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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.
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.
The prediction of RNA secondary structure is complex. The biomolecule can adopt or sample numerous stable conformations, can change structure in response to a stimulus such as a binding event, and does not simply obey thermodynamically favorable folding rules. Due to this, estimation of structure based on the primary sequence is unreliable and misleading. Probing RNA structure dramatically improves computational prediction. The examination of both, ex vivo and in cell RNA can provide important information regarding structural stability, the RNA interactome, and refolding effects.
Current structural probes for RNA selective 2′-hydroxy acylation analyzed by primer extension (SHAPE) rely on a reaction with the 2′-OH on the ribose sugar of residues that are not base paired. These flexible residues can then be determined using gel electrophoresis or quantified using next generation sequencing (NGS) and mutational profiling (MaP) to prepare a library of probing data. The advent of SHAPE technologies led to a rapid increase in the accessibility of RNA structural data. Several successful SHAPE probes have been previously demonstrated, but arguments regarding reactivity and cell permeability remain. In this work, the design and application of novel, variably reactive SHAPE probes is shown ex vivo and a novel in cell probe is demonstrated.
Students with extensive support needs (ESN) are a heterogenous group of students with the most pervasive and ongoing support needs who typically receive special education services under the categories of autism spectrum disorder (ASD), intellectual disability, or multiple disabilities and often qualify to take their state’s alternative assessment (Taub et al., 2017). Students with ASD who have ESN may have elevated support needs for social behavior (Jang et al., 2011; Matson et al., 2011; Shogren et al., 2017). Although there are several evidence-based practices to support the behavioral needs of students with ASD who have ESN (Steinbrenner et al., 2020), educators often have difficulty implementing these practices with fidelity (Brock et al., 2014; Morrier et al., 2011; Robertson et al., 2020). School-wide Positive Behavioral Interventions and Supports (SWPBIS) is an evidence-based framework to support the social and behavioral needs of all students with evidence-based practices, data-based decision making, and systems to support teacher implementation fidelity (Horner & Sugai, 2015; Sugai & Horner, 2006, 2009). However, students with ASD who have ESN are not consistently included in SWPBIS (Kurth & Enyart, 2016; Kurth & Zagona, 2018; Walker et al., 2018). Check-in/Check-out (CICO) is an evidence-based intervention commonly used as a Tier 2 behavioral support within a SWPBIS framework (Conley et al., 2018; Maggin et al., 2015). CICO is effective for K-12 students without disabilities and students with high incidence disabilities (Maggin et al., 2015). The purpose of this study was to examine the effects of traditional or adapted CICO on the adherence to schoolwide expectations and challenging behavior of students with ASD who have ESN. Results of this single-case, multiple baseline across participants study indicated there was a decrease in challenging behavior for two of the four participants when adaptations were made to the standard CICO protocol. Additionally, educators, students, and parents found CICO feasible and socially valid. Limitations, implications for practice, and suggestions for future research are discussed.
In this thesis, a combined approach based on the Finite Element (FE) and Smoothed Particle Hydrodynamics (SPH) methods is proposed to model turning operations. The approach exploits the advantages of each method and leads to high-fidelity coupled FE-SPH machining models that are significantly more numerically efficient and are on par with the models based on each of the two methods alone. Both two-dimensional and three-dimensional models are developed and validated by comparing predicted forces and chip morphologies with experimental results. Parametric studies are carried out to fine-tune the model-based parameters in order to avoid numerical stability issues. The three-dimensional models are extended to include modulated tool path (MTP) machining which is a technique for breaking chips during machining by modulating the motion of the tool. The MTP model predictions are shown to agree with the results from an existing analytical model. With this model, various tool paths can be simulated to choose an optimal path that decreases tool-wear without sacrificing productivity. Preliminary results from a three-dimensional turning model incorporating machining dynamics through a spring-damper system are also presented. This model has the potential to be used for studying machining stability for a given set of machining conditions.
In addition to the above, another significant contribution of this thesis is the determination of Johnson-Cook material model parameters for a given material using an inverse method and experimental values of cutting forces and workpiece temperatures. The methodology described in the present work identifies the non-uniqueness of the solution to the inverse problem and proposes an approach that eliminates the non-uniqueness.
Athletes are aware that with involvement in sport they are exposed to the risk of getting injured. Suffering an injury can be one of the most stressful experiences in a student-athlete’s athletic career and can cause a series of psychological, emotional, and social responses, as well as impact one’s sense of identity. The very sparse literature in the counseling field regarding student-athletes and lack of research in general, exploring women student-athletes and women track and field student-athletes in particular, contributes to the need for this study. The purpose of this study was to explore lived experiences of former women track and field college student-athletes who trained and competed through pain and injury. This study utilized a phenomenological approach and implemented semi-structured interviews. Over the course of a six-week period, a total of 10 participants completed a demographic questionnaire and were interviewed via Zoom to facilitate in-depth descriptions of their experiences. Moustakas (1994) methods consistent with qualitative phenomenological research design were used to facilitate the data analysis. A total of five major themes emerged from the data, including: identity, perception of pain and injury, student-athlete - coach relationship, support system, and psychological impact. This research found that the themes are interconnected and impact each other. The findings indicate that women track and field student-athletes who chose to train and compete through pain and injuries face identity challenges, which are further facilitated by student-athlete – coach relationship, one’s support system, and acceptance of the “push through the pain” mindset. This mindset was found to be further facilitated by the underlying belief that student-athlete role is a job for which participants have been compensated. Participants were also found to minimize and justify their pain as a coping mechanism to help them in continuing to train and compete despite being in pain and injured. The relationship between participants and their coaches was found to contribute to negative psychological experiences. All themes were closely connected with cognitive and emotional functioning of the participants. Implications for counselors and counselor educators as well as future research recommendations are discussed. However, the emphasis for counselors is to approach working with student-athletes from a holistic standpoint, disclose personal experiences with athletics early on in the therapeutic relationship, and provide substantial psychoeducation regarding intercorrelation between mental health and athletic performance.
The main goal of learning analytics and early detection systems is to extract knowledge from student data to understand students' trends of activities towards success and risk and, therefore, design intervention methods to improve learning performance and experience. However, many factors contribute to the challenge of designing and building effective learning analytics systems. Because of the complexity of heterogeneous student data, models designed to analyze it frequently neglect temporal correlations in the interest of convenience. Moreover, the performance descriptions gained from the student data model or prediction results from the analytical models do not always help explain the "why" and "how" behind it. Furthermore, domain specialists are unable to participate in the knowledge discovery process since it necessitates significant data science abilities, and an analytical model is a black box to them.
This research aims to develop analytical models that enable domain experts to study their students' performance behavior and explore trustworthy sources of information with the help of explanations on the analytics. Our work demonstrates various approaches to using the temporal aspect of heterogeneous student data to build analytical models: weighted network analysis, unsupervised cluster analysis, and recurrent neural network analytics. The description, implementation process, and findings of each method are presented as technical contributions to the temporal analysis of student data. We experiment with all these analytical models that highlight the complexity of heterogeneous-temporal data, model building, decision-making tasks, and the need for a more in-depth focus on visual information of analytics with state-of-art explainable AI tools and techniques.
Our work underscores a need for developing a robust way to integrate the possibilities inherent within each approach. To achieve this goal, we present a comprehensive yet flexible and empirical framework to support the design and development of analytical models to extract meaningful insights about students' academic performance and identify early actionable interventions to improve the learning experience. We illustrate our framework on three applications (e.g., student network model, unsupervised clustering model, and recurrent neural network analytics) to demonstrate the value of this framework in addressing the challenges of using student data for learning analytics. These applications present vast opportunities to benefit students' learning experience by implementing flexible educational data representations, fitting different predictive models, and extracting insights for designing prescriptive analytics and building strategies to overcome perceived limitations.
An academic institution's culture drives its ability to accept, leverage, and deploy predictive and prescriptive analytics to enhance the workflow of maximizing pedagogical outcomes. We believe that our work will aid in the future development or refinement of a set of design standards for learning analytics systems.
