A Comparative Study: Existing Vocational Trade and Industrial Teacher Certification Requirments and Practices Recomended by Authorities in the Field

This thesis was a comparative study of vocational trade and industrial teacher certification requirements as they exist in the various states and practices recommended by authorities in the field. The author composed a questionnaire including relevant aspects of vocational trade and industrial teacher certification requirements. The questionnaires sent to state directors of vocational trade and industrial education asked factual information of teacher certification in their respective states and also asked their opinions concerning vocational trade and industrial teacher certification requirements. The questionnaires sent to industrial teacher education department chairmen asked only their opinions concerning vocational T & I teacher certification requirements. In addition, both the state directors and the department chairmen ·were asked their opinions concerning various aspects of a baccalaureate degree program in vocational trade and industrial teacher education. The existing vocational trade and industrial teacher certification requirements and the recommendations of the two independent sample groups were recorded in tabular form for easy comparisons. Sixteen statistical tests were made of the recommendations between the state directors and department chairmen. Nine of the sixteen tests were statistically significant. Concerning existing vocational T & I teacher certification requirements, very little emphasis is placed upon teacher training and formal education with the opposite being true for occupational experience. The recommended practices tended to emphasize more teacher training and formal education and less occupational experience

1970 State of the University Address

Annual address delivered by the president of Illinois State University discussing the university\u27s accomplishments and plans for the future.https://ir.library.illinoisstate.edu/state-of-the-university/1003/thumbnail.jp

Rural North Dakota\u27s Oil Boom, The Impact On Social Services, And The Implications For The Social Work Profession

Technological developments within the oil and gas industry (namely horizontal drilling and hydraulic fracturing, or fracking ) combined with record-high average oil prices have propelled North Dakota into the midst of the nation\u27s largest oil boom in decades. Information about the boom has been dominated by the media, while scholarly attention--especially about the social impacts--has been unfortunately limited. Accordingly, the author (an MSW student) and two professors with the Department of Social Work at the University of North Dakota conducted a focus group with the state\u27s county social service directors to explore the pressures and challenges faced by the social service delivery system. While housing and other related challenges dominated the focus group discussion, the county directors also spoke about the boom\u27s benefits and the challenges associated with finding effective solutions. The emergent themes from the focus group were juxtaposed against a variety of independent sources, including archival data and government reports. This allowed the researchers to examine how the directors\u27 narratives aligned with available data. The results highlight social and environmental justice issues that social workers are uniquely qualified to address. The study concludes with implications for the social work profession by recommending future areas of research and suggesting interventions to mitigate the impacts of North Dakota\u27s oil boom

The Chief Executive Officer of Charter Management Organizations and their Perspective on Instructional Leadership to Improve Student Achievement

The purpose of the study is to investigate the relationship between academic emphasis and executive leadership from the perspective of a California charter management organization (CMO) chief executive officer (CEO). Executive leaders in California CMOs have a unique perspective that needs investigated. They experience educational leadership differently depending upon their individual backgrounds, educational experiences, and the families they serve. This study seeks to understand the experiences of a specific group of executive leaders and how they define academic emphasis in their CMO. The theoretical framework used to interpret the research findings was instructional leadership. The framework effectively built a lens for the reader to conceptualize the research of this study. The theoretical framework worked to guide and frame interpretation of respondent data. The research served to inform the research questions, not answer them explicitly. The research used a qualitative case study design approach focused on the story of the lived experience from the individual. The design aspired to interpret meanings and experiences from responses to uncover deep and detailed understanding. A CMO CEO responded to semi-structured interview questions. The interview protocol consisted of various question types: open-ended questions, follow-up questions, and classification questions. As transformational and educational leadership collide in Dr. Viviane Robinson’s current instructional leadership framework, the capability that is not explicitly presented is the ability for school leaders to build capacity for equity consciousness in all teammates in their organization and/or school. The findings from this study suggested that a fourth leadership capability is emerging in Dr. Robinson’s framework. There is a critical need for organization and school leaders to be equipped with the skills to seed an equity consciousness across teammates and other stakeholders

The Size Conundrum: Why Online Knowledge Markets Can Fail at Scale

In this paper, we interpret the community question answering websites on the StackExchange platform as knowledge markets, and analyze how and why these markets can fail at scale. A knowledge market framing allows site operators to reason about market failures, and to design policies to prevent them. Our goal is to provide insights on large-scale knowledge market failures through an interpretable model. We explore a set of interpretable economic production models on a large empirical dataset to analyze the dynamics of content generation in knowledge markets. Amongst these, the Cobb-Douglas model best explains empirical data and provides an intuitive explanation for content generation through concepts of elasticity and diminishing returns. Content generation depends on user participation and also on how specific types of content (e.g. answers) depends on other types (e.g. questions). We show that these factors of content generation have constant elasticity---a percentage increase in any of the inputs leads to a constant percentage increase in the output. Furthermore, markets exhibit diminishing returns---the marginal output decreases as the input is incrementally increased. Knowledge markets also vary on their returns to scale---the increase in output resulting from a proportionate increase in all inputs. Importantly, many knowledge markets exhibit diseconomies of scale---measures of market health (e.g., the percentage of questions with an accepted answer) decrease as a function of number of participants. The implications of our work are two-fold: site operators ought to design incentives as a function of system size (number of participants); the market lens should shed insight into complex dependencies amongst different content types and participant actions in general social networks.Comment: The 27th International Conference on World Wide Web (WWW), 201

The Expatriate Experience: A Case Study Of A Pharmaceutical Company In The European Union

This study examines the Cross-Cultural adjustment of expatriates entering into Belgium corporate and social society. The research was petitioned by the Organization of People Development of a multinational pharmaceutical corporation based within the European (EU). Concern existed regarding retention rates of expatriates and the formidable costs of failed expatriate assignments. Thirty upper level managers expressed an interest in participating in a series of interviews focused on their personal and professional experiences as expatriates. The methodological approach was qualitative in nature. The technique used was informal, in-depth interviews in tandem with a quantitatively based survey instrument to collect demographic data and satisfaction rates. Results revealed significant dissatisfaction with a number of dependent variables correlated with successful integration, i.e. personal and professional satisfaction and spousal or partner satisfaction. Narrative descriptions of acculturation adjustment provide depth into the challenges of expatriate’s assignments. Researcher relied on Hofstede’s (2001) cultural model for a greater understanding of “Culture’s Consequences.

Copper carbenes alkylate guanine chemoselectively through a substrate directed reaction

Cu(I) carbenes derived from α-diazocarbonyl compounds lead to selective alkylation of the O6 position in guanine (O6-G) in mono- and oligonucleotides. Only purine-type lactam oxygens are targeted – other types of amides or lactams are poorly reactive under conditions that give smooth alkylation of guanine. Mechanistic studies point to N7G as a directing group that controls selectivity. Given the importance of O6-G adducts in biology and biotechnology we expect that Cu(I)-catalyzed O6-G alkylation will be a broadly used synthetic tool. While the propensity for transition metals to increase redox damage is well-appreciated, our results suggest that transition metals might also increase the vulnerability of nucleic acids to alkylation damage

mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs

Modular vision-language models (Vision-LLMs) align pretrained image encoders with (pretrained) large language models (LLMs), representing a computationally much more efficient alternative to end-to-end training of large vision-language models from scratch, which is prohibitively expensive for most. Vision-LLMs instead post-hoc condition LLMs to `understand' the output of an image encoder. With the abundance of readily available high-quality English image-text data as well as monolingual English LLMs, the research focus has been on English-only Vision-LLMs. Multilingual vision-language models are still predominantly obtained via expensive end-to-end pretraining, resulting in comparatively smaller models, trained on limited multilingual image data supplemented with text-only multilingual corpora. In this work, we present mBLIP, the first multilingual Vision-LLM, which we obtain in a computationally efficient manner -- on consumer hardware using only a few million training examples -- by leveraging a pretrained multilingual LLM. To this end, we \textit{re-align} an image encoder previously tuned to an English LLM to a new, multilingual LLM -- for this, we leverage multilingual data from a mix of vision-and-language tasks, which we obtain by machine-translating high-quality English data to 95 languages. On the IGLUE benchmark, mBLIP yields results competitive with state-of-the-art models. Moreover, in image captioning on XM3600, mBLIP (zero-shot) even outperforms PaLI-X (a model with 55B parameters). Compared to these very large multilingual vision-language models trained from scratch, we obtain mBLIP by training orders of magnitude fewer parameters on magnitudes less data. We release our model and code at \url{https://github.com/gregor-ge/mBLIP}

Large-eddy simulation and experimental study of heat transfer, nitric oxide emissions and combustion instability in a swirled turbulent high-pressure burner

Nitric oxide formation in gas turbine combustion depends on four key factors: flame stabilization, heat transfer, fuel-air mixing and combustion instability. The design of modern gas turbine burners requires delicate compromises between fuel efficiency, emissions of oxides of nitrogen (NOx) and combustion stability. Burner designs allowing substantial NOx reduction are often prone to combustion oscillations. These oscillations also change the NOx fields. Being able to predict not only the main species field in a burner but also the pollutant and the oscillation levels is now a major challenge for combustion modelling. This must include a realistic treatment of unsteady acoustic phenomena (which create instabilities) and also of heat transfer mechanisms (convection and radiation) which control NOx generation. In this work, large-eddy simulation (LES) is applied to a realistic gas turbine combustion chamber configuration where pure methane is injected through multiple holes in a cone-shaped burner. In addition to a non-reactive simulation, this article presents three reactive simulations and compares them to experimental results. The first reactive simulation neglects effects of cooling air on flame stabilization and heat losses by radiation and convection. The second reactive simulation shows how cooling air and heat transfer affect nitric oxide emissions. Finally, the third reactive simulation shows the effects of combustion instability on nitric oxide emissions. Additionally, the combustion instability is analysed in detail, including the evaluation of the terms in the acoustic energy equation and the identification of the mechanism driving the oscillation. Results confirm that LES of gas turbine combustion requires not only an accurate chemical scheme and realistic heat transfer models but also a proper description of the acoustics in order to predict nitric oxide emissions and pressure oscillation levels simultaneousl

Towards high quality, scalable education: Techniques in automated assessment and probabilistic user behavior modeling

There are two primary challenges for instructors in offering a high-quality course at large scale. The first is scaling educational experiences to such a large audience. The second major challenge encountered is that of enabling adaptivity of the educational experience. This thesis addresses both major challenges in the way of high-quality scalable education by developing new techniques for large-scale automated assessment (for addressing scalability) and developing new models for interpretable user behavior analysis in educational environments for improving the quality of interaction via personalized education. Specifically, I perform a study of automated assessment of complex assignments where I explore the effectiveness of different types of features in a feasibility study. I argue for re-framing automated assessment techniques in these more complex contexts as a ranking problem, and provide a systematic approach for integrating expert, peer, and automated assessment techniques via an active-learning-to-rank formulation that outperforms a traditional randomized training solution. I also present the design and implementation of CLaDS---a Cloud-based Lab for Data Science---to enable students to engage with real-world data science problems at-scale with minimal cost ($7.40/student). I discuss our experience with deploying seven major text data assignments for students in both on-campus and online courses and show that the general infrastructure of CLaDS can be used to efficiently deliver a wide range of hands-on data science assignments. Understanding student behavior is necessary for improving the quality of scalable education through adaptivity. To this end, I present two general user behavior models for analyzing student interaction log data to understand student behavior. The first focuses on the discovery and analysis of action-based roles in community question answering (CQA) platforms using a generative model called the MDMM behavior model. I show interesting distinctions within CQA communities in question-asking behavior (where two distinct types of askers can be identified) and answering behavior (where two distinct roles surrounding answers emerge). Second, I find that where there are statistically significant differences in health metrics across topical groups on StackExchange, there are also statistically significant differences in behavior compositions, suggesting a relationship between behavior composition and health. Third, I show that the MDMM behavior model can be used to demonstrate similar but distinct evolutionary patterns between topical groups. The second model focuses on discovering temporal action patterns of learners in Coursera MOOCs. I present a two-layer hidden Markov model (2L-HMM) to extract a multi-resolution summary of user behavior patterns and their evolution, and show that these patterns can be used to extract latent features that correlate with educational outcomes. Finally, I develop the Piazza Educational Role Mining (PERM) system to close the gap between theory and practice by providing an easy-to-use web-based interface for leveraging probabilistic user behavior models on Piazza CQA interaction data. PERM allows instructors to easily crawl their courses and run subsequent MDMM behavior analyses on them. Analyses provide instructors with insight into the common user behavior patterns (roles) uncovered by plotting their action distributions in a browser. PERM enables instructors to perform deep-dives into an individual role by viewing the concrete sessions that have been assigned a specific role by the model, along with each session's individual actions and associated content. This allows instructors to flexibly combine data-driven statistical inference (through the MDMM behavior model) with a qualitative understanding of the behavior within a role. Finally, PERM develops a model of individual users as mixtures over the discovered roles, which instructors can also deep-dive into to explore exactly what individual users were doing on the platform