38 research outputs found
Educational commitment and social networking: The power of informal networks
The lack of an engaging pedagogy and the highly competitive atmosphere in
introductory science courses tend to discourage students from pursuing science,
technology, engineering, and mathematics (STEM) majors. Once in a STEM field,
academic and social integration has been long thought to be important for
students' persistence. Yet, it is rarely investigated. In particular, the
relative impact of in-class and out-of-class interactions remains an open
issue. Here, we demonstrate that, surprisingly, for students whose grades fall
in the "middle of the pack," the out-of-class network is the most significant
predictor of persistence. To do so, we use logistic regression combined with
Akaike's information criterion to assess in- and out-of-class networks, grades,
and other factors. For students with grades at the very top (and bottom), final
grade, unsurprisingly, is the best predictor of persistence---these students
are likely already committed (or simply restricted from continuing) so they
persist (or drop out). For intermediate grades, though, only out-of-class
closeness---a measure of one's immersion in the network---helps predict
persistence. This does not negate the need for in-class ties. However, it
suggests that, in this cohort, only students that get past the convenient
in-class interactions and start forming strong bonds outside of class are or
become committed to their studies. Since many students are lost through
attrition, our results suggest practical routes for increasing students'
persistence in STEM majors.Comment: 12 pages, 2 figures, 8 tables, 6 pages of Supplementary Material
Revealing Differences Between Curricula Using the Colorado Upper-Division Electrostatics Diagnostic
The Colorado Upper-Division Electrostatics (CUE) Diagnostic is an exam
developed as part of the curriculum reform at the University of Colorado,
Boulder (CU). It was designed to assess conceptual learning within
upper-division electricity and magnetism (E&M). Using the CUE, we have been
documenting students' understanding of E&M at Oregon State University (OSU)
over a period of 5 years. Our analysis indicates that the CUE identifies
concepts that are generally difficult for students, regardless of the
curriculum. The overall pattern of OSU students' scores reproduces the pattern
reported by Chasteen et al. at CU. There are, however, some important
differences that we will address. In particular, our students struggle with the
CUE problems involving separation of variables and boundary conditions. We will
discuss the possible causes for this, as well as steps that may rectify the
situation.Comment: 4 pages, 3 figures, 1 tabl
Assessing student reasoning in upper-division electricity and magnetism at Oregon State University
Standardized assessment tests that allow researchers to compare the
performance of students under various curricula are highly desirable. There are
several research-based conceptual tests that serve as instruments to assess and
identify students' difficulties in lower-division courses. At the
upper-division level, however, assessing students' difficulties is a more
challenging task. Although several research groups are currently working on
such tests, their reliability and validity are still under investigation. We
analyze the results of the Colorado Upper-Division Electrostatics diagnostic
from Oregon State University and compare it with data from University of
Colorado. In particular, we show potential shortcomings in the Oregon State
University curriculum regarding separation of variables and boundary
conditions, as well as uncover weaknesses of the rubric to the free response
version of the diagnostic. We also demonstrate that the diagnostic can be used
to obtain information about student learning during a gap in instruction. Our
work complements and extends the previous findings from the University of
Colorado by highlighting important differences in student learning that may be
related to the curriculum, illuminating difficulties with the rubric for
certain problems and verifying decay in post-test results over time.Comment: 11 pages, 12 figure
Practitioner’s guide to social network analysis: Examining physics anxiety in an active-learning setting
The application of social network analysis (SNA) has recently grown prevalent in science, technology, engineering, and mathematics education research. Research on classroom networks has led to greater understandings of student persistence in physics majors, changes in their career-related beliefs (e.g., physics interest), and their academic success. In this paper, we aim to provide a practitioner’s guide to carrying out research using SNA, including how to develop data collection instruments, setup protocols for gathering data, as well as identify network methodologies relevant to a wide range of research questions beyond what one might find in a typical primer. We illustrate these techniques using student anxiety data from active-learning physics classrooms. We explore the relationship between students’ physics anxiety and the social networks they participate in throughout the course of a semester. We find that students’ with greater numbers of outgoing interactions are more likely to experience decrease in anxiety even while we control for pre-anxiety, gender, and final course grade. We also explore the evolution of student networks and find that the second half of the semester is a critical period for participating in interactions associated with decreased physics anxiety. Our study further supports the benefits of dynamic group formation strategies that give students an opportunity to interact with as many peers as possible throughout a semester. To complement our guide to SNA in education research, we also provide a set of tools for other researchers to use this approach in their work—the SNA toolbox—that can be accessed on GitHub
Network analysis of graduate program support structures through experiences of various demographic groups
Physics graduate studies are substantial efforts, on the part of individual
students, departments, and institutions of higher education. Understanding the
factors that lead to student success and attrition is crucial for improving
these programs. Students' broadly defined experiences related to support
structures are one such factor that has recently begun being investigated. The
aspects of student experience scale (ASES), a Likert-style survey, was
developed by researchers to do just that. Previously, we have used the ASES
data set to develop and demonstrate the network approach for Likert-style
surveys (NALS) methodology. We found that NALS can identify thematic clusters
within the resulting network that help to define different large-scale patterns
for individually linked experiences. In this study, we leverage NALS to provide
a unique interpretation of responses to the ASES instrument for well-defined
demographic groups and showcase how this approach can be applied to other
Likert-style data sets. We confirm the validity of the resulting themes by
studying their stability and investigating how they are expressed within
demographic-based networks.Comment: 17 pages, 6 figure
Linking engagement and performance: The social network analysis perspective
Theories developed by Tinto and Nora identify academic performance, learning
gains, and involvement in learning communities as significant facets of student
engagement that, in turn, support student persistence. Collaborative learning
environments, such as those employed in the Modeling Instruction introductory
physics course, provide structure for student engagement by encouraging
peer-to-peer interactions. Because of the inherently social nature of
collaborative learning, we examine student interactions in the classroom using
network analysis. We use centrality---a family of measures that quantify how
connected or "central" a particular student is within the classroom
network---to study student engagement longitudinally. Bootstrapped linear
regression modeling shows that students' centrality predicts future academic
performance over and above prior GPA for three out of four centrality measures
tested. In particular, we find that closeness centrality explains 28 % more of
the variance than prior GPA alone. These results confirm that student
engagement in the classroom is critical to supporting academic performance.
Furthermore, we find that this relationship for social interactions does not
emerge until the second half of the semester, suggesting that classroom
community develops over time in a meaningful way
QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
Over the past decade, machine learning techniques have revolutionized how
research is done, from designing new materials and predicting their properties
to assisting drug discovery to advancing cybersecurity. Recently, we added to
this list by showing how a machine learning algorithm (a so-called learner)
combined with an optimization routine can assist experimental efforts in the
realm of tuning semiconductor quantum dot (QD) devices. Among other
applications, semiconductor QDs are a candidate system for building quantum
computers. The present-day tuning techniques for bringing the QD devices into a
desirable configuration suitable for quantum computing that rely on heuristics
do not scale with the increasing size of the quantum dot arrays required for
even near-term quantum computing demonstrations. Establishing a reliable
protocol for tuning that does not rely on the gross-scale heuristics developed
by experimentalists is thus of great importance. To implement the machine
learning-based approach, we constructed a dataset of simulated QD device
characteristics, such as the conductance and the charge sensor response versus
the applied electrostatic gate voltages. Here, we describe the methodology for
generating the dataset, as well as its validation in training convolutional
neural networks. We show that the learner's accuracy in recognizing the state
of a device is ~96.5 % in both current- and charge-sensor-based training. We
also introduce a tool that enables other researchers to use this approach for
further research: QFlow lite - a Python-based mini-software suite that uses the
dataset to train neural networks to recognize the state of a device and
differentiate between states in experimental data. This work gives the
definitive reference for the new dataset that will help enable researchers to
use it in their experiments or to develop new machine learning approaches and
concepts.Comment: 18 pages, 6 figures, 3 table
Stability of Frustration-Free Hamiltonians
We prove stability of the spectral gap for gapped, frustration-free Hamiltonians under general, quasi-local perturbations. We present a necessary and sufficient condition for stability, which we call Local Topological Quantum Order and show that this condition implies an area law for the entanglement entropy of the groundstate subspace. This result extends previous work by Bravyi et al. on the stability of topological quantum order for Hamiltonians composed of commuting projections with a common zero-energy subspace. We conclude with a list of open problems relevant to spectral gaps and topological quantum order