The field of education relies heavily on instructional coaches to build teacher capacity in the implementation of evidence-based practices (EBPs) with fidelity. Although observation tools are used to measure the fidelity of implementation by teachers, less is reported about specific behaviors demonstrated by a coach. This two-part nonexperimental study used primary and secondary data. It sought to develop a valid and reliable Coaching Observation Tool, and used it to analyze 36 recorded real-time coaching sessions supporting the implementation of an EBP, Targeted Reading Intervention (TRI). The tool was developed using an iterative process of initial coach interview and systematic review of the literature, review of a sample of recorded coaching sessions with the initial draft of the tool, and focus group member checking interview with coaches. Next, the tool was used to analyze a sample of recorded TRI coaching sessions. The coaches in the present study provided coaching to teachers during year 2 of a TRI multi-site randomized controlled trial study. Although the tool was developed and used to identify the frequency with which discrete coaching behaviors were used, the current tool did not demonstrate validity and reliability. The findings suggest this tool could be helpful to identify coaching practices to support the implementation of EBP, such as TRI. Researchers using coaching to support the implementation of EBP alone, or as a component within PD, will find this tool provides them a clearer understanding of the instructional coach in building teacher capacity with the fidelity of implementation of the EBP.
Organizational change is an initiative to transition from current state to a desired future state, where the initiatives can be either planned or unplanned based on the motivational factors. This study evaluates the impact of organizational characteristics and change management strategies adopted by facility management (FM) professionals to implement planned and unplanned changes due to internal as well as external factors. The implementation of new or innovative workplace strategies (flexible workspace) were considered as planned changes, whereas changes implemented at workplace as a response to the COVID-19 pandemic were considered as unplanned changes (e.g., remote working, safety protocols, etc.). The research team adopted a survey-based methodology to collect information on planned and unplanned change management experiences from FM professionals across the world. Through the help of an FM association, a total of 800+ responses were recorded from 60 different countries. Using machine learning algorithm, the research team was able to identify the impact of key organizational characteristics and change management strategies responsible for the successful planned or unplanned change initiatives. Most of the models were tested on 30% of the randomly selected data and recorded predictive accuracies as high as 85%.
Trucking industry thrives on just-in time management, efficient routing and less travel delays. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel cause loss of revenue to the trucking companies. Truck travel time performance measures assist in understanding the level of “truck-exclusive” congestion to plan for better routing. The truck travel times and routing strategies depend on the on-network (road) characteristics and off-network (land use and demographics) characteristics within the vicinity of roads. The literature documents limited to no research dedicated to truck travel time performance measures or their association with on-network and off-network characteristics.
The main goal of this dissertation is to research truck travel patterns, recommend performance measures, identify chokepoints, and understand the influence of on-/off-network characteristics on truck congestion. The first part of the research focuses on examining truck travel time data to choose performance measures, and understand their relationship with on-network and off-network characteristics. These performance measures are visualized geospatially to locate the chokepoints. The second part of the research focuses on the truck travel time estimation models using the on-network and off-network characteristics as the independent variables. The methodology and findings assist in locating chokepoints and prioritizing areas for truck travel improvement. The models help to estimate truck travel times and proactively plan land use or transportation network improvements.
Foreign aid is one of the most powerful tools at a singe state’s disposal. Economically, it can provide much needed support to the poorest of countries or those facing catastrophic conditions. Politically, it can strengthen the current government’s position and provide them with what they deem necessary to keeping their rule of law, whether they be democracies or autocracies. In addition to these effects, a donor state must also balance their own interests, which can sometimes conflict with a recipient. An alternative route that can potentially help a donor avoid such dilemmas is to divert funds through a multilateral organization instead, which can help donors avoid the need to go through another state government. Regardless of the method of disbursement though, the use of financial foreign aid has a tendency to produce effects that go far beyond its initial goal. This tendency is what this study seeks to examine. Within this context, this study seeks to empirically investigate the unintended impact that foreign aid may have on one of the world’s biggest human rights abuse: human trafficking. Human trafficking is a generations-old problem still in search of a solution. In two parts, I use an ordinal logistic regression to examine historical data in a post-Cold War era to determine the extent of foreign aid’s unintended impact on a crime yet undefined. In the first part, total financial foreign aid is examined for general impact. In the second part, financial foreign aid is split into its most common forms, bilateral aid and multilateral aid, to examine specific individualized impacts. Overall, my study reveals that there is indeed an impact on human trafficking, even though it had yet to be clearly defined at the time . In addition to that, bilateral aid experienced more statistical significance as compared to multilateral aid, suggesting that bilateral aid may have had a bigger part to play in the realm of human trafficking. The magnitude and type of relationship that foreign aid has with human trafficking appears to change over time. However, this study does have its limitations, which make the interpretation of the results a cautious act. With these facts in mind, policymakers are faced with a multi-faceted dilemma in need of fine-tuning.
Culturally diverse college students often have high rates of addictive disorders, yet tend to have lower rates of treatment participation and completion. Much of this is due to the lack of culturally relevant practices and treatment. Collegiate Recovery Programs have been established over time to serve college students in a capacity that reinforces a lifestyle of recovery from substance use and addictive behaviors. This study examines nationwide enrollment and demographic data collected from the 133 Collegiate Recovery Programs operating in the United States. While the 133 Collegiate Recovery Programs are spread across the United States, North Carolina has a number of unique characteristics which separates it from the remaining states. The state of North Carolina was the first state to use public funds to support collegiate recovery. There are currently nine CRPs established at universities within the system, including the sole Collegiate Recovery program operating in a Historically Black College. This study provides more focused analysis of how collegiate recovery program operate in in North Carolina, with a focus on the differences between the collegiate recovery program at a Historically Black College and University (HBCU) and Predominantly White Institutions (PWIs). Overall results indicated that most recovery programs are housed primarily within campus Student Health and Wellness Services. This study’s findings demonstrate that HBCU environment may differ by more often coordinating campus wide participation for recovery events. The implications of integrating the larger community to recovery services allows for greater participation from allies and advocates. This study advances the research in collegiate recovery and provides insight to practice for coordinators, counselors, administrators, and researchers.
In the United States, Black women often face a number of disparities due to historical systems of oppression, social determinants of health and intersecting aspects related to gender and race (Lewis et al., 2016; Thomas et al., 2011; Spates et al., 2020). These factors may affect aspects of physical, mental and spiritual health, thus impacting overall quality of life and wellness outcomes. Wellness is defined as an integrated multidimensional construct (Myers & Sweeney, 2000). Tenets of the theory of intersectionality also apply an integrated framework addressing the unique contributions of intersected identities in the lives of Black women (Crenshaw, 1999). Many bodies of work outline the detrimental effects of systematic oppression and institutional racism on specific aspects of mental health, health and well-being of minoritized populations. However, there is little research focusing on the intersectional experiences of Black women in relation to gendered racism, race-related stress socioeconomic status (SES) and its impacts on total wellness factors. In this study, a non-experimental correlational research design was used with a standard multiple regression to explore relationships between gendered racism, race-related stress, SES and wellness scores amongst Black women. A total of 471women across the U.S. completed an online survey consisting of a demographic questionnaire and three measurements: The Gendered Racial Microaggression Scale for Black Women, Index of Race-Related Stress-Brief and the Five Factor Wellness Inventory. A standard multiple regression analysis indicated that more gendered racial microaggression on certain domains (Assumption of Beauty and Sexual Objectification, Silenced and Marginalized, Angry Black Woman) were associated with higher wellness scores, but other domains (Strong Black Woman) were not. Additionally, higher scores on race-related stress and the lowest SES status group were associated with lower overall wellness scores. Findings from this study highlight the need and importance of examining the intersections of race and gender and their impacts on the lived experiences, health and wellbeing of Black women. Recommendations for future research are provided along with implications for counseling practice and instruction.
Nature walks have been demonstrated to increase cognitive and emotional well-being by restoring attention and increasing positive affect, both of which are linked to increases in reflective (“broadened") thinking. Broadened thinking is contrasted to the narrowing of thoughts associated with scarcity, the feeling of not having enough resources. This study proposed a model outlining the process by which broadened thinking occurs during nature walks while also incorporating scarcity. One hundred sixty-five college students reporting varying levels of scarcity took at 30-minute outdoor walk. Structural equation modeling demonstrated that the proposed model was a good fit for the data, supporting the hypothesized links between nature, restoration, positive affect, and broadened thinking. Although scarcity did not moderate relationships as expected, ANOVAs showed that participants experiencing the highest time scarcity saw the greatest increases in restoration and broadened thinking, providing some support for the hypothesis that those with more scarcity would derive greater benefit from nature walks. This study demonstrates the effectiveness of nature walks as an intervention, especially for students pressed for time, and highlights the importance of cultivating walk environments that are safe and accessible for all. Implications for future research and interventions at the individual and societal level are discussed.
In this thesis, we hypothesize that data visualization users are subject to systematic errors, or cognitive biases, in decision-making under uncertainty. Based on research from psychology, behavioral economics, and cognitive science, we design five experiments to measure the role of anchoring bias, confirmation bias, and myopic loss aversion under different uncertain decision tasks like social media event detection, misinformation identification, and financial portfolio allocation. This thesis makes three major contributions. First, we find evidence of cognitive biases in data visualization through multiple behavioral trace data including user decisions, interactions logs (hovers, clicks), qualitative feedback, and belief elicitation techniques. Second, we design five digital experiments with interactive data visualization systems across different design complexities (coordinated multiple views to single plot) and data types (social network, linguistic, geospatial, temporal, statistical) and evaluate them on user populations that range from novice to expert (crowdsourced, undergraduate, data scientist, domain expert). Third, we evaluate the experiments using statistical, probabilistic, and machine learning techniques to measure the effects of cognitive biases with mixed effects modeling, hierarchical clustering, natural language processing, and Bayesian cognitive modeling. These experiments show the promising role data visualizations and human-computer techniques could remediate such biases and lead to better decision-making under uncertainty.
Learning Analytics (LA) has had a growing interest by academics, researchers, and administrators motivated by the use of data to identify and intervene with students at risk of underperformance or discontinuation. Typically, faculty leadership and advisors use data sources hosted on different institutional databases to advise their students for better performance in their academic life. Although academic advising has been critical for the learning process and the success of students, it is one of the most overlooked aspects of academic support systems. Most LA systems provide technical support to academic advisors with descriptive statistics and aggregate analytics about students' groups. Therefore, one of the demanding tasks in academic support systems is facilitating the advisors' awareness and sensemaking of students at the individual level. This enables them to make rational, informed decisions and advise their students. To facilitate the advisors' sensemaking of individual students, large volumes of student data need to be presented effectively and efficiently.
Effective presentation of data and analytic results for sensemaking and decision-making has been a major issue when dealing with large volumes of data in LA. Typically, the students' data is presented in dashboard interfaces using various kinds of visualizations like scientific charts and graphs. From a human-centered computing perspective, the user’s interpretation of such visualizations is a critical challenge to design for, with empirical evidence already showing that ‘usable’ visualizations are not necessarily effective and efficient from a learning perspective. Since an advisor's interpretation of the visualized data is fundamentally the construction of a narrative about student progress, this dissertation draws on the growing body of work in LA sensemaking, data storytelling, creative storytelling, and explainable artificial intelligence as the inspiration for the development of FIRST, Finding Interesting stoRies about STudents, that supports advisors in understanding the context of each student when making recommendations in an advising session. FIRST is an intelligible interactive interface built to promote the advisors' sensemaking of students' data at the individual level. It combines interactive storytelling and aggregate analytics of student data. It presents the student's data through natural language stories that are automatically generated and updated in coordination with the results of the aggregate analytics. In contrast to many LA systems designed to support student awareness of their performance or to support teachers in understanding the students' performance in their courses, FIRST is designed to support advisors and higher education leadership in making sense of students' success and risk in their degree programs. The approach to interactive sensemaking has five main stages: (i) Student temporal data Model, (ii) Domain experts’ questions and queries, (iii) Student data reasoning, (iv) Student storytelling model, and (v) Domain experts’ reflection. The student storytelling stage is the main component of the sensemaking model and it composes four tasks: (i) Data sources, (ii) Story synthesis, (iii) Story analysis, and (iv) User interaction.
The contributions of this study are: i) A novel student storytelling model to facilitate the sensemaking of complex, diverse, and heterogeneous student data, ii) An anomaly detection model to enrich student stories with interesting, yet, insightful information for the domain experts and iii) An explainable and interpretable interactive LA model to inspire advisors' trust and confidence with the student stories. This study reports on four ethnographic studies to show the potential of the proposed LA sensemaking model and how it affects the advisor's sensemaking of students at the individual level. The user studies considered for this dissertation were focus group discussions, in-depth interviews, and diary study- in-situ and snippet technique. These studies investigate if FIRST can improve and facilitate the advisor's sensemaking of students’ success or risk by presenting individual student's heterogeneous data as a complete and comprehensive story.
While continuous improvement initiatives such as benchmarking have a history of utilization for general business objectives, their successful utilization in the built environment industries, such as construction and facilities management is not nearly as well documented or researched. This project identifies how the built environment fields are using continual improvement initiatives, evaluates how effectively these initiatives are being utilized, and identifies critical success factors for improving and leveraging these techniques to achieve the sustained continuous improvement initiatives that will be necessary to meet long -term sustainability goals in relation to the operations of the built environment. This project takes place in three parts; a case study of a novel way to benchmark and identify areas for improvement, a large-scale survey of how facility managers are using benchmarking and their involvement in benchmarking networks, and an analysis of the relationship of organizational learning culture and the role that it plays in facilitating and supporting benchmarking initiatives. This research provides the first-of its-kind survey and assessment of how practitioners in the built environment are utilizing benchmarking. The results of this project serve to assist facility practitioners in developing, leveraging, and strengthening their continuous improvement initiatives to sustain ongoing change critical for the success of long-term organizational goals related to the built environment lifecycle.
