Atmospheric Flight Mechanics (AFM) is one of the cornerstones of aeronautical engineering and includes subjects like aerodynamic prediction, stability and control, dynamics, and vehicle design. These topics are critical to the success of aircraft development, so AFM is considered one of the most important foundational knowledge areas for aerospace engineering. Unfortunately, students graduating from aerospace engineering programs are often underprepared to perform in AFM jobs. This ongoing research focuses on developing a blueprint for assessing conceptual understanding of AFM concepts. Since existing literature suggests that novices and experts organize knowledge differently, comparing students' and experts' mental models can shine a light on the alternative conceptions that students retain post-instruction. As such, framing the study around synthetic mental models can be advantageous. To explore these mental models, three types of data have been collected and analyzed. Document analysis was done on course documents to identify what concept relationships were being presented to the students. Class observations were conducted to analyze how concepts are introduced to students and what relationships are highlighted by the instructor. Finally, a concept mapping activity was facilitated to study the mental models that the students built after instruction. The results show a lack of synthetization between the knowledge introduced in the classroom and students' prior knowledge which translated into student mental models that do not meet some of the expectations of the course. Moreover, this study highlights the importance of the instructor's awareness of their own expectations for learning and knowledge synthetization in the design of an AFM course. / Doctor of Philosophy / Conceptual understanding research has often focused on how students develop their understanding of scientific concepts that are difficult to grasp. Through this research, many assessment techniques have been developed and implemented in the design of STEM courses. However, many of these techniques and implementations have been limited to K-12 or introductory engineering courses. Atmospheric Flight Mechanics (AFM) is an important part of the aerospace curriculum that has yet to be studied under the conceptual understanding lens. The goal of this study was to investigate how students develop AFM conceptual understanding using a synthetic mental model framework. This study focused on answering three questions, how are students being introduced to AFM concepts?, how do students' mental models develop throughout the semester?, and how do the students' and instructor's mental models compare?. Through the exploration of class documents, class observations, and concept mapping activities, this research found that students are having a hard time making sense of new knowledge based on their previous understanding of similar topics. By trying to integrate this new knowledge with their previous mental models, students are developing synthetic mental models that do not align with the scientific explanations of the topic. This study also found that instructors are often unaware of their own knowledge and expectations for learning which makes knowledge synthetization harder for the students. Therefore, addressing these issues during course design could make an AFM course easier to understand for students.

With the rapid development of computer and information technologies, medical imaging has become one of the major sources of information for therapy and research in medicine, biology and other fields. Along with the advancement of medical imaging techniques, computer-aided detection and diagnosis (CAD/CADx) has recently emerged to become one of the major research subjects within the area of diagnostic radiology and medical image analysis. This thesis presents two multi-level learning-based approaches for medical image understanding with applications of CAD/CADx. The so-called "multi-level learning strategy" relies on that supervised and unsupervised statistical learning techniques are utilized to hierarchically model and analyze the medical image content in a "bottom up" way. As the first approach, a learning-based algorithm for automatic medical image classification based on sparse aggregation of learned local appearance cues is proposed. The algorithm starts with a number of landmark detectors to collect local appearance cues throughout the image, which are subsequently verified by a group of learned sparse spatial configuration models. In most cases, a decision could already be made at this stage by simply aggregating the verified detections. For the remaining cases, an additional global appearance filtering step is employed to provide complementary information to make the final decision. This approach is evaluated on a large-scale chest radiograph view identification task and a multi-class radiograph annotation task, demonstrating its improved performance in comparison with other state-of-the-art algorithms. It also achieves high accuracy and robustness against images with severe diseases, imaging artifacts, occlusion, or missing data. As the second approach, a learning-based approach for automatic segmentation of ill-defined and spiculated mammographic masses is presented. The algorithm starts with statistical modeling of exemplar-based image patches. Then, the segmentation problem is regarded as a pixel-wise labeling problem on the produced mass class-conditional probability image, where mass candidates and clutters are extracted. A multi-scale steerable ridge detection algorithm is further employed to detect spiculations. Finally, a graph-cuts technique is employed to unify all outputs from previous steps to generate the final segmentation mask. The proposed method specifically tackles the challenge of inclusion of mass margin and associated extension for segmentation, which is considered to be a very difficult task for many conventional methods. / Master of Science

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The technology principle in the Principles and Standards for School Mathematics (NCTM, 2000) states that technology plays an important role in how teachers teach mathematics and in how students learn mathematics. The purpose of this sequential explanatory mixed-methods study was to examine the impact of interactive applets on students’ understanding of parameter changes to parent functions. Students in the treatment classes were found to have statistically significantly higher posttest scores than students in the control classes. Although the data analysis showed a statistically significant difference between classes on procedural understanding, no statistically significant difference was found with regard to conceptual understanding. Student and teacher interviews provided insight on how and why the use of applets helped or hindered students’ understanding of parameter changes to parent functions.

Visual textures play an important role in image understanding because theyare a key component of the semantic of many images. Furthermore, texture representations, which pool local image descriptors in an orderless manner, have hada tremendous impact in a wide range of computer vision problems, from texture recognition to object detection. In this thesis we make several contributions to the area of texture understanding. First, we add a new semantic dimension to texture recognition. Instead of focusing on instance or material recognition, we propose a human-interpretable vocabulary of texture attributes, inspired from studies in Cognitive Science, to describe common texture patterns. We also develop a corresponding dataset, the Describable Texture Dataset (DTD), for benchmarking. We show that these texture attributes produce intuitive descriptions of textures. We also show that they can be used to extract a very low dimensional representation of any texture that is very effective in other texture analysis tasks, including improving the state-of-the art in material recognition on the most challenging datasets available today. Second, we look at the problem of recognizing texture attributes and materials in realistic uncontrolled imaging conditions, including when textures appear in clutter. We build on top of the recently proposed Open Surfaces dataset, introduced by the graphics community, by deriving a corresponding benchmarks for material recognition. In addition to material labels, we also augment a subset of Open Surfaces with semantic attributes. Third, we propose a novel texture representation, combining the recent advances in deep-learning with the power of Fisher Vector pooling. We provide thorough evaluation of the new representation, and revisit in general classic texture representations, including bag-of-visual-words, VLAD and the Fisher Vectors, in the context of deep learning. We show that these pooling mechanisms have excellent efficiency and generalisation properties if the convolutional layers of a deep model are used as local features. We obtain in this manner state-of-the-art performance in numerous datasets, both in texture recognition and image understanding in general. We show through our experiments that the proposed representation is an efficient way to apply deep features to image regions, and that it is an effective manner of transferring deep features from one domain to another.

The purpose of this study is to examine philosophical hermeneutics as a referent for student understanding of Nature-of-Science (NOS) concepts. Rather than focus on a prescriptive set of canons used in addressing NOS pedagogy in K-12 schools, this study seeks to explicate a descriptive set of principles based on Hans Georg-Gadamer’s theory of interpretation that has the potential for developing dispositions necessary for understanding. Central among these are the concepts of fore-structure, prejudice, temporal distance, and history of effect, all of which constitute part of the whole of the hermeneutic circle as envisaged by Gadamer. As such, Gadamer’s hermeneutics is contrasted with Cartesian epistemology and its primacy of method, the Enlightenment’s prejudice against prejudice, the modernist/progressive tendency to consider all situations as problems to be solved by relegating all forms of knowledge to techné, and the subjective nature of interpretation inherent in a hermeneutics of suspicion. The implication of such a conceptual analysis for NOS pedagogy is that student understanding is considered not so much as a cognitive outcome dependent on a series of mental functions but rather as an ontological characteristic of Dasein (being-human) that situates learning in the interchange between interpreter and text. In addition, the philosophical foundations implicit in addressing student understanding of NOS found in many curricular reform efforts and pedagogical practices in science education are questioned. Gadamer’s hermeneutics affords science education a viable philosophical framework within which to consider student understanding of the development of scientific knowledge and the scientific enterprise.

Despite numerous studies of the development of theory of mind, how children express their understanding of mind in less structured, play settings has gone largely unstudied. Many developmental accounts, regardless of disagreement on other theoretical issues, agree that the child’s engagement within social contexts is crucial to the development of understanding of mind. Our goals were to collect a detailed account of how children use their understanding of mind and how mothers align their support to the child’s capabilities within social interactions. In this longitudinal study, typically developing preschoolers (N = 52) engaged in a hiding game with their mothers in a semi-structured play setting when the children were 42-, 54-, and 66-months old. Aspects of children’s understanding of mind were rated including understanding of knowledge access, deception, false belief, and emotional response to false belief, as well as, affective charge and engagement with the task. Mothers’ utterances were coded for various characteristics, particularly role and content. Children’s understanding of mind increased across visits and positively correlated with false belief task performance at the 42- and 54-month visits, rs = .35 and .39, p < .05, but not the 66-month visit, rs = –.25, p = .10. Children’s enthusiasm was positively related to their understanding of mind at the first and second visits, but not the last. Mothers tailored the content of their utterances to the child’s growing expertise, but whether mothers adjusted the role of their utterances to children’s understanding of mind remains unclear. Observing children’s playful use of their emerging understanding of mind in social interactions allowed for the capture of subtle variations in how children express and caregivers support their understanding.

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