A Tradesperson’s Transition to Vocational Technical (VT) Teaching

This qualitative study examined survey and interview data collected from tradespeople who transitioned to vocational technical (VT) teaching in regional vocational technical schools in Massachusetts. This study included two research questions that inquired about how tradespeople’s prior experiences, beliefs, and thoughts influenced or inspired them to pursue a transition to vocational technical (VT) teaching and about how their anticipated transitional experiences aligned with their actual transitional experiences. The survey phase included 170 respondents. Survey responses provided an overview of participants, which was integral in identifying four interview participants who were digitally recorded during one-to-one interview sessions. A multiple Case Study involving two of the interviewed VT teachers resulted, which compares their career-change experiences via vignettes and an analysis of themes across all data sets. The findings illuminate how their prior thoughts and experiences influenced their interest in teaching and their motivations for leaving their trade career to teach. In addition, findings reveal that previously acquired behaviors and trade skills did transfer to teaching, licensure requirements added tension to the transition, perceptions of teaching and school community experiences did not align with their actual experiences, and collegial interactions contributed to a smoother transition to the teaching profession. The findings reveal how a tradesperson’s thoughts, motivations, and prior experiences influenced their transitional experiences and the implications of the study are relevant to tradespeople considering a transition to VT teaching, departments of education and school leaders who establish support for novice VT teachers, and career counselors who advise tradespeople about career options

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Deep Open Representative Learning for Image and Text Classification

Title from PDF of title page viewed November 5, 2020Dissertation advisor: Yugyung LeeVitaIncludes bibliographical references (pages 257-289)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2020An essential goal of artificial intelligence is to support the knowledge discovery process from data to the knowledge that is useful in decision making. The challenges in the knowledge discovery process are typically due to the following reasons: First, the real-world data are typically noise, sparse, or derived from heterogeneous sources. Second, it is neither easy to build robust predictive models nor to validate them with such real-world data. Third, the `black-box' approach to deep learning models makes it hard to interpret what they produce. It is essential to bridge the gap between the models and their support in decisions with something potentially understandable and interpretable. To address the gap, we focus on designing critical representatives of the discovery process from data to the knowledge that can be used to perform reasoning. In this dissertation, a novel model named Class Representative Learning (CRL) is proposed, a class-based classifier designed with the following unique contributions in machine learning, specifically for image and text classification, i) The unique design of a latent feature vector, i.e., class representative, represents the abstract embedding space projects with the features extracted from a deep neural network learned from either images or text, ii) Parallel ZSL algorithms with class representative learning; iii) A novel projection-based inferencing method uses the vector space model to reconcile the dominant difference between the seen classes and unseen classes; iv) The relationships between CRs (Class Representatives) are represented as a CR Graph where a node represents a CR, and an edge represents the similarity between two CRs.Furthermore, we designed the CR-Graph model that aims to make the models explainable that is crucial for decision-making. Although this CR-Graph does not have full reasoning capability, it is equipped with the class representatives and their inter-dependent network formed through similar neighboring classes. Additionally, semantic information and external information are added to CR-Graph to make the decision more capable of dealing with real-world data. The automated semantic information's ability to the graph is illustrated with a case study of biomedical research through the ontology generation from text and ontology-to-ontology mapping.Introduction -- CRL: Class Representative Learning for Image Classification -- Class Representatives for Zero-shot Learning using Purely Visual Data -- MCDD: Multi-class Distribution Model for Large Scale Classification -- Zero Shot Learning for Text Classification using Class Representative Learning -- Visual Context Learning with Big Data Analytics -- Transformation from Publications to Ontology using Topic-based Assertion Discovery -- Ontology Mapping Framework with Feature Extraction and Semantic Embeddings -- Conclusion -- Appendix A. A Comparative Evaluation with Different Similarity Measure

Special Libraries, November 1975

Volume 66, Issue 11https://scholarworks.sjsu.edu/sla_sl_1975/1008/thumbnail.jp

Special Libraries, November 1975

Volume 66, Issue 11https://scholarworks.sjsu.edu/sla_sl_1975/1008/thumbnail.jp