Using a sequential mixed-method methodology, this dissertation study set out to understand the emic and etic perspectives of the knowledge encompassed in the technological pedagogical content knowledge (TPACK) framework and to develop fixed answer questions based on that knowledge. While there have been many studies examining ways to measure TPACK in in-service and pre-service teachers, very few have addressed measuring TPACK using fixed-answer questions. Through the use of the mixed-methods, a snapshot of the emic (inside) and etic (outside) perspectives on the TPACK framework was obtained. This study used a focus group with in-service teachers (emic perspective) and interviews with teacher educators (etic perspective) to understand the kind of knowledge attributed to the TPACK framework. Six themes were derived from the focus group and interviews, from which fixed-answer questions were developed. Those six themes included such issues as access to technology, the use of technology for solid teaching and learning purposes, and passive versus active learning when using technology. Following best practices, the eleven questions included a scenario that gave context to the questions asked and the answers provided. In-service teachers reviewed the items to assure that the language and context were appropriate to classroom practice. Four experts on the TPACK framework reviewed the items for face validity. Across the experts six of the eleven items were rated as valid. Although only the experts saw a small number of items as valid, this study indicates that this kind of measurement for the TPACK framework may be possible.

Undergraduate biomechanics is classically viewed as one of the most difficult courses included in kinesiology programs, often leading to poor student performance and attitudes. By adjusting the interactions students have with course material, it may be possible to positively impact student outcomes. Past work has shown that interactive learning episodes can positively impact student attitudes toward difficult course content, as well as improve student performance variables (Catena & Carbonneau, n.d.; Moreno & Mayer, 2007; Pandy, Petrosino, Austin, & Barr, 2004; Zhang, Zhou, Briggs, & Nunamaker, 2005). In the present study, I investigated the effectiveness of interactive, exploratory based learning episodes in undergraduate biomechanics laboratory sessions. Episodes consisted of a brief introduction of the laboratory topic, which was consistent across groups, followed immediately by a pre- laboratory assessment. Students then completed the laboratory, which either included exploration in interactive computer applications or still images of the applications displaying the necessary information for completion. Intervention sessions utilized custom interactive computer applications where students were prompted to explore course concepts centered around reciprocal relationships between variables specific to each laboratory topic. Student performance was collected and assessed for Work Loop Muscle Mechanics and EMG signal processing laboratory topics at two independent instances. For both learning topics, intervention and control groups both, improved their scores between pre- and post-laboratory assessments indicating learning. In the post-laboratory testing, the intervention group significantly outperformed the control group on the most challenging assessment question (P = 0.005). Adversely, the intervention group achieved significantly lower scores for the simplest signal processing questionnaire item (P <0.001). Although the present study contained mixed results, it supports the utilization of exploratory based learning episodes on typically challenging topics with abstract concepts. Further investigation is needed in order to explore the chronic learning effects of such instructional methods. / Graduate

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Thesis (Ph.D. (Applied English Language)) -- University of Limpopo, 2019 / Refer to document

The purpose of this study was to explore the community college students’ perceptions on the use and effects of social media and social networking sites as well as any differences in perceptions based on students’ demographic characteristics. A community college in the state of Mississippi was chosen for this study. This community college is a comprehensive educational institution, accredited by the Commission on Colleges of the Southern Association of Colleges and Schools. The population for this study was currently enrolled freshman and sophomore students at one of the multiple locations. The respondents in this study were gathered by using a convenience sampling of students enrolled in academic core courses and electives or career and technical programs of study during the spring 2018 semester. The instrument that used for data collection in this study was a modified version of the Social Media Updates Survey (Pew Research Center, 2016). This study was quantitative in design, and a descriptive research methodology was used to conduct the study. The results revealed that students 25 years and older had significantly different social media habits than the students in the other age groups. Female students used social media more often than male students. More females than males used social media to interact with family. The data revealed that students mostly used mobile devices/tables to access social media and social networking sites. More students from academic programs accessed social media using desktops and laptops. More students from career technical education accessed social media using mobile devices. The most preferred social media websites were Facebook, YouTube, Instagram and SnapChat. Of the 201 respondents 41.3% agreed to social networking sites help them academically in getting educational materials for assignments or projects in class and 48.8% agreed that social networking sites are an effective tool for e-learning. 45.8% disagree to social media sites having positively affected their GPA. When asked the question, Social media networking sites have been effective in enhancing my active learning skills, 41.3% disagreed.

Surrogate models are indispensable in the analysis of engineering systems. The quality of surrogate models is determined by the data quality and the model class but achieving a high standard of them is challenging in complex engineering systems. Heterogeneity, implicit constraints, and extreme events are typical examples of the factors that complicate systems, yet they have been underestimated or disregarded in machine learning. This dissertation is dedicated to tackling the challenges in surrogate modeling of complex engineering systems by developing the following machine learning methodologies. (i) Partitioned active learning partitions the design space according to heterogeneity in response features, thereby exploiting localized models to measure the informativeness of unlabeled data. (ii) For the systems with implicit constraints, failure-averse active learning incorporates constraint outputs to estimate the safe region and avoid undesirable failures in learning the target function. (iii) The multi-output extreme spatial learning enables modeling and simulating extreme events in composite fuselage assembly. The proposed methods were applied to real-world case studies and outperformed benchmark methods. / Doctor of Philosophy / Data-driven decisions are ubiquitous in the engineering domain, in which data-driven models are fundamental. Active learning is a subdomain in machine learning that enables data-efficient modeling, and extreme spatial modeling is suitable for analyzing rare events. Although they are superb techniques for data-driven modeling, existing methods thereof cannot effectively address modern engineering systems complicated by heterogeneity, implicit constraints, and rare events. This dissertation is dedicated to advancing active learning and extreme spatial modeling for complex engineering systems by proposing three methodologies. The first method is partitioned active learning that efficiently learns systems, changing their behaviors, by localizing the information measurement. Second, failure-averse active learning is established to learn systems subject to implicit constraints, which cannot be analytically solved, and to minimize constraint violations. Lastly, the multi-output extreme spatial model is developed to model and simulate rare events that are associated with extremely large values in the aircraft manufacturing system. The proposed methods overcome the limitations of existing methods and outperform benchmark methods in the case studies.

This study investigated the effect of inquiry-based instruction in technical undergraduate education. Specifically, the effect was measured along two dimensions: 1) the effect on student learning and, 2) student attitude towards subject matter. The researcher designed an inquiry-based instructional approach to encourage interaction between teacher and students and to help students take more responsibility for their learning. Three technical undergraduate classes participated in the study. Each class was divided into experimental and control groups. For the experimental group, a twice-a-week traditional lecture was replaced with a once-a-week inquiry-based question and answer session. Students in the control group were taught as normal, by a traditional style lecture. Students in the experimental group were expected to use the extra hour, gained by meeting only once once-a-week, to study and prepare. Both groups were administered pre- and post- tests to determine the learning that took place during the experimental intervention. Pre- and post- surveys were also administered to assess the effect of the inquiry-based instruction on student attitude. Additionally, scores from student exams, professor surveys, and researcher observations were used to collect data and understand the effect of the instructional approach. The findings suggest that inquiry-based learning in technical classes can have a positive effect on learning and attitude.

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Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional composition, in which intermediate GP layers warp the original inputs, providing flexibility to model non-stationary dynamics. Recent applications in machine learning favor approximate, optimization-based inference for fast predictions, but applications to computer surrogate modeling - with an eye towards downstream tasks like Bayesian optimization and reliability analysis - demand broader uncertainty quantification (UQ). I prioritize UQ through full posterior integration in a Bayesian scheme, hinging on elliptical slice sampling of latent layers. I demonstrate how my DGP's non-stationary flexibility, combined with appropriate UQ, allows for active learning: a virtuous cycle of data acquisition and model updating that departs from traditional space-filling designs and yields more accurate surrogates for fixed simulation effort. I propose new sequential design schemes that rely on optimization of acquisition criteria through evaluation of strategically allocated candidates instead of numerical optimizations, with a motivating application to contour location in an aeronautics simulation. Alternatively, when simulation runs are cheap and readily available, large datasets present a challenge for full DGP posterior integration due to cubic scaling bottlenecks. For this case I introduce the Vecchia approximation, popular for ordinary GPs in spatial data settings. I show that Vecchia-induced sparsity of Cholesky factors allows for linear computational scaling without compromising DGP accuracy or UQ. I vet both active learning and Vecchia-approximated DGPs on numerous illustrative examples and real computer experiments. I provide open-source implementations in the "deepgp" package for R on CRAN. / Doctor of Philosophy / Scientific research hinges on experimentation, yet direct experimentation is often impossible or infeasible (practically, financially, or ethically). For example, engineers designing satellites are interested in how the shape of the satellite affects its movement in space. They cannot create whole suites of differently shaped satellites, send them into orbit, and observe how they move. Instead they rely on carefully developed computer simulations. The complexity of such computer simulations necessitates a statistical model, termed a "surrogate", that is able to generate predictions in place of actual evaluations of the simulator (which may take days or weeks to run). Gaussian processes (GPs) are a common statistical modeling choice because they provide nonlinear predictions with thorough estimates of uncertainty, but they are limited in their flexibility. Deep Gaussian processes (DGPs) offer a more flexible alternative while still reaping the benefits of traditional GPs. I provide an implementation of DGP surrogates that prioritizes prediction accuracy and estimates of uncertainty. For computer simulations that are very costly to run, I provide a method of sequentially selecting input configurations to maximize learning from a fixed budget of simulator evaluations. I propose novel methods for selecting input configurations when the goal is to optimize the response or identify regions that correspond to system "failures". When abundant simulation evaluations are available, I provide an approximation which allows for faster DGP model fitting without compromising predictive power. I thoroughly vet my methods on both synthetic "toy" datasets and real aeronautic computer experiments.

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