2,415 research outputs found
Development and Validation of the Colorado Learning Attitudes about Science Survey for Experimental Physics
As part of a comprehensive effort to transform our undergraduate physics
laboratories and evaluate the impacts of these efforts, we have developed the
Colorado Learning Attitudes about Science Survey for Experimental Physics
(E-CLASS). The E-CLASS assesses the changes in students' attitudes about a
variety of scientific laboratory practices before and after a lab course and
compares attitudes with perceptions of the course grading requirements and
laboratory practices. The E-CLASS is designed to give researchers insight into
students' attitudes and also to provide actionable evidence to instructors
looking for feedback on their courses. We present the development, validation,
and preliminary results from the initial implementation of the survey in three
undergraduate physics lab courses.Comment: 8 pages, 4 figures, 1 table, submitted to 2012 PERC Proceeding
SAT Modulo Monotonic Theories
We define the concept of a monotonic theory and show how to build efficient
SMT (SAT Modulo Theory) solvers, including effective theory propagation and
clause learning, for such theories. We present examples showing that monotonic
theories arise from many common problems, e.g., graph properties such as
reachability, shortest paths, connected components, minimum spanning tree, and
max-flow/min-cut, and then demonstrate our framework by building SMT solvers
for each of these theories. We apply these solvers to procedural content
generation problems, demonstrating major speed-ups over state-of-the-art
approaches based on SAT or Answer Set Programming, and easily solving several
instances that were previously impractical to solve
Development and results from a survey on students views of experiments in lab classes and research
The Colorado Learning Attitudes about Science Survey for Experimental Physics
(E-CLASS) was developed as a broadly applicable assessment tool for
undergraduate physics lab courses. At the beginning and end of the semester,
the E-CLASS assesses students views about their strategies, habits of mind, and
attitudes when doing experiments in lab classes. Students also reflect on how
those same strategies, habits-of-mind, and attitudes are practiced by
professional researchers. Finally, at the end of the semester, students reflect
on how their own course valued those practices in terms of earning a good
grade. In response to frequent calls to transform laboratory curricula to more
closely align it with the skills and abilities needed for professional
research, the E-CLASS is a tool to assess students' perceptions of the gap
between classroom laboratory instruction and professional research. The E-CLASS
has been validated and administered in all levels of undergraduate physics
classes. To aid in its use as a formative assessment tool, E-CLASS provides all
participating instructors with a detailed feedback report. Example figures and
analysis from the report are presented to demonstrate the capabilities of the
E-CLASS. The E-CLASS is actively administered through an online interface and
all interested instructors are invited to administer the E-CLASS their own
classes and will be provided with a summary of results at the end of the
semester
An epistemology and expectations survey about experimental physics: Development and initial results
In response to national calls to better align physics laboratory courses with
the way physicists engage in research, we have developed an epistemology and
expectations survey to assess how students perceive the nature of physics
experiments in the contexts of laboratory courses and the professional research
laboratory. The Colorado Learning Attitudes about Science Survey for
Experimental Physics (E-CLASS) evaluates students' epistemology at the
beginning and end of a semester. Students respond to paired questions about how
they personally perceive doing experiments in laboratory courses and how they
perceive an experimental physicist might respond regarding their research.
Also, at the end of the semester, the E-CLASS assesses a third dimension of
laboratory instruction, students' reflections on their course's expectations
for earning a good grade. By basing survey statements on widely embraced
learning goals and common critiques of teaching labs, the E-CLASS serves as an
assessment tool for lab courses across the undergraduate curriculum and as a
tool for physics education research. We present the development, evidence of
validation, and initial formative assessment results from a sample that
includes 45 classes at 20 institutions. We also discuss feedback from
instructors and reflect on the challenges of large-scale online administration
and distribution of results.Comment: 31 pages, 9 figures, 3 tables, submitted to Phys. Rev. - PE
Mixing Metaphors In The Cerebral Hemispheres: What Happens When Careers Collide?
Are processes of figurative comparison and figurative categorization different? An experiment combining alternative-sense and matched-sense metaphor priming with a divided visual field assessment technique sought to isolate processes of comparison and categorization in the 2 cerebral hemispheres. For target metaphors presented in the right visual field/left cerebral hemisphere (RVF/LH), only matched-sense primes were facilitative. Literal primes and alternative-sense primes had no effect on comprehension time compared to the unprimed baseline. The effects of matched-sense primes were additive with the rated conventionality of the targets. For target metaphors presented to the left visual field/right cerebral hemisphere (LVF/RH), matched-sense primes were again additively facilitative. However, alternative-sense primes, though facilitative overall, seemed to eliminate the preexisting advantages of conventional target metaphor senses in the LVF/RH in favor of metaphoric senses similar to those of the primes. These findings are consistent with tightly controlled categorical coding in the LH and coarse, flexible, context-dependent coding in the RH. (PsycINFO Database Record (c) 2013 APA, all rights reserved)(journal abstract
Quantified Uncertainty in Thermodynamic Modeling for Materials Design
Phase fractions, compositions and energies of the stable phases as a function
of macroscopic composition, temperature, and pressure (X-T-P) are the principle
correlations needed for the design of new materials and improvement of existing
materials. They are the outcomes of thermodynamic modeling based on the
CALculation of PHAse Diagrams (CALPHAD) approach. The accuracy of CALPHAD
predictions vary widely in X-T-P space due to experimental error, model
inadequacy and unequal data coverage. In response, researchers have developed
frameworks to quantify the uncertainty of thermodynamic property model
parameters and propagate it to phase diagram predictions. In previous studies,
uncertainty was represented as intervals on phase boundaries (with respect to
composition) or invariant reactions (with respect to temperature) and was
unable to represent the uncertainty in eutectoid reactions or in the stability
of phase regions. In this work, we propose a suite of tools that leverages
samples from the multivariate model parameter distribution to represent
uncertainty in forms that surpass previous limitations and are well suited to
materials design. These representations include the distribution of phase
diagrams and their features, as well as the dependence of phase stability and
the distributions of phase fraction, composition activity and Gibbs energy on
X-T-P location - irrespective of the total number of components. Most
critically, the new methodology allows the material designer to interrogate a
certain composition and temperature domain and get in return the probability of
different phases to be stable, which can positively impact materials design
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
Calcium imaging is an important technique for monitoring the activity of
thousands of neurons simultaneously. As calcium imaging datasets grow in size,
automated detection of individual neurons is becoming important. Here we apply
a supervised learning approach to this problem and show that convolutional
networks can achieve near-human accuracy and superhuman speed. Accuracy is
superior to the popular PCA/ICA method based on precision and recall relative
to ground truth annotation by a human expert. These results suggest that
convolutional networks are an efficient and flexible tool for the analysis of
large-scale calcium imaging data.Comment: 9 pages, 5 figures, 2 ancillary files; minor changes for camera-ready
version. appears in Advances in Neural Information Processing Systems 29
(NIPS 2016
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