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