This thesis introduces "SNR," a Python library for programming robotic systems in the context of introductory robotics courses. Greater demand for roboticists has pressured educational institutions to expand robotics curricula. Students are now more likely to take robotics courses earlier and with less prior programming experience. Students may be attempting to simultaneously learn a systems programming language, a library API, and robotics concepts. SNR is written purely in Python to present familiar semantics, eliminating one of these learning curves. Industry standard robotics libraries such as ROS often require additional build tools and configuration languages. Students in introductory courses frequently lack skills needed for these tools. SNR does not use any additional build tools, so students are faced with fewer compounding learning curves. SNR presents students with concepts important to robotic systems programming such as modular and event driven architectures to bridge the gap between introductory programming courses and industry standard libraries.

The world is faced with uncertain and dramatic changes in water movement, availability, and quality are due to human-induced stressors such as population growth, climatic variability, and land use changes. At the apex of this problem is the need to understand and predict the complex forces that control the movement and life-cycle of water, a critical component of which is stormwater runoff. Success in addressing these issues is also dependent upon educating hydrology professionals who understand the physical processes that produce stormflow and the effects that these stressors have on stormwater runoff and water quality. This dissertation addresses these challenges through methodologies that can improve the way we measure stormflow and educate future hydrology professionals. A methodology is presented to (i) evaluate the uncertainty due to inadequate temporal sampling of stormflow data, and (ii) develop equations using regional regression analysis that can be used to select a stormflow sampling frequency of a watershed. A case study demonstrates how the proposed methodology has been applied to 25 stream gages with watershed areas ranging between 30 and 11,865 km2 within the Valley and Ridge geomorphologic region of Virginia. Results indicate that autocorrelation of stormflow hydrographs, drainage area of the catchment, and time of concentration are statistically significant predictor variables in single-variable regional regression analysis for estimating the site-specific stormflow sampling frequency under a specific magnitude of uncertainty. Methods and resources are also presented that utilize high-frequency continuous stormwater runoff data in hydrology education to improve student learning. Data from a real-time continuous watershed monitoring station (flow, water quality, and weather) were integrated into a senior level hydrology course at Virginia Tech (30 students) and two freshman level introductory engineering courses at Virginia Western Community College (70 students) over a period of 3 years using student-centered modules. The goal was to assess student learning through active and collaborative learning modules that provide students with field and virtual laboratory experiences. A mixed methods assessment revealed that student learning improved through modules that incorporated watershed data, and that students most valued working with real-world data and the ability to observe real-time environmental conditions. / Ph. D.

<p>  </p> <p>Sketching is a valuable skill in engineering for representing information, developing design ideas, and communicating technical and abstract information. Design thinking is supported through sketching as a means of translating between internal and external representations, and creating shared representations of collaborative thinking. Sketching is also an important means of developing spatial abilities which are predictive of success in STEM fields. Computer-based design visualization tools have largely replaced freehand sketching in undergraduate engineering classrooms, but the shift has negatively impacted students’ design thinking and spatial reasoning skills. While many published classroom assessments of engineering and engineering design sketching skill exist, few are linked to theory of mental rotation and mental imagery, and the validity evidence for these instruments is scarce. This dissertation reports the development of a new instrument to assess sketching skills in engineering education based on spatial reasoning skills. </p> <p>The first study is a systematic literature review of engineering education literature on sketching assessment, sketching constructs and metrics were identified across existing tests, as well as cognitive theory which informed their use and wider learning contexts and purposes for sketching assessment. From content analysis after abstract and full paper sorting and review, metrics on accuracy, perspective, line quality, annotations, and aesthetics were found to be most prevalent. Cognitive skills included perceiving the sketch subject, creatively sketching ideas, using metacognition to monitor the sketching process, and communication. Sketching assessment varies by discipline and relies on feedback and scores or grades as well as expert review. </p> <p>From these findings, a new Object Assembly Test of Sketching was developed to evaluate sketching skills on 3-dimensional mental imagery and mental rotation tasks in 1- and 2-point perspective. The second study describes two rounds of pilot testing and revisions to the Object Assembly Test over Fall 2021 and Spring 2022 in two sections of an undergraduate mechanical engineering course. Initial inter-rater reliability values were low and rubric categories had inconsistent performance, and mean scores suggested line quality metrics were more difficult for students than shape quality metrics. Revisions to rubric categories were made after consulting with subject matter experts in engineering design, industrial design, and assessment, and a second round of pilot testing showed improvement in reliability between raters with similar patterns of mean scores. </p> <p>The third study presents reliability and preliminary validity evidence for the Object Assembly Test’s use in undergraduate mechanical engineering design graphics courses. Correlation and repeated-measures ANOVA were used to investigate pre-post score differences before and after sketching classroom learning intervention. A linear relationship between Object Assembly scores and intelligent tutoring software sketching metrics was also modeled for pre-post scores. Inter-rater reliability was improved through two rounds of grading and discussion. Correlations were moderately positive between scores and metrics, with more complex exercises negatively related to Speed. Post-test scores were significantly higher than pre-test scores but did not interact significantly with skill. Linear regression results showed some significant prediction of Precision and Smoothness by shape quality metrics, and a clear speed-accuracy tradeoff with negative prediction of Speed by nearly all sketching skills. </p> <p>We anticipate future use for this instrument where instructors and researchers may implement the sketching exercises and rubric in engineering classrooms alongside 3-dimensional drawing software. The Object Assembly Test can provide students with opportunities to practice perspective sketching before using computer design tools, as well as apply mental imagery and mental rotation cognition when manipulating complex solid shapes for sketching. Ongoing validation studies with this instrument will expand to a more diverse test-taking population and develop a theoretical model of mental rotation and mental imagery skills to inform object assembly sketching. </p>

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