Failure is a necessary step in the process of learning. For this reason, there has been a myriad of research dedicated to the study of student perseverance in the presence of failure, leading to several commonly-cited theories and frameworks to characterize productive and unproductive representations of the construct of persistence. While researchers are in agreement that it is important for students to persist when struggling to learn new material, there can be both positive and negative aspects of persistence. What is it, then, that separates productive from unproductive persistence? The purpose of this work is to address this question through the development, extension, and study of data-driven models of student affect, behavior, and knowledge. The increased adoption of computer-based learning platforms in real classrooms has led to unique opportunities to study student learning at both fine levels of granularity and longitudinally at scale. Prior work has leveraged machine learning methods, existing learning theory, and previous education research to explore various aspects of student learning. These include the development of sensor-free detectors that utilize only the student interaction data collected through such learning platforms. Building off of the considerable amount of prior research, this work employs state-of-the-art machine learning methods in conjunction with the large scale granular data collected by computer-based learning platforms in alignment with three goals. First, this work focuses on the development of student models that study learning through the use of advancements in student modeling and deep learning methodologies. Second, this dissertation explores the development of tools that incorporate such models to support teachers in taking action in real classrooms to promote productive approaches to learning. Finally, this work aims to complete the loop in utilizing these detector models to better understand the underlying constructs that are being measured through their application and their connection to productive perseverance and commonly-observed learning outcomes.

This thesis explores the potential impacts of the implementation of best management practices (BMPs) in Upper Roberts Creek (URC) watershed in northeast Iowa as part of the Iowa Nutrient Research Center (INRC). The INRC was formed in response to the United States Environmental Protection Agency (EPA) requirement that the states along the Mississippi River develop and implement strategies for reducing the nutrient load leaving their states and entering the Gulf of Mexico. The impacts of BMP implementation in URC were evaluated through the use of HydroGeoSphere which was used to develop a three dimensional, coupled surface/subsurface model of the watershed. The URC model was used to evaluate the hypothetical impacts of the widespread implementation of cover crops on agricultural land within the watershed, the construction of eight Iowa Conservation Enhancement Reserve Program (CREP) style wetlands, and the combination of these two BMPs. Through the comparison of these simplified, hypothetical scenarios to a baseline condition, potential nitrate load reduction estimates were made for each practice or combination of practices. These estimates indicate that neither of the individual practices would be likely to achieve the nitrogen reductions targeted by the EPA and in order to achieve these goals a combination of practices would likely be required.

<p>Safety assessment is a rational and systematic process for assessing the risk associated with the usage of a product. While the safety assessment process is important even when making a simple product, the true importance of this process comes into light when designing for example an aircraft, where a failure could possibly lead to the loss of human lives. However,even though this process is vital for certain industries, it is plagued by a lack of tools. The existing tools are focused on specific parts of the process and do not make use of work done in earlier steps of the process which often means that the safety engineer needs to manually do work that could have been calculated automatically from information that is already present from an earlier step in the process.</p><p>This thesis shows that by creating a model of the product that can be present and augmented throughout every step in the process, many calculations that are currently done by hand can be automated or semi-automated by examining this shared model. The thesis proposes a specification for a modeling formalism that is simple enough to be used as early as the requirements phase of a project, but powerful enough to provide important information all the way throughout the safety assessment process.</p><p>The thesis also specifically shows how this model can be used to help in the creation and updating process of Failure Mode and Effects Analysis (FMEA) documents as a proof-of concept implementation based on Sörman Information AB’s product “Uptime BPC Standard”.Algorithms for synchronizing between the model and the FMEA representation, as well as algorithms for automatically calculating the next level effect and global level effect of failure modes based on the hierarchy and connections made in the model are also presented.</p><p>The prototype implementation shows that even though the entire safety assessment process cannot be automated it is possible to extract information from the model by analyzing its hierarchy and connections. While more work still needs to be done before the entire safety assessment process can be encompassed, the initial results shows that the proposed modeling formalism allows us to create models from which relevant information that can be used to support the safety assessment process can be calculated.</p>

La connaissance de la structure tridimensionnelle (3D) des protéines est une information capitale. Néanmoins, le nombre de protéines dont la structure 3D a été déterminée expérimentalement est cent fois plus faible que le nombre de protéines connues aujourd'hui. Cet écart ne pourra pas être comblé, car les techniques expérimentales de détermination de structure (diffraction de rayons X et résonance magnétique nucléaire) sont coûteuses et lentes (un an de travail en moyenne pour une seule protéine). Un moyen d'obtenir plus rapidement la structure 3D de protéines est de la prédire par des moyens bioinformatiques. La technique de prédiction la plus précise actuellement est la modélisation par homologie. Celle-ci est basée sur la similitude de structure entre deux protéines de séquences similaires. L'étape critique de cette méthode est l'étape d'alignement entre la séquence à modéliser et une séquence similaire de structure connue. Notre travail a consisté tout d'abord en la conception d'une nouvelle méthode d'alignement pairé très fiable. Cette méthode a ensuite été incluse dans un système automatique de modélisation par homologie: la bonne qualité des structures prédites par le système trouve en partie son origine dans le programme d'alignement utilisé. Enfin, nous avons appliqué notre système de modélisation automatique à la modélisation de toutes les protéines déduites du génome d'une bactérie pathogène étudiée dans notre unité de recherche: Brucella melitensis. Cela nous a conduit à créer une banque de données structurales et fonctionnelles consacrée au génome de cette bactérie. Cette banque de données est devenue un outil de travail indispensable pour plusieurs équipes de recherche européennes qui étudient Brucella melitensis.

As the use of simulation increases across many diff erent application domains, the need for high- fidelity three-dimensional virtual representations of real-world environments has never been greater. This need has driven the research and development of both faster and easier methodologies for creating such representations. In this research, we present two diff erent inference-based geometric modeling techniques that support the automatic construction of complex cluttered environments. The fi rst method we present is a surface reconstruction-based approach that is capable of reconstructing solid models from a point cloud capture of a cluttered environment. Our algorithm is capable of identifying objects of interest amongst a cluttered scene, and reconstructing complete representations of these objects even in the presence of occluded surfaces. This approach incorporates a predictive modeling framework that uses a set of user provided models for prior knowledge, and applies this knowledge to the iterative identifi cation and construction process. Our approach uses a local to global construction process guided by rules for fi tting high quality surface patches obtained from these prior models. We demonstrate the application of this algorithm on several synthetic and real-world datasets containing heavy clutter and occlusion. The second method we present is a generative modeling-based approach that can construct a wide variety of diverse models based on user provided templates. This technique leverages an inference-based construction algorithm for developing solid models from these template objects. This algorithm samples and extracts surface patches from the input models, and develops a Petri net structure that is used by our algorithm for properly fitting these patches in a consistent fashion. Our approach uses this generated structure, along with a defi ned parameterization (either user-defi ned through a simple sketch-based interface or algorithmically de fined through various methods), to automatically construct objects of varying sizes and con figurations. These variations can include arbitrary articulation, and repetition and interchanging of parts sampled from the input models. Finally, we affim our motivation by showing an application of these two approaches. We demonstrate how the constructed environments can be easily used within a physically-based simulation, capable of supporting many diff erent application domains.

The thesis contains two studies: First is the water budget analysis using the groundwater modeling and next is the groundwater modeling using the MCMC scheme. The case study for the water budget analysis was the Norman Landfill site in Oklahoma with a quite complex hydrology. This site contains a wetland that controls the groundwater-surface water interaction. This study reports a simulation study for better understanding of the local water balance at the landfill site using MODFLOW-2000. Inputs to the model are based on local climate, soil, geology, vegetation and seasonal hydrological dynamics of the system to determine the groundwater-surface water interaction, water balance components in various hydrologic reservoirs, and the complexity and seasonality of local/regional hydrological processes. The model involved a transient two- dimensional hydrogeological simulation of the multi-layered aquifer. In the second part of the thesis, a Markov Chain Monte Carlo (MCMC) method were developed to estimate the hydraulic conductivity field conditioned on the measurements of hydraulic conductivity and hydraulic head for saturated flow in randomly heterogeneous porous media. The groundwater modeling approach was found to be efficient in identifying the dominant hydrological processes at the Norman Landfill site including evapotranspiration, recharge, and regional groundwater flow and groundwater-surface water interaction. The MCMC scheme also proved to be a robust tool for the inverse groundwater modeling but its strength depends on the precision of the prior covariance matrix.

Modeling of natural phenomena has been of particular interest in the graphics ommunity in recent years. This thesis will explore a method for creating and animating orb webs using a coupled spring-mass system. Using a spring-mass system for creating the orb web is ideal as we can represent each web strand using coupled spring-mass pairs. This allows the orb web simulator to be physically based, i.e., the simulation follows the laws that act on objects in the real world. This in turn simplifies the process of animating the web, as the animation emerges from the simulator without anyone having to set it up explicitly. Since this model is physically based, it would allow for realistic visualization of effects such as observing an orb web under a wind. In the children's book ``Charlotte's Web', the spider creates orb webs with words inscribed on them. Charlotte's web is used as an inspiration, in this thesis, to create webs which no real world spider could possibly create, while keeping the model physically based. This involves modifying the orb web such that the target text shows up on the orb web while keeping the web looking as natural as possible.

Introduction: with the expanded application of ERP system, business process model-ing, as an essential part of the ERP system implementation, has already attracted in-creasingly attention from the organizations. However, even though with the great signif-icance, there is no specific guideline to indicate the key factors of a business process modeling within the ERP system implementation context, especially from the supplier, customer and consultancy point of view. Accordingly, the authors are interested in the following two research questions:1. What are the factors that ERP customer. Supplier and consultancy focused on when they do business process modeling for an ERP implementation?2. How these factors are ranked and viewed by ERP customers, suppliers and con-sultants?Purpose: This study is an exploratory study aiming at identifying improvement factors for business process modeling within the context of ERP system implementation. addi-tionally, the authors seek to identify the diverse attitudes from the parties involved in the process of ERP implantation, that are supplier, customer and consultancy, towards the significance level of the improvement factors.Method: This research has adopted an inductive approach due to the nature of its re-search questions. Besides, data has been collected through six interviews in in total. And each supplier, consultancy, customer party has been conducted two interviews respec-tively. The six interview companies are with different cultural background. Additionally, secondary data from books, magazines are obtained through documentation as well.Conclusion: The thesis main results show that there are five improvement factors con-cerning the business process modeling within ERP system implementation, involving correctness, clarity, resource efficiency, cultural specification and relevance. Based upon the suppliers, consultancies and customers points of view, correctness, clarity and cul-tural specification can be categorized into basic factors and resource efficiency, rele-vance are grouped as optional factors. Furthermore, from the customers perspective, correctness and clarity ranks at the top two places among those five factors, while con-sultants focus on correctness and resource efficiency first. Besides, suppliers regard cor-rectness as the most fundamental improvement factors for business process modeling.

The idea that a complex object can be decomposed into simpler parts is fundamental to 3D design, so it is clearly desirable that digital representations of 3D shapes incorporate this part information. While solid modeling techniques based on set-theoretic volumetric composition intrinsically support hierarchical part-based shape descriptions, organic objects such as a human vertebra are more efficiently represented by surface modeling techniques. And although a human observer will easily identify part decompositions in surface models, the homogenous graphs of connected points and edges used in surface representations do not readily support explicit part decompositions. In this thesis, I will develop a part-based representation for 3D surface models. In abstract mathematics, a surface part can be represented as a deformation of a Riemannian manifold. To create a practical implementation, it is necessary to define representations of the 3D part shape and the region on the target surface where the part is to be placed. To represent the part region I will develop the Discrete Exponential Map (DEM), an algorithm which approximates the intrinsic normal coordinates on manifolds. To support arbitrary part shapes I will develop the COILS surface deformation, a robust geometric differential representation of point-sampled surfaces. Based on this part definition, I will then propose the Surface Tree, which makes possible the representation of complex shapes via a procedural, hierarchical composition of surface parts, analogous to the trees used in solid modeling. A major theme throughout the thesis is that part-based approaches have the potential to make surface design interfaces significantly more efficient and expressive. To explore this question and demonstrate the utility of my technical contributions, I present three novel modeling tools: an interactive texture design interface, a drag-and-drop mesh composition tool, and a sketch-based Surface Tree modeling environment. In addition to comparative algorithmic evaluations, and a consideration of representational capabilities, I have evaluated this body of work by publicly distributing my modeling tools. I will close the thesis with a discussion of the extensive feedback provided by users of my drag-and-drop mesh composition tool, called meshmixer. This feedback suggests that part-based approaches have significant benefits for surface modeling.

The idea that a complex object can be decomposed into simpler parts is fundamental to 3D design, so it is clearly desirable that digital representations of 3D shapes incorporate this part information. While solid modeling techniques based on set-theoretic volumetric composition intrinsically support hierarchical part-based shape descriptions, organic objects such as a human vertebra are more efficiently represented by surface modeling techniques. And although a human observer will easily identify part decompositions in surface models, the homogenous graphs of connected points and edges used in surface representations do not readily support explicit part decompositions. In this thesis, I will develop a part-based representation for 3D surface models. In abstract mathematics, a surface part can be represented as a deformation of a Riemannian manifold. To create a practical implementation, it is necessary to define representations of the 3D part shape and the region on the target surface where the part is to be placed. To represent the part region I will develop the Discrete Exponential Map (DEM), an algorithm which approximates the intrinsic normal coordinates on manifolds. To support arbitrary part shapes I will develop the COILS surface deformation, a robust geometric differential representation of point-sampled surfaces. Based on this part definition, I will then propose the Surface Tree, which makes possible the representation of complex shapes via a procedural, hierarchical composition of surface parts, analogous to the trees used in solid modeling. A major theme throughout the thesis is that part-based approaches have the potential to make surface design interfaces significantly more efficient and expressive. To explore this question and demonstrate the utility of my technical contributions, I present three novel modeling tools: an interactive texture design interface, a drag-and-drop mesh composition tool, and a sketch-based Surface Tree modeling environment. In addition to comparative algorithmic evaluations, and a consideration of representational capabilities, I have evaluated this body of work by publicly distributing my modeling tools. I will close the thesis with a discussion of the extensive feedback provided by users of my drag-and-drop mesh composition tool, called meshmixer. This feedback suggests that part-based approaches have significant benefits for surface modeling.