Employing Best Practices In Teacher Education: Faculty Perceptions Of Their Success And Their Needs In Preparing Teachers To Increase Student Achievement

This qualitative study focuses on the faculty engaged in the preparation of secondary teachers at North East University (NEU). It seeks to discover how they see themselves as professionals and assess their work preparing future teachers in Best Practices of teaching so that they can effectively teach all students, particularly low achievers. To achieve the goal of this study, I conducted semi-structured individual interviews with those faculty who are engaged in preparing teachers at the secondary program. Eight participants were interviewed for this study, among them six participants were fully engaged in the teacher preparation. Once I collected the data from the interviews, then I transcribed, coded, analyzed the data, and identified similarities, differences, patterns, and themes from the interviews. The findings of this study indicate that these faculty have a strong commitment to preparing outstanding teachers that is rooted in their belief in social justice and equality. They expressed they have dreams about their teaching, about their student-teachers and about their program. The faculty are highly confident of their ability to educate secondary teachers and believe that they make a difference in the academic performance of those children their graduates serve in the schools. This study also concluded that the teacher educators at NEU\u27s secondary program think they are successful in introducing Best Practices of teaching, especially helping their student-teachers in differentiating instructions, dealing with disabilities, teaching ELL students, employing technology in teaching, understanding diversity, culture and traditions, and preparing their student-teachers in examining issues relating to prejudice, discrimination, stereotyping, race, poverty, gender, social class and ethnicity

A mHealth Architecture for Diabetes Self-Management System

Recent advancement in smartphones coupled with the proliferation of data connectivity has resulted in increased interest and unprecedented growth in mobile applications for diabetes self-management. Nevertheless, a review of the literature highlights critical gaps between available functionality and user requirements and expectations. In this paper, we present a mHealth architecture of diabetes self-management system. The architecture has the following functionalities: automated data-entry through the use of wireless sensors; adherence to clinical guidelines; advanced statistical techniques for diabetes modeling and prediction; and advanced charting capabilities for data presentation and quality control

Active Learning for the Automation of Medical Systematic Review Creation

While systematic reviews (SRs) are positioned as an essential element of modern evidence-based medical practice, the creation of these reviews is resource intensive. To mitigate this problem there has been some attempts to leverage supervised machine learning to automate the article triage procedure. This approach has been proved to be helpful for updating existing SRs. However, this technique holds very little promise for creating new SRs because training data is rarely available when it comes to SR creation. In this research we propose an active machine learning approach to overcome this labeling bottleneck and develop a classifier for supporting the creation of systematic reviews. The results indicate that active learning based sample selection could significantly reduce the human effort and is viable technique for automating medical systematic review creation with very few training dataset

Information Technology for Evidence Based Medicine: Status and Future Direction

Evidence based medicine (EBM) refers to the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. This article presents systematic review of the use of information technology (IT) to support EBM with a particular emphasis on status and opportunities. Out of 2,490 papers initially scanned, 585 articles were included at the title level review. This was followed by an abstract review, which resulted in 196 articles. On full text scanning of the 196 articles, 69 articles met the inclusion criteria and were included in the final analyses. The key issues and potential for IT support for the practice of EBM are insufficient techniques to produce evidence in a computer interpretable format, insufficient research to combine the evidence from the multiple sources, inadequate techniques that automatically rate the literature and practice-based evidence, and integration of evidence at the clinician’s workflow

Opportunities for Business Intelligence and Big Data Analytics in Evidence Based Medicine

Evidence based medicine (EBM) is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. Each year, a significant number of research studies (potentially serving as evidence) are reported in the literature at an ever-increasing rate outpacing the translation of research findings into practice. Coupled with the proliferation of electronic health records, and consumer health information, researchers and practitioners are challenged to leverage the full potential of EBM. In this paper we present a research agenda for leveraging business intelligence and big data analytics in evidence based medicine, and illustrate how analytics can be used to support EBM

Advanced analytics for the automation of medical systematic reviews

While systematic reviews (SRs) are positioned as an essential element of modern evidence-based medical practice, the creation and update of these reviews is resource intensive. In this research, we propose to leverage advanced analytics techniques for automatically classifying articles for inclusion and exclusion for systematic reviews. Specifically, we used soft-margin polynomial Support Vector Machine (SVM) as a classifier, exploited Unified Medical Language Systems (UMLS) for medical terms extraction, and examined various techniques to resolve the class imbalance issue. Through an empirical study, we demonstrated that soft-margin polynomial SVM achieves better classification performance than the existing algorithms used in current research, and the performance of the classifier can be further improved by using UMLS to identify medical terms in articles and applying re-sampling methods to resolve the class imbalance issue

Using semi-supervised learning for the creation of medical systematic review: An exploratory analysis

In this research, we explore semi-supervised learning based classifiers to identify articles that can be included when creating medical systematic reviews (SRs). Specifically, we perform comparative study of various semi-supervised learning algorithm, and identify the best technique that is suited for SRs creation. We also aim to identify whether semisupervised learning technique with few labeled samples produce meaningful work saving for SRs creation. Through an empirical study, we demonstrate that semi-supervised classifiers are viable for selecting articles for systematic reviews and situations when only a few numbers of training samples are available

Using Semi-supervised Learning for the Creation of Medical Systematic Review: An exploratory Analysis

In this research, we explore semi-supervised learning based classifiers to identify articles that can be included when creating medical systematic reviews (SRs). Specifically, we perform comparative study of various semi-supervised learning algorithm, and identify the best technique that is suited for SRs creation. We also aim to identify whether semisupervised learning technique with few labeled samples produce meaningful work saving for SRs creation. Through an empirical study, we demonstrate that semi-supervised classifiers are viable for selecting articles for systematic reviews and situations when only a few numbers of training samples are available

Mobile Applications for Diabetes Self-Management: Status and Potential

Background:Advancements in smartphone technology coupled with the proliferation of data connectivity has resulted in increased interest and unprecedented growth in mobile applications for diabetes self-management. The objective of this article is to determine, in a systematic review, whether diabetes applications have been helping patients with type 1 or type 2 diabetes self-manage their condition and to identify issues necessary for large-scale adoption of such interventions.Methods:The review covers commercial applications available on the Apple App Store (as a representative of commercially available applications) and articles published in relevant databases covering a period from January 1995 to August 2012. The review included all applications supporting any diabetes self-management task where the patient is the primary actor.Results:Available applications support self-management tasks such as physical exercise, insulin dosage or medication, blood glucose testing, and diet. Other support tasks considered include decision support, notification/alert, tagging of input data, and integration with social media. The review points to the potential for mobile applications to have a positive impact on diabetes self-management. Analysis indicates that application usage is associated with improved attitudes favorable to diabetes self-management. Limitations of the applications include lack of personalized feedback; usability issues, particularly the ease of data entry; and integration with patients and electronic health records.Conclusions:Research into the adoption and use of user-centered and sociotechnical design principles is needed to improve usability, perceived usefulness, and, ultimately, adoption of the technology. Proliferation and efficacy of interventions involving mobile applications will benefit from a holistic approach that takes into account patients\u27 expectations and providers\u27 needs