23,899 research outputs found
ENHANCING STUDENT ENGAGEMENT, TEACHER SELF-EFFICACY, AND PRINCIPAL LEADERSHIP SKILLS THROUGH MORNING MEETING IN AN ONLINE LEARNING ENVIRONMENT
This study examined the experiences of educators in a small, rural elementary school who provided live instruction in an online setting during the COVID-19 pandemic. The scholarly practitioner collaborated with inquiry partners to enhance student engagement, teacher self-efficacy, and principal leadership skills by implementing Morning Meeting, a social and emotional learning program from Responsive Classroom®, when students participated in remote online learning. The scholarly practitioner used over four decades of research about efficacy and identified leadership strategies and approaches that assisted in building individual and collective teacher efficacy so that teachers could effectively engage students.
Behavioral, emotional, and cognitive engagement were identified in research and used by teachers to determine the quality of participation in Morning Meeting. Teachers took daily and weekly attendance to measure engagement, and the scholarly practitioner facilitated team meetings with groups of teachers to compile comments and statements regarding student engagement. These statements were coded using pre-selected codes based on research about types of student engagement.
The scholarly practitioner facilitated the administration of a pre-study and post-study Teacher Self-Efficacy Scale so that individual, grade-span, and full-school efficacy data could be compiled. In addition, the scholarly practitioner held team meetings with the teachers to compile comments and categorize those statements into four areas: job accomplishment, skill development, social interaction, and coping with job stress. These four areas were also coded using the four categories described on the Teacher Self-Efficacy Scale.
The scholarly practitioner also maintained a journal using a self-reflection tool about the lived experiences before, during, and after the study. The emphasis on this journal was about the development and growth of leadership skills, and the categories were pre-coded using Bernard Bass’s categories of transformational leadership: individualized consideration, inspirational motivation, idealized influence, and intellectual stimulation.
Student engagement increased throughout the study, and 77 percent of students were fully engaged during the study. Teachers expressed an increase in collective efficacy at the conclusion of the study, and six of the eight teachers reported individual increases in efficacy. The scholarly practitioner’s use of differentiation within the context of transformational leadership was observed most frequently in the study
Environmental-agreement design and political ideology in democracies
Does the political ideology of negotiating parties influence the design of international environmental agreements? This article distinguishes between leftist and rightist executives in democracies to develop a twofold argument. First, left-leaning democratic governments tend to be generally more environmental-friendly, which implies that they should favor designs that are more conducive to effective institutions. Second, leftist democratic executives are commonly less concerned about sovereignty costs. Both mechanisms suggest that environmental treaties likely comprise “legalized,” i.e., hard-law elements when left-wing democracies negotiate their design. The empirical implication of the theory is tested with quantitative data on international environmental agreements since 1975. The findings report an association between leftist ideology in democracies and agreement legalization, although this is driven by aspects of sovereignty delegation. This article contributes to the literatures on environmental institutions, international cooperation more generally, as well as party politics
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The influence of blockchains and internet of things on global value chain
Copyright © 2022 The Authors. Despite the increasing proliferation of deploying the internet of things (IoT) in the global value chain (GVC), several challenges might lead to a lack of trust among value chain partners, for example, technical challenges (i.e., confidentiality, authenticity, and privacy); and security challenges (i.e., counterfeiting, physical tampering, and data theft). In this study, we argue that blockchain technology (BT), when combined with the IoT ecosystem, will strengthen GVC and enhance value creation and capture among value chain partners. Therefore, we examine the impact of BT combined with the IoT ecosystem and how it can be utilized to enhance value creation and capture among value chain partners. We collected data through an online survey, and 265 U.K. Agri-food retailers completed the survey. Our data were analyzed using structural equation modeling. Our finding reveals that BT enhances GVC by improving IoT scalability, security, and traceability combined with the IoT ecosystem. Moreover, the combination of BT and IoT strengthens GVC and creates more value for value chain partners, which serves as a competitive advantage. Finally, our research outlines the theoretical and practical contribution of combining BT and the IoT ecosystem
Image classification over unknown and anomalous domains
A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting.
Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each.
While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so.
In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks
Data-to-text generation with neural planning
In this thesis, we consider the task of data-to-text generation, which takes non-linguistic
structures as input and produces textual output. The inputs can take the form of
database tables, spreadsheets, charts, and so on. The main application of data-to-text
generation is to present information in a textual format which makes it accessible to
a layperson who may otherwise find it problematic to understand numerical figures.
The task can also automate routine document generation jobs, thus improving human
efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful
encoder-decoder architecture or its variants. These models generate fluent (but often
imprecise) text and perform quite poorly at selecting appropriate content and ordering
it coherently. This thesis focuses on overcoming these issues by integrating content
planning with neural models. We hypothesize data-to-text generation will benefit from
explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our
generator are tables (with records) in the sports domain. And the output are summaries
describing what happened in the game (e.g., who won/lost, ..., scored, etc.).
We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records
should be mentioned and in which order, and then generate the document while taking
the micro plan into account.
We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the
records corresponding to the entities by using hierarchical attention at each time step.
We then combine planning with the high level organization of entities, events, and
their interactions. Such coarse-grained macro plans are learnt from data and given
as input to the generator. Finally, we present work on making macro plans latent
while incrementally generating a document paragraph by paragraph. We infer latent
plans sequentially with a structured variational model while interleaving the steps of
planning and generation. Text is generated by conditioning on previous variational
decisions and previously generated text.
Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document
The Impact of Social Media Use on Online Collective Action During China’s COVID-19 Pandemic Mitigation: A Social Identity Model of Collective Action (SIMCA) Perspective
The role of social media in fostering collective action in China is under constant debate,
and the mechanism underlying the effects of social media use on collective action has not
garnered sufficient scholarly attention. This study aims to investigate the (in)direct effects
of attention to social media—administered by the governmental (gov) and
nongovernmental sectors (nongov), respectively—for information about COVID-19
mitigation in China on intention to participate in online collective action (IPOCA). Findings
from a survey suggest that attention to both social media (gov) and social media (nongov)
directly predicted IPOCA. The indirect effect of attention to social media (gov) on IPOCA
was significantly mediated by social identification. This study evidences the impact of
social media on collective action in China and theoretically underpins its mechanisms
through the social identity model of collective action
Sexual violence as a form of social control : the role of hostile and benevolent sexism
This thesis examines the feminist hypothesis that rape functions as a tool of social control through which women are kept in subordinate social positions (Brownmiller, 1975). In examining this hypothesis, the current thesis explores the role of benevolent and hostile sexism in accounting for people's responses to different types of rape (i.e. stranger vs. acquaintance rape). An examination of the literature suggests that there are general societal beliefs in the distinction between "good" and "bad" rape victims (Pollard, 1992). Interestingly, researchers have observed that benevolent sexism (BS) is related to the idealisation of women in traditional gender roles (i.e. "good" women; Glick et aI., 2000). It is, therefore, argued that individuals who idealise women in traditional roles (i.e. high BS individuals) are more likely to negatively evaluate rape victims who can be perceived as violating these norms. Nine empirical studies are presented in this thesis. Study 1 examines the potential role of BS in accounting for previously observed differences in the amount of blame attributed to stranger and acquaintance rape victims (e.g. Pollard, 1992). Studies 2 and 3 examine the psychological mechanisms that underlie the relationship between BS and victim blame in acquaintance rape situations. Studies 2 and 4 also explore the psychological mechanisms that underlie the relationship between hostile sexism (HS) and self reported rape proclivity in acquaintance rape situations (c.f. Viki, 2000). In Study 5, the relationship between BS and paternalistic chivalry (attitudes that are simultaneously courteous and restrictive to women) is examined. Studies 6 and 7 examine the role of BS in accounting for participants' responses to stranger vs. acquaintance rape perpetrators. The last two studies (Studies 8 and 9) examine the potential role of legal verdicts in moderating the relationship between BS and victim blame in acquaintance rape cases. Taken together, the results support the argument that BS provides a psychological mechanism through which differences in the amount of blame attributed to stranger and acquaintance rape victims can be explained. In contrast, HS provides a mechanism for explaining differences in self-reported proclivity to commit stranger and acquaintance rape. The thesis concludes with a summary of the findings, a discussion of the methodological limitations of the studies and suggestions of directions for future research
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Reliable Decision-Making with Imprecise Models
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if the system has access to a comprehensive decision-making model that accounts for all the details in the environment and all possible scenarios the agent may encounter, it may be intractable to solve this complex model optimally. Consequently, this complex, high fidelity model may be simplified to accelerate planning, introducing imprecision. Reasoning with such imprecise models affects the reliability of autonomous systems. A system\u27s actions may sometimes produce unexpected, undesirable consequences, which are often identified after deployment. How can we design autonomous systems that can operate reliably in the presence of uncertainty and model imprecision?
This dissertation presents solutions to address three classes of model imprecision in a Markov decision process, along with an analysis of the conditions under which bounded-performance can be guaranteed. First, an adaptive outcome selection approach is introduced to devise risk-aware reduced models of the environment that efficiently balance the trade-off between model simplicity and fidelity, to accelerate planning in resource-constrained settings. Second, a framework that extends stochastic shortest path framework to problems with imperfect information about the goal state during planning is introduced, along with two solution approaches to solve this problem. Finally, two complementary solution approaches are presented to minimize the negative side effects of agent actions. The techniques presented in this dissertation enable an autonomous system to detect and mitigate undesirable behavior, without redesigning the model entirely
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