This study was an exploration into the effects of four categories of extra-student variables: high school performance, demographic characteristics, Myers-Briggs personality preferences and mathematics attitudes on three measures of college mathematics achievement (a Problem-Solving Test, an Algebra Skills Final Examination and course grade for all seven classes of 175 undergraduate students taking Pre-Calculus I Fall semester 1993). High school performance explained the most variation for all measures of mathematics achievement. Demographic characteristics and mathematics attitudes do not significantly influence any measure of mathematics achievement. The Myers-Briggs Type Indicator (MBTI) preference Extravert versus Intraverts (E versus 1) was a significant predictor for the Problem-Solving Test; the Judging versus Perception a versus P) preference was a significant predictor for the Algebra Skills Final Examination, and both E versus I and J versus P were predictors for the course grade. An experimental design was used to explore four classroom variables--3 class times, 2 instructional settings, MBTI E versus I and J versus P-- in six classes. Students taking 8:00 classes averaged 9 points lower than students taking 10:00 classes and 11 points lower than students taking 1:00 classes for all measures of mathematics achievement. There was no significant difference for the two instructional settings--cooperative learning or traditional lecture--for any measure of mathematics achievement. Students who were Introverted averaged 8 points higher on the Problem-Solving Test. Students who had the Judging preference averaged 11 points higher on the Algebra Skills Final Examination and 5 points higher for the course grade. There was a significant interaction (p<.01) for the Problem-Solving Test of class Time x instructional setting caused by the poor performance of the 8:00 Cooperative Learning class. The interaction of E versus I x J versus P or the EIJP learning styles was significant (p<.05) for the Algebra Skills Final Examination and course grade. The students with the IJ learning style averaged 13 to 20 points higher for scores on the Algebra Skills Final Examination and 11 points higher for scores on the course grade than students with the other three learning styles--EP, EJ and IP. / Ph. D.

Freshwater turtles are among the most threatened groups of taxa globally, and the bog turtle, Glyptemys muhlenbergii is among the most imperiled in North America. In Virginia, USA, bog turtles are restricted to occupying Appalachian Mountain fens. Fens are naturally small and fragmented wetlands characterized by elevated water tables and an open canopy. Although there is a strong need to document and monitor populations of bog turtles, efforts to do so are often limited by the low detection of the species. The first objective of this thesis was to assess proposed methodologies for locating populations of turtles on the landscape. My first chapter assessed a previously-developed habitat distribution model for bog turtles using an occupancy modeling approach. I conducted 216 surveys of 49 discretely predicted patches of habitat, recording conditions such as weather, size of wetland and time of year, hypothesized to affect detection during each survey. In addition, I assessed factors including stream entrenchment, grazing presence and surrounding impervious surfaces for each surveyed patch to identify data sources that could improve future models or better assess sites. I found that sites with larger total wetland area had higher detection per survey, possibly due to larger sites having higher densities of turtles (among other explanations), and that sites with higher amounts of impervious surfaces within their drainage were less likely to be occupied. In addition to the bog turtle, several plant species also occur in mountain fens. These species usually have a locally rare distribution or are disjuncts from a more northern latitude. Because of these traits, a high diversity of specialist plants may be indicative of a fen with a robust hydrology that has historically been less disturbed. Past site quality analyses have proposed using indicator diversity to assess sites, but no study has found if these species to tend to co-occur. My second chapter examines this hypothesis. I first chose a list of plant species that would most likely have habitat requirements similar to those of turtles. Then, at 12 sites, 6 with turtles and 6 without, I conducted a complete floral inventory. I first tested community-wide differences between the floral communities of these sites and found no difference, but when I narrowed my analysis to examining occurrence patterns of plant species determined a priori to be fen specialists and Glyptemys muhlenbergii, a pattern of co-occurrence was found. This lends support to the idea that indicator plants could be used as a tool to better evaluate sites that may have bog turtles. My last chapter investigated movement of bog turtles in a landscape impacted by anthropogenic development. Movement of turtles between adjacent sites is critical to maintaining genetic diversity and maintaining metapopulation integrity. Despite this importance, records of long distances movements among wetlands are scarce in the literature, likely due to the lack of long-term studies for areas with multiple adjacent sites. In Virginia, mark recapture monitoring has been done intermittently in a cluster of sites for over 32 years. To determine the prevalence of movement among sites for bog turtles, I examined the dataset for all instances of turtles found at sites different from their last capture. I calculated the straight-line distance for each recorded movement. I also examined the sex of the turtle to test whether sex influences movement the frequency and distance of movements. For a subset of movements, I calculated least-cost pathways to identify possible barriers to movement using a previously published resistance model. I found 21 instances where a turtle was caught at a different site than its last capture over 32 years of monitoring. Neither sex was more likely to move farther than the other. Although the study's observed rate of movement may appear low, it is likely an underestimate when detection and asymmetric sampling are taken into account. The least cost pathways analysis suggested that roads or driveways were likely crossed for a significant portion of movement events. Finally, to examine how movement may be affecting the current distribution of bog turtles, I described a method to test whether adjacency to known populations influences the probability of a new site being occupied by turtles. I prove the utility of the method by applying it to a map of bog turtle occurrences collected over this study and show that it can account for habitat differences and barriers to movement between sites as well. In spite of plausibility of the method, limitations in how occurrence data are currently collected prevent its immediate application. Together, this thesis will help managers not only find and assess wetlands on the landscape, it will also provide information about the network of connected patches on the landscape. Knowing where bog turtles are and what wetlands or sub-populations are potentially connected will allowed for a more directed and informed regional management strategy. / Master of Science / Freshwater turtles are facing population declines worldwide, and the bog turtle Glyptemys muhlenbergii is among the most imperiled in North America. Bog turtles occupy naturally small, specialized wetlands called Appalachian Mountain fens. The prevalence of fens on the landscape has declined over recent decades due to agricultural practices. Although there is a strong need to document and monitor bog turtle populations due to their threatened status, bog turtles are difficult to find due to their small size and ability to burrow completely into substrate. Thus, considerable effort must be expended to find populations and track their status. The first overall objective of this thesis was to assess methods for locating populations of bog turtles. My first chapter tests a habitat distribution model that uses publicly available landscape data such as topopgraphy and land cover to predict areas likely to contain turtles. To do this, I systematically surveyed 49 predicted sites multiple times each over 2 years. Simultaneously, I recorded variables such as the time of year, size of the wetland and the weather to determine whether any factor significantly explained the ability to find turtles on any given survey. In addition, I was able to record several variables relating to wetland quality and isolation that were not in the initial model. I found that larger wetlands were easier to search than smaller wetlands, possibly due to larger sites having more turtles, and that wetlands near more impermeable surfaces (such as roads and buildings) were less likely to have bog turtles. As another potential method to find bog turtles and assess sites, we tested the use of 'pristine indicator' plants as a metric for potential wetlands. Mountain fens have specific attributes, such as high groundwater influence and exposure to a large amount of sunlight. Several species, including the bog turtle, are specialized to these factors and are rarely found in the surrounding landscape. Because a distinct community exists for mountain fens in this region, sites with a higher diversity of fen specialist plants may be indicative of a higher quality site which can support more specialists, including the bog turtle. My second chapter tests this hypothesis. I first chose a list of species that would most likely have habitat requirements similar to those of bog turtles. Then, at 12 sites I documented every plant species I encountered within the wetland. I compared the plant community as a whole between bog turtle-occupied and unoccupied sites and found no significant difference between the two. When I narrowed my analysis to focus on plants I previously identified as sharing habitat requirements with the bog turtle, I found a strong pattern of their co-occurrence with bog turtles. This lends support to the idea that these 'pristine indicator' plants could be used as a tool to better evaluate sites that may have bog turtles. My last chapter investigates movement of bog turtles in a landscape impacted by human development. Movement of turtles between adjacent wetlands is critical to maintaining long term regional viability of the species, as it lets turtles colonize new sites and exchange genes. Despite the importance of these movements, records of turtles moving long distances between two wetlands is scarce in the literature, likely due to the lack of long-term studies for areas with multiple adjacent wetlands. One method of recording movements is by marking turtles with a unique ID and recording where it was encountered as wetlands are surveyed on the landscape. In Virginia, this procedure has been conducted at multiple sites over 32 years. To understand the prevalence of movement between sites for this species, I examined this dataset and examined all instances of a turtle being found at a site different from its last capture. I recorded the straight-line distance moved for each recorded movement as well as the sex of the turtle, to test if either sex was more or less likely to undertake these movements. Then, for a subset of movements, I calculated least-cost pathways, a metric that accounts for landscape features and plots the easiest route for turtles to move. This way, I could evaluate the prevalence of barriers to movement, such as roads or development, on the landscape. I found 21 documented movements among sites over 32 years of monitoring. Neither sex was more likely to move further than the other. Compared to studies looking at other freshwater turtles, the observed rate of movement appeared low, but this was likely an underestimate due to the difficulty of capturing specific individuals. I also found evidence of significant barriers to movement in 13 out of 17 evaluated least-costs paths, usually roads or driveways. Finally, to examine how movement affects bog turtle distribution, I describe a methodology of testing if adjacency to known populations influences the probability of a new site being occupied by turtles. I demonstrate the plausibility of the method by applying it to a map of occurrences collected over this study and show that it can account for habitat differences and barriers to movement between sites as well. However, limitations in my sampling scheme limit conclusions from my dataset. Together, these findings will help future managers find where turtles are and which sites may be connected. These results will help managers make more informed decisions for managing bog turtles at a statewide level.

Pathogen contamination, typically quantified by elevated levels of fecal indicator bacteria (FIB), remains the leading cause of surface water-quality impairments in the United States. Continuous watershed-scale models are typically employed to facilitate Total Maximum Daily Load (TMDL) restoration efforts. Due to limited understanding of microbial fate and transport, predictions of FIB concentrations are associated with considerable uncertainty relative to other water-quality contaminants. By focusing on a data-rich instrumented urban watershed, this study aims to improve understanding of microbial fate and transport processes. Weekly FIB concentrations in both the water column and streambed sediments were monitored for one year, and statistical correlations with hydrometeorological and physicochemical variables were identified. An intensive six storm intra-sampling campaign quantified and contrasted loading trends of both traditional regulatory FIB and emerging Microbial Source Tracking (MST) markers. Together, these intensive monitoring efforts facilitated evaluation of the impacts of bacteria-sediment interactions on the predictions of daily FIB concentrations in Hydrological Simulation Program-Fortran (HSPF) over multiple years. While superior overall model performance was demonstrated as compared to earlier efforts, the inclusion of bacteria-sediment interactions did not improve performance. Large wet-weather microbial loading appears to have dwarfed the effects of FIB release and resuspension from sediment. Although wet-weather loading is generally considered as a primary source of waterbody microbial loads, dry-weather periods are more directly associated with public health concern, which may be a more suitable area for future model-refinement efforts. Site evaluation is critical to determine whether the added model complexity and effort associated with partitioning phases of FIB can be sufficiently offset by gains in predictive capacity. Finally, a stochastic framework to translate simulated daily FIB concentrations into estimates of human illness risks is presented that can be can be readily integrated into existing TMDLs. As even small concentrations of FIB from human sources are associated with great risk, and monitoring efforts indicated moderate/high levels of human-associated MST marker in this watershed, remediation efforts to protect public health would be best directed toward infrastructure improvements. Uncertainty analysis indicates more site-specific knowledge of pathogen presence and densities would best improve the estimation of illness risks. / Ph. D.

E. coli and Enterococcus were used to determine the fate and survival of fecal indicator bacteria (FIB) in sand and sediments. The microbial source tracking (MST) methods antibiotic resistance analysis (ARA), Bacteroides human-specific primer test, and fluorometry were compared against the FIBs to determine how reliable each method was in detecting the presence of human fecal contamination. Two phases (Summer 2009 and 2010) were evaluated based on the type of contamination event. A combined sewage overflow (CSO) event was simulated in Phase I, where large amounts of influent were added to sand and bay water columns over 1 to 4 days. In 2010, a low volume sewage leak was simulated in which smaller doses of influent were added to sand and bay water columns over a period of 5 to 15 days. Within each of the phases, both non- and re-circulated columns were also evaluated. Evaluation of FIB survival indicated that Enterococcus was able to stabilize and re-grow in the water and at the sediment/water interface within the Phase I non-circulated columns. E. coli was unable to re-grow and/or stabilize within any environment. Comparisons between the ARA and the FIBs revealed a large majority of isolates identified as coming from either bird or wildlife sources. Human sources were identified but at much lower concentrations than expected. Bacteroides results indicated strong relationships between the increase of FIB concentrations and the presence of the human-specific Bacteroides. Fluorometry results did not indicate any relationship with the FIBs. Unexpectedly, fluorometry readings increased as time progressed indicating that another compound was present that fluoresced at the same wavelength as optical brighteners (OBs). This project was one of the first to study the differences related to two different pollution events (CSO vs. sewage leak) while also evaluating what happens to pollution as it settles into the sediment. It was also unique because it compared bacterial (ARA), molecular (Bacteroides), and chemical (fluorometry) MST methods. / Master of Science

Between 1970 and 2010 almost one-third of drinking water related waterborne disease outbreaks reported to the US Centers for Disease Control and Prevention were associated with systems dependent on untreated groundwater (i.e., most commonly, household wells). This is unsurprising, given that numerous past efforts to monitor household well water quality have indicated a high prevalence of fecal coliforms and/or E. coli at the point of use. Non-point sources of pollution, including septic tank leakages and poorly constructed drain fields, have been identified as the leading risk factors associated with outbreaks in households dependent on groundwater. Ideally, the integration of emerging source tracking (ST) analyses in well monitoring programs could be used to identify whether the presence of fecal indicator bacteria (FIB) is associated with human or non-human sources in order to inform remediation strategies. However, the application of ST to groundwater has been limited, and the interpretation of data is consequently difficult. This research compares the fate and transport of FIB (E. coli and enterococci) with a chemical (optical brighteners, OB) and a molecular (Bacteroides HF183) ST target in order to evaluate their potential use as indicators of water quality issues in private drinking water systems. Eighteen PVC soil columns were constructed in an outdoor soil column facility to represent small-scale septic drainfield models; they received synchronized doses of primary-treated wastewater twice daily and were monitored bi-weekly over a 7-month period. Columns were subject to variable influent loading rates of wastewater effluent, and differing degrees of soil compromisation (i.e. synthetic solution channels). Results show that while column effluent volume and constituent levels were related to dosage, they were not always related to soil compromisation (ANOVA, p < 0.05). E. coli and enterococci concentrations were associated with effluent volume and OB levels (Spearman's rank, p < 0.05). The presence of Bacteroides HF183 was not strongly associated with the other measured ST target levels (Point-biserial correlation, p < 0.05). Findings from this study suggest surface water ST methodologies may have a role in groundwater quality monitoring efforts. Quantifying the relative recovery of ST targets and FIB from controlled groundwater simulations will assist in the development of strategies to identify non-point sources of human wastewater pollution efficiently and effectively to inform remediation. / Master of Science

This exploratory study investigated the relationships between community college students' Myers-Briggs typology preferences and their problem-solving skills. The literature provides reason to believe that students' MBTI preferences are related to problem-solving style and ability. The educator's ability to teach problem-solving will be enhanced through an identification of motivational patterns affecting learning. A sample of 577 community college students participated in the study. Achievement Level for each student was identified using the New Jersey College Basic Skills Placement Test. In addition, problem-solving ability was measured using the New Jersey Test of Reasoning Skills. The Myers-Briggs Type Indicator, Form F, provided the MBTI profile scores. Chi Square analyses, correlations, and stepwise regression techniques were employed to identify and test relationships. The best identified predictor was the student's preference on the Sensing/Intuition index. Preference on the Judgment/Perception index also proved to be significant. Students who have the least skills in mathematics problem-solving have stronger preferences in the direction of the sensing dimension and the judgment dimension. Implications and recommendations for classroom instruction were presented and recommendations for related research were suggested. / Ed. D.

Corporate crisis events such as cyber attacks, executive scandals, facility accidents, fraud, and product recalls can damage customer trust and firm reputation severely, which may lead to tremendous loss in sales and firm equity value. My research aims to integrate information available on the market to assist firms in tackling crisis events, and to provide insight for better decision making. We first study the impact of crisis events on firm performance. We build a hybrid deep learning model that utilizes information from financial news, social media, and historical stock prices to predict firm stock performance during firm crisis events. We develop new methodologies that can extract, select, and represent useful features from textual data. Our hybrid deep learning model achieves 68.8% prediction accuracy for firm stock movements. Furthermore, we explore the underlying mechanisms behind how stakeholders adopt and propagate event information on social media, as well as how this would impact firm stock movements during such events. We adopt an extended epidemiology model, SEIZ, to simulate the information propagation on social media during a crisis. The SEIZ model classifies people into four states (susceptible, exposed, infected, and skeptical). By modeling the propagation of firm-initiated information and user-initiated information on Twitter, we simulate the dynamic process of Twitter stakeholders transforming from one state to another. Based on the modeling results, we quantitatively measure how stakeholders adopt firm crisis information on Twitter over time. We then empirically evaluate the impact of different information adoption processes on firm stock performance. We observe that investors often react very positively when a higher portion of stakeholders adopt the firm-initiated information on Twitter, and negatively when a higher portion of stakeholders adopt user-initiated information. Additionally, we try to identify features that can indicate the firm stock movement during corporate events. We adopt Layer-wised Relevance Propagation (LRP) to extract language features that can be the predictive variables for stock surge and stock plunge. Based on our trained hybrid deep learning model, we generate relevance scores for language features in news titles and tweets, which can indicate the amount of contributions these features made to the final predictions of stock surge and plunge. / Doctor of Philosophy / Corporate crisis events such as cyber attacks, executive scandals, facility accidents, fraud, and product recalls can damage customer trust and firm reputation severely, which may lead to tremendous loss in sales and firm equity value. My research aims to integrate information available on the market to assist firms in tackling crisis events and providing insight for better decision making. We first study the impact of crisis events on firm performance. We investigate five types of crisis events for SandP 500 companies, with 14,982 related news titles and 4.3 million relevant tweets. We build an event-driven hybrid deep learning model that utilizes information from financial news, social media, and historical stock prices to predict firm stock performance during firm crisis events. Furthermore, we explore how stakeholders adopt and propagate event information on social media, as well as how this would impact firm stock movements during the events. Social media has become an increasingly important channel for corporate crisis management. However, little is known on how crisis information propagates on social media. We observe that investors often react very positively when a higher portion of stakeholders adopt the firm-initiated information on Twitter, and negatively when a higher portion of stakeholders adopt user-initiated information. In addition, we find that the language used in the crisis news and social media discussions can have surprising predictive power on the firm stock. Thus, we develop a methodology to identify the importance of text features associated with firm performance during crisis events, such as predictive words or phrases.

This study investigated the similarities between students who participated in the Career Interest Testing Groups at the Virginia Tech University Counseling Center and undecided students in general. Holland's Theory of Congruence was tested by examining the student's SCII and MBTI test results and doing a follow-up regarding achievement,persistence in school, changes of major and compatibility with final choice of major. The undecided students in this study resembled those in the literature on only two dimensions; having a low rate of graduation and a high incidence of personal problems. Holland“s Theory of Congruence did not hold up with this group. A scoring system, which indicated the degree of compatibility between the choices of major and test results, was used and indicated no movement toward greater congruence as the students changed majors. For those who did graduate in highly congruent majors, better grades did not result. The conclusion was that undecided students at a select admissions university represent the more academically able and mature individuals among undecided students in general. As the literature predicted, this group did have a high drop out rate. The findings regarding Holland's Theory of Congruence have been mixed and the theory did not hold in this investigation. / Ed. D. / incomplete_metadata

The purpose of this study was to examine the relationships between problem solving style as measured by the Myers Briggs Type Inventory (MBTI) and achievement in College Chemistry. It was postulated that preferences measured by the MBTI would increase the variance explained by the predictive tool currently in use. The sample population was taken from a suburban community college. It was found that problem solving style does increase the variance explained by the currently used predictive tool. There is a non-simplistic relationship between problem solving style as measured by the MBTI and achievement in College Chemistry. The problem solving characteristics of successful and unsuccessful students, as well as those who withdraw, were documented. Some of the relationships found were the same as those reported in MBTI literature; others were contrary to expectations based on the MBTI literature. / Ph. D.

Yes / The 17 Sustainable Development Goals (SDGs) bring together environmental, social and economic concerns. They therefore have the potential to move society away from the dominant model of prosperity as purely economic toward a more holistic and ‘sustainable’ prosperity. But, the success of such a transformative agenda rests on its implementation. At the heart of planned implementation of the SDGs is a set of 230 indicators. Indicators have been strongly critiqued in a range of literatures. However, in the context of the SDGs, indicators have been described as ‘essential’ with little critical assessment of their role in implementation. Therefore, this chapter aims to provide this critical voice. To do this, the chapter reviews critiques of indicators from sustainability science, anthropology and sociology and provides illustrative cases of indicators implementation. From this review we are able to draw lessons for the use of indicators in SDG implementation. Specifically, the chapter argues that indicators are reductionist and struggle with contested concepts. Nevertheless, by making the operationalisation of concepts visible and enabling quantified analysis, indicators can have a useful role in SDG implementation. However, this requires that indicator critiques are taken seriously and inform indicator use. / ESRC Research Centre for the Understanding of Sustainable Prosperity, Grant Number ES/M010163/1