Water Clarity at the River-Estuary Transition Zone: A Comparative Study of the James, Mattaponi, and Pamunkey Sub-estuaries

Water clarity is a key parameter for monitoring water quality and often used to assess habitat suitability for submerged aquatic vegetation (SAV). Light attenuation, a measure of water clarity, is impacted by colored dissolved organic matter (CDOM), and by suspended particulates which include living and non-living components. We anticipated that the relative importance of these factors in regulating light attenuation would vary among the upper portions of three sub-estuaries differing in morphometry, hydrology, and degree of human influence. The James is characterized by eutrophic conditions and high algal abundance, whereas the Mattaponi and Pamunkey exhibit lower phytoplankton production. The Mattaponi and Pamunkey have extensive floodplains, which likely serve as sources for CDOM. We measured light attenuation, turbidity, total suspended solids (TSS), chlorophyll a (CHLa), dissolved organic carbon (DOC), and CDOM over a 3-year period at sites within each estuary. These parameters, along with discharge, were analyzed to identify factors regulating light attenuation. The Mattaponi and Pamunkey exhibited greater light attenuation than the James. Turbidity and TSS were the strongest predictors of variation in light attenuation at all sites. CHLa was not found to be a significant predictor of light attenuation at any of the sites. Light scattering per unit of suspended particle mass was twice as high in the James compared to the other rivers despite similarities in suspended particle size and mass. Linear statistical models based on suspended solids and dissolved organic matter accounted for 64-93% of the range of variation in light attenuation. Understanding factors that regulate light attenuation is important when considering management activities intended to improve estuarine water clarity and SAV habitat

Modeling student pathways in a physics bachelor's degree program

Physics education research has used quantitative modeling techniques to explore learning, affect, and other aspects of physics education. However, these studies have rarely examined the predictive output of the models, instead focusing on the inferences or causal relationships observed in various data sets. This research introduces a modern predictive modeling approach to the PER community using transcript data for students declaring physics majors at Michigan State University (MSU). Using a machine learning model, this analysis demonstrates that students who switch from a physics degree program to an engineering degree program do not take the third semester course in thermodynamics and modern physics, and may take engineering courses while registered as a physics major. Performance in introductory physics and calculus courses, measured by grade as well as a students' declared gender and ethnicity play a much smaller role relative to the other features included the model. These results are used to compare traditional statistical analysis to a more modern modeling approach.Comment: submitted to Physical Review Physics Education Researc

An Exploration of Diversity and Inclusion in Introductory Physics

Diversity and inclusion has been a concern for the physics community for nearly 50 years. Despite significant efforts including the American Physical Society (APS) Conferences for Undergraduate Women in Physics (CUWiP) and the APS Bridge Program, women, African Americans, and Hispanics continue to be substantially underrepresented in the physics profession. Similar efforts within the field of engineering, whose students make up the majority of students in the introductory calculus-based physics courses, have also met with limited success. With the introduction of research-based instruments such as the Force Concept Inventory (FCI), the Force and Motion Conceptual Evaluation (FMCE), and the Conceptual Survey of Electricity and Magnetism (CSEM), differences in performance by gender began to be reported. Researchers have yet to come to an agreement as to why these gender gaps exist in the conceptual inventories that are widely used in physics education research and/or how to reduce the gaps.;The gender gap has been extensively studied; on average, for the mechanics conceptual inventories, male students outperform female students by 13% on the pretest and by 12% post instruction. While much of the gender gap research has been geared toward the mechanics conceptual inventories, there have been few studies exploring the gender gap in the electricity and magnetism conceptual inventories. Overall, male students outperform female students by 3.7% on the pretest and 8.5% on the post-test; however, these studies have much more variation including one study showing female students outperforming male students on the CSEM.;Many factors have been proposed that may influence the gender gap, from differences in background and preparation to various psychological and sociocultural effects. A parallel but largely disconnected set of research has identified gender biased questions within the FCI. This research has produced sporadic results and has only been performed on the FCI. The work performed in this manuscript will seek to synthesize these strands and use large datasets and deep demographic data to understand the persistent differences in male and female performance

Examining the relationship between student performance and video interactions

In this work, we attempted to predict student performance on a suite of laboratory assessments using students' interactions with associated instructional videos. The students' performance is measured by a graded presentation for each of four laboratory presentations in an introductory mechanics course. Each lab assessment was associated with between one and three videos of instructional content. Using video clickstream data, we define summary features (number of pauses, seeks) and contextual information (fraction of time played, in-semester order). These features serve as inputs to a logistic regression (LR) model that aims to predict student performance on the laboratory assessments. Our findings show that LR models are unable to predict student performance. Adding contextual information did not change the model performance. We compare our findings to findings from other studies and explore caveats to the null-result such as representation of the features, the possibility of underfitting, and the complexity of the assessment.Comment: 4 pages, 1 figure, submitted to the PERC 2018 proceeding

If the Shoe Fits: Proposing a Randomised Control Trial on the effect of a digitised in-custody footwear technology compared to a paper-based footwear method.

In order to address the issue of footwear capture from individuals arrested for recordable crime, technology has been developed, which is known as Tread Finder. This technology and development was made possible through Home Office Police Innovation Funding. Tread Finder is now a finished product and the technology has been deployed into a North London custody suite. Tread Finder incorporates the use of a 300 dpi scanner and newly developed software enabling capture, assisted coding and automated geographical crime scene searching. This paper sets out the proposal of a Randomised Control Trial to replicate and upscale a previous lab based experiment into a field environment to assess the cost, efficiency and crime solving benefits realised as a result of deploying Tread Finder technology compared with the previous paper based alternative

Gender Fairness within the Force Concept Inventory

Research on the test structure of the Force Concept Inventory (FCI) has largely ignored gender, and research on FCI gender effects (often reported as "gender gaps") has seldom interrogated the structure of the test. These rarely-crossed streams of research leave open the possibility that the FCI may not be structurally valid across genders, particularly since many reported results come from calculus-based courses where 75% or more of the students are men. We examine the FCI considering both psychometrics and gender disaggregation (while acknowledging this as a binary simplification), and find several problematic questions whose removal decreases the apparent gender gap. We analyze three samples (total $N_{pre}=5,391$, $N_{post}=5,769$) looking for gender asymmetries using Classical Test Theory, Item Response Theory, and Differential Item Functioning. The combination of these methods highlights six items that appear substantially unfair to women and two items biased in favor of women. No single physical concept or prior experience unifies these questions, but they are broadly consistent with problematic items identified in previous research. Removing all significantly gender-unfair items halves the gender gap in the main sample in this study. We recommend that instructors using the FCI report the reduced-instrument score as well as the 30-item score, and that credit or other benefits to students not be assigned using the biased items.Comment: 18 pages, 3 figures, 5 tables; submitted to Phys. Rev. PE

Identifying features predictive of faculty integrating computation into physics courses

Computation is a central aspect of 21st century physics practice; it is used to model complicated systems, to simulate impossible experiments, and to analyze mountains of data. Physics departments and their faculty are increasingly recognizing the importance of teaching computation to their students. We recently completed a national survey of faculty in physics departments to understand the state of computational instruction and the factors that underlie that instruction. The data collected from the faculty responding to the survey included a variety of scales, binary questions, and numerical responses. We then used Random Forest, a supervised learning technique, to explore the factors that are most predictive of whether a faculty member decides to include computation in their physics courses. We find that experience using computation with students in their research, or lack thereof and various personal beliefs to be most predictive of a faculty member having experience teaching computation. Interestingly, we find demographic and departmental factors to be less useful factors in our model. The results of this study inform future efforts to promote greater integration of computation into the physics curriculum as well as comment on the current state of computational instruction across the United States

Partitioning the gender gap in physics conceptual inventories: Force Concept Inventory, Force and Motion Conceptual Evaluation, and Conceptual Survey of Electricity and Magnetism

Over the last decade, the “gender gap” in physics conceptual inventory scores has been extensively studied by the physics education research community. Researchers have identified many factors that influence the overall differences in post-test scores between men and women. More recently, it has been shown that the Force Concept Inventory (FCI) contains eight items that are substantially unfair; six are unfair to women, two are unfair to men. The Force and Motion Conceptual Evaluation (FMCE) and the Conceptual Survey of Electricity and Magnetism (CSEM), however, contain fewer unfair items. In this work, results from prior studies are used to further explore the gender gap in five large samples of conceptual inventory data: the FCI (N1 ¼ 3663), the FMCE (N2 ¼ 2551, N3 ¼ 3719), and the CSEM (N4 ¼ 1767, N5 ¼ 2439). The gender gap in these samples is partitioned into four components: the gender gap resulting from the student’s academic performance, the gender gap resulting from prior preparation in physics, the gender gap resulting from instrumental fairness, and the gender gap of students with equal academic performance and physics preparation on the fair instrument. For all samples, very little of the gender gap was explained by differences in academic performance between men and women, measured by ACT or SAT math percentile scores or physics test average. The percentage of the gender gap resulting from instrumental fairness varied across samples from 30% in the FCI to 2% to 6% in the CSEM. A substantial part of the gender gap in four of the five samples (30%–40%) was explained by differences in prior physics preparation, measured by pretest scores on the conceptual inventories. Further correcting for conceptual physics prior preparation using the post-test score in the previous class reduced gender differences substantially