The active learning assignment, Pink Time, provides an opportunity to experience and reflect upon learning that may both benefit individuals and contribute to high-quality teaching. Previous studies have found that Pink Time supports university students' motivation and comprehension of the learning process (Baird et al., 2020, Baird et al., 2015). The present study examined the impact of an active learning assignment, Pink Time, on pre-service elementary teachers' motivated-related perceptions. A multiple method approach offers an understanding of the extent to which Pink Time influences the three psychological needs that are a part of Self-Determination Theory (SDT). This theory provides a framework to examine three key components of motivation: autonomy, competence, and relatedness. A sample of 28 pre-service teachers participated in two Pink Time iterations over two different courses. Quantitative data was collected through 21 responses on the MUSIC Model of Academic Inventory (Jones, 2012, 2020) with open-ended response questions to perceptions related to empowerment (autonomy), usefulness, success (competence), interest, and caring (relatedness). Qualitative data was collected using five interviews, four group discussions, and 21 responses to the open-ended survey questions on the MUSIC Model Inventory. The findings imply that Pink Time is a useful tool to support pre-service teachers' perception of motivation in areas of empowerment, usefulness, success, interest, and caring. Implications of this study include contributions to classroom assignments in teacher education programs that support motivation which results in high-quality teachers. Pink Time may also be used in the PK-12 setting for both students and teachers. Supporting PK-12 students in pursuing interests and increasing motivation is pertinent to academic success. Educational leaders could offer teachers professional development opportunities through Pink Time where teachers seek out their interests to support their own professional growth and uniquely contribute to school-level outcomes such as inclusive learning environments, effective online/virtual learning, and wellness. / Doctor of Philosophy / This study was used to understand how pre-service teachers perceive motivation through an active learning assignment called Pink Time. Pre-service teachers participated in two Pink Time assignments by skipping class and learning about a self-selected topic related to education. During the next class session, the pre-service teachers presented what they had learned through the assignment and about themselves as learners. After the presentations were completed, the researcher facilitated a discussion with motivation-related questions. After completing the second Pink Time assignment, pre-service teachers were given a survey that included open-ended questions. Five interviews were conducted after the two Pink Time assignments were completed. Analysis from the interviews, group discussions, and answers on the open-ended items suggested that pre-service teachers described their motivation-related perceptions of Pink Time with three overall themes: influencers of motivation, outcomes of Pink Time, and reactions toward Pink Time. This study showed that Pink Time supported pre-service teachers' motivation related to empowerment, usefulness, success, interest, and caring. Implications of this study can lead to teacher educator programs using Pink Time to support pre-service teachers' motivation. Implications are discussed for the PK-12 school setting as PK-12 teachers can support their students' motivation by allowing young learners to choose topics of interest to learn.

Due to advancements in supercomputing and algorithms for finite element analysis, today's computer simulation models often contain complex calculations that can result in a wealth of knowledge. Gaussian processes (GPs) are highly desirable models for computer experiments for their predictive accuracy and uncertainty quantification. This dissertation addresses GP modeling when data abounds as well as GP adaptive design when simulator expense severely limits the amount of collected data. For data-rich problems, I introduce a localized sparse covariance GP that preserves the flexibility and predictive accuracy of a GP's predictive surface while saving computational time. This locally induced Gaussian process (LIGP) incorporates latent design points, inducing points, with a local Gaussian process built from a subset of the data. Various methods are introduced for the design of the inducing points. LIGP is then extended to adapt to stochastic data with replicates, estimating noise while relying upon the unique design locations for computation. I also address the goal of identifying a contour when data collection resources are limited through entropy-based adaptive design. Unlike existing methods, the entropy-based contour locator (ECL) adaptive design promotes exploration in the design space, performing well in higher dimensions and when the contour corresponds to a high/low quantile. ECL adaptive design can join with importance sampling for the purpose of reducing uncertainty in reliability estimation. / Doctor of Philosophy / Due to advancements in supercomputing and physics-based algorithms, today's computer simulation models often contain complex calculations that can produce larger amounts of data than through physical experiments. Computer experiments conducted with simulation models are sought-after ways to gather knowledge about physical problems but come with design and modeling challenges. In this dissertation, I address both data size extremes - building prediction models with large data sets and designing computer experiments when scarce resources limit the amount of data. For the former, I introduce a strategy of constructing a series of models including small subsets of observed data along with a set of unobserved data locations (inducing points). This methodology also contains the ability to perform calculations with only unique data locations when replicates exist in the data. The locally induced model produces accurate predictions while saving computing time. Various methods are introduced to decide the locations of these inducing points. The focus then shifts to designing an experiment for the purpose of accurate prediction around a particular output quantity of interest (contour). A experimental design approach is detailed that selects new sample locations one-at-a-time through a function to maximize the amount of information gain in the contour region for the overall model. This work is combined with an existing method to estimate the true volume of the contour.

Active learning (AL) aims at reducing labeling effort by identifying the most valuable unlabeled data points from a large pool. Traditional AL frameworks have two limitations: First, they perform data selection in a multi-round manner, which is time-consuming and impractical. Second, they usually assume that there are a small amount of labeled data points available in the same domain as the data in the unlabeled pool. In this thesis, we initiate the study of one-round active learning to solve the first issue. We propose DULO, a general framework for one-round setting based on the notion of data utility functions, which map a set of data points to some performance measure of the model trained on the set. We formulate the one-round active learning problem as data utility function maximization. We then propose D²ULO on the basis of DULO as a solution that solves both issues. Specifically, D²ULO leverages the idea of domain adaptation (DA) to train a data utility model on source labeled data. The trained utility model can then be used to select high-utility data in the target domain and at the same time, provide an estimate for the utility of the selected data. Our experiments show that the proposed frameworks achieves better performance compared with state-of-the-art baselines in the same setting. Particularly, D²ULO is applicable to the scenario where the source and target labels have mismatches, which is not supported by the existing works. / M.S. / Machine Learning (ML) has achieved huge success in recent years. Machine Learning technologies such as recommendation system, speech recognition and image recognition play an important role on human daily life. This success mainly build upon the use of large amount of labeled data: Compared with traditional programming, a ML algorithm does not rely on explicit instructions from human; instead, it takes the data along with the label as input, and aims to learn a function that can correctly map data to the label space by itself. However, data labeling requires human effort and could be time-consuming and expensive especially for datasets that contain domain-specific knowledge (e.g., disease prediction etc.) Active Learning (AL) is one of the solution to reduce data labeling effort. Specifically, the learning algorithm actively selects data points that provide more information for the model, hence a better model can be achieved with less labeled data. While traditional AL strategies do achieve good performance, it requires a small amount of labeled data as initialization and performs data selection in multi-round, which pose great challenge to its application, as there is no platform provide timely online interaction with data labeler and the interaction is often time inefficient. To deal with the limitations, we first propose DULO which a new setting of AL is studied: data selection is only allowed to be performed once. To further broaden the application of our method, we propose D²ULO which is built upon DULO and Domain Adaptation techniques to avoid the use of initial labeled data. Our experiments show that both of the proposed two frameworks achieve better performance compared with state-of-the-art baselines.

Voice Personal Assistant (VPA) is a software agent, which can interpret the user's voice commands and respond with appropriate information or action. The users can operate the VPA by voice to complete multiple tasks, such as read the message, order coffee, send an email, check the news, and so on. Although this new technique brings in interesting and useful features, they also pose new privacy and security risks. The current researches have focused on proof-of-concept attacks by pointing out the potential ways of launching the attacks, e.g., craft hidden voice commands to trigger malicious actions without noticing the user, fool the VPA to invoke the wrong applications. However, lacking a comprehensive understanding of the functionality of the skills and its commands prevents us from analyzing the potential threats of these attacks systematically. In this project, we developed convolutional neural networks with active learning and keyword-based approach to investigate the commands according to their capability (information retrieval or action injection) and sensitivity (sensitive or nonsensitive). Through these two levels of analysis, we will provide a complete view of VPA skills, and their susceptibility to the existing attacks. / M.S. / Voice Personal Assistant (VPA) is a software agent, which can interpret the users' voice commands and respond with appropriate information or action. The current popular VPAs are Amazon Alexa, Google Home, Apple Siri and Microsoft Cortana. The developers can build and publish third-party applications, called skills in Amazon Alex and actions in Google Homes on the VPA server. The users simply "talk" to the VPA devices to complete different tasks, like read the message, order coffee, send an email, check the news, and so on. Although this new technique brings in interesting and useful features, they also pose new potential security threats. Recent researches revealed that the vulnerabilities exist in the VPA ecosystems. The users can incorrectly invoke the malicious skill whose name has similar pronunciations to the user-intended skill. The inaudible voice triggers the unintended actions without noticing users. All the current researches focused on the potential ways of launching the attacks. The lack of a comprehensive understanding of the functionality of the skills and its commands prevents us from analyzing the potential consequences of these attacks systematically. In this project, we carried out an extensive analysis of third-party applications from Amazon Alexa and Google Home to characterize the attack surfaces. First, we developed a convolutional neural network with active learning framework to categorize the commands according to their capability, whether they are information retrieval or action injection commands. Second, we employed the keyword-based approach to classifying the commands into sensitive and nonsensitive classes. Through these two levels of analysis, we will provide a complete view of VPA skills' functionality, and their susceptibility to the existing attacks.

Yes / To identify co-produced multi-stakeholder perspectives important for successful widespread physically active learning (PAL) adoption and implementation. A total of 35 stakeholders (policymakers n = 9; commercial education sector, n = 8; teachers, n = 3; researchers, n = 15) attended a design thinking PAL workshop. Participants formed 5 multi-disciplinary groups with at least 1 representative from each stakeholder group. Each group, facilitated by a researcher, undertook 2 tasks: (1) using Post-it Notes, the following question was answered: within the school day, what are the opportunities for learning combined with movement? and (2) structured as a washing-line task, the following question was answered: how can we establish PAL as the norm? All discussions were audio-recorded and transcribed. Inductive analyses were conducted by 4 authors. After the analyses were complete, the main themes and subthemes were assigned to 4 predetermined categories: (1) PAL design and implementation, (2) priorities for practice, (3) priorities for policy, and (4) priorities for research. The following were the main themes for PAL implementation: opportunities for PAL within the school day, delivery environments, learning approaches, and the intensity of PAL. The main themes for the priorities for practice included teacher confidence and competence, resources to support delivery, and community of practice. The main themes for the policy for priorities included self-governance, the Office for Standards in Education, Children's Services, and Skill, policy investment in initial teacher training, and curriculum reform. The main themes for the research priorities included establishing a strong evidence base, school-based PAL implementation, and a whole-systems approach. The present study is the first to identify PAL implementation factors using a combined multi-stakeholder perspective. To achieve wider PAL adoption and implementation, future interventions should be evidence based and address implementation factors at the classroom level (e.g., approaches and delivery environments), school level (e.g., communities of practice), and policy level (e.g., initial teacher training). / The research symposium and workshop were supported by an internal research grant from the School of Sport, Leeds Beckett University

"The objectives of this research were (1) to develop experimental active-based-learning curricula for undergraduate courses in transportation engineering and (2) to assess the effectiveness of an active-learning-based traffic engineering curriculum through an educational experiment. The researcher developed a new highway design course as a pilot study to test selected active-learning techniques before employing them in the traffic engineering curriculum. Active-learning techniques, including multiple-choice questions, short problems completed by individual students or small groups, and group discussions, were used as active interludes within lectures. The researcher also collected and analyzed student performance and attitude data from control and experimental classes to evaluate the relative effectiveness of the traditional lecture (control) approach and the active-learning (experimental) approach. The results indicate that the active-learning approach adopted for the experimental class did have a positive impact on student performance as measured by exam scores. The students in the experimental class also indicated slightly more positive attitudes at the end of the course than the control class, although the difference was not significant. The author recommends that active interludes similar to those in the experimental curricula be used in other courses in civil engineering."

Thesis (M. Ed.)--University of Hong Kong, 1998. / Includes bibliographical references (leaves 148-157).

Thesis (M.Ed.)--University of Hong Kong, 1998. / Includes bibliographical references (leaves 148-157). Also available in print.

L’apprentissage statistique cherche à modéliser un lien fonctionnel entre deux variables X et Y à partir d’un échantillon aléatoire de réalisations de (X,Y ). Lorsque la variable Y prend un nombre binaire de valeurs, l’apprentissage s’appelle la classification (ou discrimination en français) et apprendre le lien fonctionnel s’apparente à apprendre la frontière d’une variété dans l’espace de la variable X. Dans cette thèse, nous nous plaçons dans le contexte de l’apprentissage actif, i.e. nous supposons que l’échantillon d’apprentissage n’est plus aléatoire et que nous pouvons, par l’intermédiaire d’un oracle, générer les points sur lesquels l’apprentissage de la variété va s’effectuer. Dans le cas où la variable Y est continue (régression), des travaux précédents montrent que le critère de la faible discrépance pour générer les premiers points d’apprentissage est adéquat. Nous montrons, de manière surprenante, que ces résultats ne peuvent pas être transférés à la classification. Dans ce manuscrit, nous proposons alors le critère de la dispersion pour la classification. Ce critère étant difficile à mettre en pratique, nous proposons un nouvel algorithme pour générer un plan d’expérience à faible dispersion dans le carré unité. Après une première approximation de la variété, des approximations successives peuvent être réalisées afin d’affiner la connaissance de celle-ci. Deux méthodes d’échantillonnage sont alors envisageables : le « selective sampling » qui choisit les points à présenter à un oracle parmi un ensemble fini de candidats et l’« adaptative sampling » qui permet de choisir n’importe quels points de l’espace de la variable X. Le deuxième échantillonnage peut être vu comme un passage à la limite du premier. Néanmoins, en pratique, il n’est pas raisonnable d’utiliser cette méthode. Nous proposons alors un nouvel algorithme basé sur le critère de dispersion, menant de front exploitation et exploration, pour approximer une variété. / Statistical learning aims to modelize a functional link between two variables X and Y thanks to a random sample of realizations of the couple (X,Y ). When the variable Y takes a binary number of values, learning is named classification and learn the functional link is equivalent to learn the boundary of a manifold in the feature space of the variable X. In this PhD thesis, we are placed in the context of active learning, i.e. we suppose that learning sample is not random and that we can, thanks to an oracle, generate points for learning the manifold. In the case where the variable Y is continue (regression), previous works show that criterion of low discrepacy to generate learning points is adequat. We show that, surprisingly, this result cannot be transfered to classification talks. In this PhD thesis, we propose the criterion of dispersion for classification problems. This criterion being difficult to realize, we propose a new algorithm to generate low dispersion samples in the unit cube. After a first approximation of the manifold, successive approximations can be realized in order to refine its knowledge. Two methods of sampling are possible : the « selective sampling » which selects points to present to the oracle in a finite set of candidate points, and the « adaptative sampling » which allows to select any point in the feature space of the variable X. The second sampling can be viewed as the infinite limit of the first. Nevertheless, in practice, it is not reasonable to use this method. Then, we propose a new algorithm, based on dispersion criterion, leading both exploration and exploitation to approximate a manifold.

This mixed methods study investigated 5th-grade teachers' reported use of computer technology and variables that have been identified by researchers as affecting teachers' use of technology, including professional development activities, physical access to computer technology, and technical and instructional support provided for teachers. Quantitative data were collected from 80 5th-grade teachers from a Florida public school district through an online survey in which teachers reported how frequently they used and had their students use computer technology for 27 different purposes. The teachers also reported the amount of emphasis those 27 different topics received during their technology-related professional development experiences, the number of hours they participated in technology-related professional development, the number of months they participating in a technology coaching/mentoring program, the access their students had to computers in the classroom and in a one-to-one co mputing environment, and the frequency that they received technical and instructional support. Information from the school district's technology plan provided a context for the study. Qualitative data were collected through interviews with seven of the survey participants. The findings indicated that for 18 different purposes of technology, there was a significant correlation between how frequently teachers used and had their students use technology and the teacher-reported emphasis those topics received during technology related professional development. Self-reported frequency of support, student-to-computer ratio in the classroom, hours of professional development, and months of mentoring did not moderate the relationship between frequency of technology use and the content of professional development. / The relationship between having students use technology to work cooperatively or collaboratively and the reported emphasis that topic received in professional development strengthened if teachers reported that their students had access to a one-to-one computing environment. An additional finding was that the teachers' reported frequency of use of technology and reported emphasis of content of technology-related professional development leaned toward direct instruction and test preparation and leaned less toward innovative uses of technology. Implications and suggestions for future research are offered for technology integration and professional development for teachers at the elementary school level. / by Debbie Beaudry. / Thesis (Ph.D.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.