Ohio Mental Health Consumer Outcomes System Report 20: Early Warning System for Identifying Youth at Risk of Treatment Failure

2009 ◽  
Keyword(s):  
Mental Health ◽  
At Risk ◽  
Treatment Failure ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Mental Health Consumer ◽  
Health Consumer ◽  
Youth At Risk ◽  
Risk Of Treatment

scholarly journals An Early Warning System to Detect At-Risk Students in Online Higher Education

2020 ◽  
Vol 10 (13) ◽  
pp. 4427 ◽  
Author(s):  
David Bañeres ◽  
M. Elena Rodríguez ◽  
Ana Elena Guerrero-Roldán ◽  
Abdulkadir Karadeniz
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Predictive Model ◽  
Early Warning ◽  
Early Warning System ◽  
Time Lag ◽  
At Risk Students ◽  
Warning System ◽  
Online Higher Education ◽  
Depth Analysis

Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

INTRODUCING THE FIRST WORLD WATCH LIST FOR DOMESTIC ANIMAL DIVERSITY

1994 ◽  
Vol 13 ◽  
pp. 3-10
Author(s):  
Ronan I. oftus
Keyword(s):  
At Risk ◽  
Genetic Resources ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Domestic Animal ◽  
Comprehensive List ◽  
Animal Diversity ◽  
First World ◽  
Watch List

SUMMARYThe recently published World Watch List for Domestic Animal Diversity (WWL-DAD) provides the first comprehensive list of endangered livestock breeds worldwide (FAO/LTNEP 1993). This document will function as a global early warning system to help prevent the erosion of livestock genetic resources. Seven species are covered, namely ass, buffalo, cattle, goat, horse, pig and sheep. Within these species, breeds at risk are defined as critical (The Critical Breeds List) or endangered (The Endangered Breeds List) based on the number of breeding females. Although the statistics for these seven species are still incomplete at the time of going to press, over 390 breeds are already known to be at risk.

Vulnerability index related to populations at-risk for landslides in the Brazilian Early Warning System (BEWS)

2020 ◽  
Vol 49 ◽  
pp. 101742 ◽  
Author(s):  
Mariane Carvalho de Assis Dias ◽  
Silvia Midori Saito ◽  
Regina Célia dos Santos Alvalá ◽  
Marcelo Enrique Seluchi ◽  
Tiago Bernardes ◽  
...  
Keyword(s):  
At Risk ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Vulnerability Index ◽  
Populations At Risk

ANALYSIS OF THE ACCURACY OF AN EARLY WARNING SYSTEM FOR LEARNERS AT-RISK: A CASE STUDY

2019 ◽  
Author(s):  
David Baneres ◽  
Abdulkadir Karadeniz ◽  
Ana-Elena Guerrero-Roldán ◽  
M. Elena Rodríguez-Gonzalez ◽  
Montse Serra
Keyword(s):  
At Risk ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System

scholarly journals Developing an early-warning system for spotting at-risk students by using eBook interaction logs

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Gökhan Akçapınar ◽  
Mohammad Nehal Hasnine ◽  
Rwitajit Majumdar ◽  
Brendan Flanagan ◽  
Hiroaki Ogata
Keyword(s):  
At Risk ◽  
Early Warning ◽  
Early Warning System ◽  
At Risk Students ◽  
Warning System

Mapping characteristics of at-risk population to disasters in the context of Brazilian early warning system

2019 ◽  
Vol 41 ◽  
pp. 101326 ◽  
Author(s):  
Regina Célia dos Santos Alvalá ◽  
Mariane Carvalho de Assis Dias ◽  
Silvia Midori Saito ◽  
Cláudio Stenner ◽  
Cayo Franco ◽  
...  
Keyword(s):  
At Risk ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Risk Population
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