4,722 research outputs found
The Comparative Intercultural Sensitivity of American Faculty Teaching Abroad and Domestically : A Mixed-Methods Investigation Employing Participant-Generated Visuals
This thesis aimed to identify and compare the intercultural sensitivity, or IS, of tertiary American instructors teaching mono-national, non-American student populations abroad in the UAE and that of American tertiary instructors in multinational, non-American student populations domestically in the US. The study investigated the use of reflexive photography and photo-elicitation interviews methods as both data collection approaches and possible cultivators of IS, as well as any variation in findings between the two participant groups. The study employed a mixed-methods approach involving surveys and semi-structured photo-elicitation interviews following a four-week reflexive photography project. Qualitative data were analyzed through the lens of a developmental framework and inductively through thematic analysis to capture fuller images of participants’ environments. Both groups of participants self-report fairly high IS, with the US-based group’s sensitivity averaging higher than the UAE-based group. Both groups, on average, showed slightly increased IS quantitatively following the reflexive photography project and photo-elicitation interviews, with the UAE-based group experiencing a slightly greater increase. This research involves a small number of participants; findings should be considered for indicative purposes only. Participants’ IS, when observed through the theoretical lens, indicate more progressive sensitivity among US-based participants. Thematic analysis of interview data reflects distinct teaching contexts faced by each participant group, with five and six themes emerging from the UAE- and US-based groups, respectively. This research is the first to the best of the author’s knowledge to investigate the IS of tertiary American faculty teaching internationally diverse student populations domestically and is also the first to compare differences in IS between this group and America
Meta-learning algorithms and applications
Meta-learning in the broader context concerns how an agent learns about their own learning, allowing them to improve their learning process. Learning how to learn is not only beneficial for humans, but it has also shown vast benefits for improving how machines learn. In the context of machine learning, meta-learning enables models to improve their learning process by selecting suitable meta-parameters that influence the learning. For deep learning specifically, the meta-parameters typically describe details of the training of the model but can also include description of the model itself - the architecture. Meta-learning is usually done with specific goals in mind, for example trying to improve ability to generalize or learn new concepts from only a few examples.
Meta-learning can be powerful, but it comes with a key downside: it is often computationally costly. If the costs would be alleviated, meta-learning could be more accessible to developers of new artificial intelligence models, allowing them to achieve greater goals or save resources. As a result, one key focus of our research is on significantly improving the efficiency of meta-learning. We develop two approaches: EvoGrad and PASHA, both of which significantly improve meta-learning efficiency in two common scenarios. EvoGrad allows us to efficiently optimize the value of a large number of differentiable meta-parameters, while PASHA enables us to efficiently optimize any type of meta-parameters but fewer in number.
Meta-learning is a tool that can be applied to solve various problems. Most commonly it is applied for learning new concepts from only a small number of examples (few-shot learning), but other applications exist too. To showcase the practical impact that meta-learning can make in the context of neural networks, we use meta-learning as a novel solution for two selected problems: more accurate uncertainty quantification (calibration) and general-purpose few-shot learning. Both are practically important problems and using meta-learning approaches we can obtain better solutions than the ones obtained using existing approaches. Calibration is important for safety-critical applications of neural networks, while general-purpose few-shot learning tests model's ability to generalize few-shot learning abilities across diverse tasks such as recognition, segmentation and keypoint estimation.
More efficient algorithms as well as novel applications enable the field of meta-learning to make more significant impact on the broader area of deep learning and potentially solve problems that were too challenging before. Ultimately both of them allow us to better utilize the opportunities that artificial intelligence presents
Learning recommender systems from biased user interactions
Recommender systems have been widely deployed to help users quickly find what they need from a collection of items. Predominant recommendation methods rely on supervised learning models to predict user ratings on items or the probabilities of users interacting with items. In addition, reinforcement learning models are crucial in improving long-term user engagement within recommender systems. In practice, both of these recommendation methods are commonly trained on logged user interactions and, therefore, subject to bias present in logged user interactions. This thesis concerns complex forms of bias in real-world user behaviors and aims to mitigate the effect of bias on reinforcement learning-based recommendation methods. The first part of the thesis consists of two research chapters, each dedicated to tackling a specific form of bias: dynamic selection bias and multifactorial bias. To mitigate the effect of dynamic selection bias and multifactorial bias, we propose a bias propensity estimation method for each. By incorporating the results from the bias propensity estimation methods, the widely used inverse propensity scoring-based debiasing method can be extended to correct for the corresponding bias. The second part of the thesis consists of two chapters that concern the effect of bias on reinforcement learning-based recommendation methods. Its first chapter focuses on mitigating the effect of bias on simulators, which enables the learning and evaluation of reinforcement learning-based recommendation methods. Its second chapter further explores different state encoders for reinforcement learning-based recommendation methods when learning and evaluating with the proposed debiased simulator
The opinions of science and mathematics teachers about beliefs, practices, and implementation of meaningful learning in Israel. A case study of Arab middle school(s)
Wydział Studiów EdukacyjnychWiele badań pokazuje, że przekonania nauczycieli dotyczące nauczania i uczenia się silnie oddziałują na ich praktykę zawodową. Celem tej pracy było zbadanie przekonań i praktyk nauczycieli przedmiotów ścisłych i matematyki w arabskich szkołach średnich w Izraelu w obliczy wdrażania nowej reformy edukacyjnej w tym kraju, silnie osadzonej na koncepcji meaningful learning. Zgodnie z tą koncepcją, uczniowie powinni być aktywni i zaangażowani w proces rozwiązywania problemów, którego rdzeniem jest szeroko ujmowany dialog pomiędzy uczestnikami procesu uczenia się. W badaniach wykorzystano strategię badań jakościowych. Prowadzono obserwacje w klasie, częściowo ustrukturyzowane wywiady oraz analizy dokumentów (m.in. planów lekcji, testów, arkuszy roboczych) i notatek terenowych. Uczestnikami badania było dwudziestu nauczycieli z trzech szkół średnich w społeczeństwie arabskim. Uzyskane dane pozwoliły zarysować obraz przekonań tych nauczycieli na temat meaningful learning oraz zidentyfikować sytuacje, które nauczyciele postrzegają jako realizację tej koncepcji. Praca kończy się rekomendacjami dotyczącymi dalszych etapów wdrażania reformy edukacji w Izraelu.The introduction of a new reform potentially challenges teachers’ beliefs and practices about teaching. This case study explores these challenges in the context of a new reform in Israel, where major educational reform has been undertaken. A considerable body of research, alternatively, advocates that teachers’ beliefs about teaching and learning affect their teaching practices and many aspects of their professional work. These beliefs and practices influence many factors on the contextual and teacher levels. Thus, this study aimed to investigate and understand Arab middle school science and mathematics teachers’ beliefs, practices, and implementation of meaningful learning in Israel. The resulting data served to construct a background picture regarding teachers’ beliefs on meaningful learning, classroom practices, and identifying situations that teachers perceived as the implementation of meaningful learning. The study found also that curricular demands, teacher perceptions of their students, pressures of time, assessment, crowded classrooms, lack of resources, workload, and inadequate teacher understanding of the components of meaningful learning inhibited student- centered instruction. Thus, along with the reformation of teachers, there should also be a reformation in the context of the learning atmosphere and infrastructures in tune with the
new reform’s intentions
Mapping the Focal Points of WordPress: A Software and Critical Code Analysis
Programming languages or code can be examined through numerous analytical lenses. This project is a critical analysis of WordPress, a prevalent web content management system, applying four modes of inquiry. The project draws on theoretical perspectives and areas of study in media, software, platforms, code, language, and power structures. The applied research is based on Critical Code Studies, an interdisciplinary field of study that holds the potential as a theoretical lens and methodological toolkit to understand computational code beyond its function. The project begins with a critical code analysis of WordPress, examining its origins and source code and mapping selected vulnerabilities. An examination of the influence of digital and computational thinking follows this. The work also explores the intersection of code patching and vulnerability management and how code shapes our sense of control, trust, and empathy, ultimately arguing that a rhetorical-cultural lens can be used to better understand code\u27s controlling influence. Recurring themes throughout these analyses and observations are the connections to power and vulnerability in WordPress\u27 code and how cultural, processual, rhetorical, and ethical implications can be expressed through its code, creating a particular worldview. Code\u27s emergent properties help illustrate how human values and practices (e.g., empathy, aesthetics, language, and trust) become encoded in software design and how people perceive the software through its worldview. These connected analyses reveal cultural, processual, and vulnerability focal points and the influence these entanglements have concerning WordPress as code, software, and platform. WordPress is a complex sociotechnical platform worthy of further study, as is the interdisciplinary merging of theoretical perspectives and disciplines to critically examine code. Ultimately, this project helps further enrich the field by introducing focal points in code, examining sociocultural phenomena within the code, and offering techniques to apply critical code methods
Relationship Between Country Culture, Country Demographics, and Restaurant Electronic Word-of-Mouth Valence Ratings
Researchers have documented that country culture and country demographics influence electronic word-of-mouth (eWOM) within various industries. Although past research has shown the importance of eWOM to restaurants as a measure of consumer satisfaction, researchers have not established the effect of country culture and country demographics on eWOM within the restaurant industry. Thus, the specific management problem addressed in this quantitative correlational study was the lack of knowledge and understanding regarding the relationship between country culture, country demographics, and restaurant eWOM valence ratings. Grounded in Hofstede’s cultural dimensions theory, the research questions addressed six measures of country culture, 12 measures of country demographics, and their relationship with restaurant eWOM valence ratings. With a purposive sample from the Yelp social media platform, eWOM ratings from 3,659 restaurants in 21 countries were analyzed with correlation analyses and multiple linear regression. Results indicated that a model of five variables and eight two-factor interactions statistically and significantly explained 14.4% of the variance in restaurant eWOM valence ratings. This study may promote positive social change by informing restaurant managers about which aspects of country culture and country demographics relate to restaurant eWOM valence ratings. Restaurant leaders may improve their eWOM response strategies by focusing on the most relevant country culture and country demographic constructs when developing eWOM communication
Virtual reality in destination branding
Abstract. Businesses might gain a competitive advantage by offering virtual tourism as it is still a novel phenomenon. Experiencing virtual reality, the user engages with the virtual content more in comparison with 2D media such as texts and pictures. The immersive aspect of virtual reality increases user engagement in the virtual environment. Therefore, the virtual experience raises higher interest towards the content. As virtual content engages the users more, it could be utilized for destination branding purposes. Destination marketers could offer virtual content in the form of virtual tourism in order to productize the intangible aspect of traveling. Virtual tourism could be also considered as a sample tool for marketing to experience a destination before potential travel.
The existing research covers mainly studies about virtual reality, virtual tourism and destination branding without combining these aspects. Therefore, this study focuses on researching the combination of these aspects. The purpose of the study is to define the uses of virtual reality in destination branding and the means of it. Also, the meaning of virtual tourism for destination brands is considered. To understand the researched phenomena even further, a theoretical framework will be provided to clarify the most essential theoretical (secondary data) aspects of the study. The theoretical framework will be later completed with empirical findings (primary data) to provide a whole diagram to support the examined phenomenon.
The chosen research method for the study is a qualitative approach to collect empirical data in support of related literature. Semi-structured interviews were selected for the qualitative research method. The criteria for selecting suitable interviewees were to select organizations that operate in the tourism field and implement destination branding. The interviews were performed with both public and private organizations. Also, the abductive aspect was applied as collected empirical data had a dialogue throughout the research with selected literature. The collected data was transcribed by the researcher and thematic analysis was applied to notice frequencies within the data. However, also infrequencies are presented.
The study results indicated that the operations regarding virtual reality varied in both public and private organizations. While public organizations provided virtual content among their other marketing operations, private organizations offered virtual content as their main purpose for their businesses. However, the joint consensus among the interviewees was to bring tourists on the spot to destinations. As by offering virtual content their ultimate purpose was marketing the destination. However, the study indicates that virtual tourism can be used for three purposes; (1) as a sample tool, (2) as a product, and (3) as an on-site added-value experience. The mentioned purposes jointly attract tourists to destinations in their various ways yet constantly aiming for destination branding
Enhancing Restaurant Dining Experience: Design and Evaluation of a Mobile App for Personalized Menu Item Selection in Restaurants
Picking the right food item from a restaurant menu can be challenging for people, specially for those who are unfamiliar with local cuisine and those with specific dietary requirements. Existing menus often lack essential information, making it difficult for diners to make quick and confident decisions. In this paper, we propose a mobile app that offers a user-friendly interface to allows users rank menu items based on their preferences and concerns. Using personalized ranking algorithms, the app analyzes the ingredients and nutritional content of menu items, providing users with valuable information to make informed choices. Preliminary tests suggest that the app is easy to use and effective in providing relevant information to users. Overall, the proposed system has the potential to improve the dining experience of individuals with various dietary needs and preferences
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