done without extensive
study of a problem. Here a new approach that automatically
obtains such sets is presented. These well-balanced sets
allow for faster approximation algorithms for integrals and
sums using fewer samples, and better tempering and annealing
Markov chains for generating random samples. Applications,
such as finding the partition function of the Ising model
and normalizing constants for posterior distributions in
Bayesian methods, are discussed. © Applied Probability
Trust 2014.},
Doi = {10.1239/jap/1395771416},
Key = {fds296294}
}
@article{fds296296,
Author = {Schott, SJ},
Title = {Girls in Math},
Journal = {Encompass Magazine},
Pages = {14-15},
Year = {2011},
Month = {April},
url = {http://issuu.com/encompassmag/docs/encompass_sp11},
Key = {fds296296}
}
@article{fds296297,
Author = {Huber, M and Schott, S},
Title = {Using TPA for Bayesian Inference},
Journal = {Bayesian Statistics 9},
Volume = {9780199694587},
Pages = {257-282},
Publisher = {Oxford Press},
Year = {2010},
url = {http://hdl.handle.net/10161/6637 Duke open access
repository},
Abstract = {Finding the integrated likelihood of a model given the data
requires the integration of a nonnegative function over the
parameter space. Classical Monte Carlo methods for numerical
integration require a bound or estimate of the variance in
order to determine the quality of the output. The method
called the product estimator does not require knowledge of
the variance in order to produce a result of guaranteed
quality, but requires a cooling schedule that must have
certain strict properties. Finding a cooling schedule can be
difficult, and finding an optimal cooling schedule is
usually computationally out of reach. TPA is a method that
solves this difficulty, creating an optimal cooling schedule
automatically as it is run. This method has its own set of
requirements; here it is shown how to meet these
requirements for problems arising in Bayesian inference.
This gives guaranteed accuracy for integrated likelihoods
and posterior means of nonnegative parameters.},
Doi = {10.1093/acprof:oso/9780199694587.003.0009},
Key = {fds296297}
}