Perceptions of Coding Instruction in K-12 Archdiocese of Los Angeles Catholic Schools

Traditional pedagogy offers students opportunities to enhance various skills and acquire content knowledge; however, additional steps can be taken to enhance student achievement, prepare them for future occupations, and bridge the divide in access to technology. A curriculum that integrates coding instruction affords students the opportunity to augment their collaboration, communication, creative thinking, and problem-solving skills. This is especially crucial for traditionally marginalized populations who have experienced inequitable access to technology. Nevertheless, coding is not integrated in schools in different domains, including Catholic institutions in the Archdiocese of Los Angeles (ADLA). This dissertation used a descriptive and inferential quantitative methodology to survey K–12 Catholic school teachers’, administrators’, and STEM directors’ understanding of what coding entails, assess their perceptions of coding’s potential to enrich student achievement, to prepare them for future occupations, and diversify STEM representation both in academics and in the workplace, and evaluate the potential link between educator epistemology and pedagogy with the penchant to incorporate coding instruction and the constructionist framework in the classroom. The largest diocese of the country, the ADLA, was the sole focus of this study and the data demonstrated participants have a relatively limited understanding of what coding entails, but they do believe it results in various benefits for students. Nevertheless, their epistemology and pedagogy are not ripe for constructionism to take hold in the classroom to facilitate coding

Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data

A Case Study on the Efficacy of STEM Pedagogy in Central New York State: Examining STEM Engagement Gaps Affecting Outcomes for High School Seniors and Post-2007 Educational Leadership Interventions to Reinforce STEM Persistence with Implications of STEM Theoretic Frameworks on Artificial Intelligence / Machine Learning

STEM (science, technology, engineering, and mathematics) has gained significant notoriety and momentum in recent years. STEM literacy highlights the vital connection between an educated STEM workforce and U.S. national prosperity and leadership. STEM educational and job placement goals have been a national priority for over the past 20 years. However, the STEM gap is widening—contributing to increasing STEM pipeline leakage and the social injustice milieu of a noncompetitive workforce— undermining efforts to create prosperity and sustain global leadership. The pace of STEM jobs filled lags the rate of technological advancement and the surges in skilled STEM labor demand. The aggregate disparity over time has troubling implications. The purpose of the study was to examine the STEM gap touchpoints for a Central New York high school during the transition period upon entering college or the workforce. A qualitative case study used Lesh’s translation model as a research framework. A semi-structured, focus group protocol was employed to gain a fresh perspective on the STEM gap problem and identify purposeful interventions. A major finding was the slow pace of adopting institutional reforms that replaces standardscompetency-based learning with progressive application- and outcome-based pedagogy. The study has implications for school districts, secondary schools, and higher education teacher preparedness programs in STEM pedagogy and curriculum development. A knowledge-based, progressive STEM theoretic framework with pedagogical scaffolding is conceptualized rooted in artificial intelligence and machine learning. The study presents recommendations for school districts, secondary education teachers, state education and legislative leaders, higher education institutions, and future research

BBC'22

The International Conference BBC'22 aims to provide an opportunity for all academic and non-academics to share their personal experiences and projects, presenting their contributions and getting feedback from other attendees.info:eu-repo/semantics/publishedVersio

Regulating by Robot: Administrative Decision Making in the Machine-Learning Era

Machine-learning algorithms are transforming large segments of the economy, underlying everything from product marketing by online retailers to personalized search engines, and from advanced medical imaging to the software in self-driving cars. As machine learning’s use has expanded across all facets of society, anxiety has emerged about the intrusion of algorithmic machines into facets of life previously dependent on human judgment. Alarm bells sounding over the diffusion of artificial intelligence throughout the private sector only portend greater anxiety about digital robots replacing humans in the governmental sphere. A few administrative agencies have already begun to adopt this technology, while others have the clear potential in the near-term to use algorithms to shape official decisions over both rulemaking and adjudication. It is no longer fanciful to envision a future in which government agencies could effectively make law by robot, a prospect that understandably conjures up dystopian images of individuals surrendering their liberty to the control of computerized overlords. Should society be alarmed by governmental use of machine learning applications? We examine this question by considering whether the use of robotic decision tools by government agencies can pass muster under core, time-honored doctrines of administrative and constitutional law. At first glance, the idea of algorithmic regulation might appear to offend one or more traditional doctrines, such as the nondelegation doctrine, procedural due process, equal protection, or principles of reason-giving and transparency. We conclude, however, that when machine-learning technology is properly understood, its use by government agencies can comfortably fit within these conventional legal parameters. We recognize, of course, that the legality of regulation by robot is only one criterion by which its use should be assessed. Obviously, agencies should not apply algorithms cavalierly, even if doing so might not run afoul of the law, and in some cases, safeguards may be needed for machine learning to satisfy broader, good-governance aspirations. Yet in contrast with the emerging alarmism, we resist any categorical dismissal of a future administrative state in which key decisions are guided by, and even at times made by, algorithmic automation. Instead, we urge that governmental reliance on machine learning should be approached with measured optimism over the potential benefits such technology can offer society by making government smarter and its decisions more efficient and just

Curiosity and experience design: developing the desire to know and explore in ways that are sociable, embodied and playful

Curiosity, as a strong motivator for exploration and discovery, has long been an underexplored but important emotional response in relation to technology. This research considers that it has great potential to improve many aspects of the user experience, especially in today’s screen-saturated context. However, engaging curiosity by novelty and uncertainty may exhaust attentional strength and challenge usability. Thus, the purpose of this research is to find ways to foster the human trait of curiosity and avoid its negative effects. To gain an in-depth understanding of curiosity, the first chapter reviews cross-disciplinary literature to expand its role in improving user experience. This ranges from serving as an attention grabber to including the values that contribute to human survival, thriving, emotional resilience, and personal development. The second chapter identifies problems in the current curiosity-provoking design methods. The chapter also emphasises design for supporting active curiosity and avoiding the creation of purely novel stimuli. This approach is to encourage active curiosity to develop. To this end, the research proceeds to conduct observational studies at a museum to broaden our understanding of factors that influence people’s curiosity and exploration within a screen-mediated context. Based on these observations, I identified that there are three conceptual elements: sociability, embodiment, and playfulness. Through theoretical discussion and reflection upon the design examples, subsequent three chapters explore the relationship between curiosity and each conceptual element. The chapters also suggest several design approaches that embrace curiosity in relation to its social, embodied, and playful nature. These include creating a sense of co-curiosity, allowing the use of covert and overt curiosity-satisfying strategies, increasing bodily exploration affordances of the screen for linking curiosity with embodiment, using metaphors of the body-screen relationship, and developing possibilities and adding enchanting effects for eliciting playfulness to enrich curiosity. In essence, this research enhances our understanding of the user experience from the perspective of curiosity, and these design suggestions also help to embrace users’ active curiosity in developing sociable, embodied, and playful well-being in the age of ubiquitous screens

Regulating by Robot: Administrative Decision Making in the Machine-Learning Era

Machine-learning algorithms are transforming large segments of the economy, underlying everything from product marketing by online retailers to personalized search engines, and from advanced medical imaging to the software in self-driving cars. As machine learning’s use has expanded across all facets of society, anxiety has emerged about the intrusion of algorithmic machines into facets of life previously dependent on human judgment. Alarm bells sounding over the diffusion of artificial intelligence throughout the private sector only portend greater anxiety about digital robots replacing humans in the governmental sphere. A few administrative agencies have already begun to adopt this technology, while others have the clear potential in the near-term to use algorithms to shape official decisions over both rulemaking and adjudication. It is no longer fanciful to envision a future in which government agencies could effectively make law by robot, a prospect that understandably conjures up dystopian images of individuals surrendering their liberty to the control of computerized overlords. Should society be alarmed by governmental use of machine learning applications? We examine this question by considering whether the use of robotic decision tools by government agencies can pass muster under core, time-honored doctrines of administrative and constitutional law. At first glance, the idea of algorithmic regulation might appear to offend one or more traditional doctrines, such as the nondelegation doctrine, procedural due process, equal protection, or principles of reason-giving and transparency. We conclude, however, that when machine-learning technology is properly understood, its use by government agencies can comfortably fit within these conventional legal parameters. We recognize, of course, that the legality of regulation by robot is only one criterion by which its use should be assessed. Obviously, agencies should not apply algorithms cavalierly, even if doing so might not run afoul of the law, and in some cases, safeguards may be needed for machine learning to satisfy broader, good-governance aspirations. Yet in contrast with the emerging alarmism, we resist any categorical dismissal of a future administrative state in which key decisions are guided by, and even at times made by, algorithmic automation. Instead, we urge that governmental reliance on machine learning should be approached with measured optimism over the potential benefits such technology can offer society by making government smarter and its decisions more efficient and just