scholarly journals Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics

2018 ◽  
Vol 5 (3) ◽  
Author(s):  
Michael Brown ◽  
R. Matthew DeMonbrun ◽  
Stephanie Teasley

In this study, we develop and test four measures for conceptualizing the potential impact of co-enrollment in different courses on students’ changing risk for academic difficulty and recovery from academic difficulty in a focal course. We offer four predictors, two related to instructional complexity and two related to structural complexity (the organization of the curriculum) that highlight different trends in student experience of the focal course. Course difficulty, discipline of major, time in semester, and simultaneous difficulty across courses were all significantly related to entering a moderate and high-risk classification in the early warning system (EWS). Course difficulty, discipline of major, and time in semester were related to exiting academic difficulty classifications. We observe a snowball effect, whereby students who are experiencing difficulty in the focal course are at increased risk of experiencing difficulty in their other courses. Our findings suggest that different metrics may be needed to identify risk for academic difficulty and recovery from academic difficulty. Our results demonstrate what a more holistic assessment of academic functioning might look like in early warning systems and course recommender systems, and suggest that academic planners consider the relationship between course co-enrollment and student academic success.

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guangju Li

Banks, financial, and credit institutions encountering the weakening financial system and increased risk factors cause high inflation and great losses for an economy. Detecting financial risks in advance could help financial institutions avoid losses, and the financial system could be eventually affected less. Early warning systems for banks could be helpful to identify financial risks and take measures to deal with hazardous situations. Various approaches have already been put forward. However, inaccuracy issues in risk detection are one of the main issues. Combining semantic hierarchy with the GMDH neural network to predict financial risks is proposed. A semantic hierarchy approach based on converting risk-related values and picking influential variables could be practical in risk detection. Besides, the GMDH algorithm utilizing neural networks based on available data has the capability of predicting possible risks that could occur in the future. The outcomes of the proposed method when compared to non-data mining methods suggest that it improves accuracy by almost 20%.


2019 ◽  
Vol 13 (4) ◽  
pp. 709-712 ◽  
Author(s):  
Krzysztof Goniewicz ◽  
Frederick M. Burkle

ABSTRACTObjectiveThe increased risk of mass accidents or major catastrophes taking place necessitates the organization of remedial measures to help protect against these unusual events and adequate preparation in order to minimize their effects. One such initiative is the early notification of residents within a specific area about the risk of a particular calamity. Nowadays, the prevalence of mobile devices enables the installation of various mobile applications allowing for the communication and receiving of information about potential dangers. In many countries there are variously developed systems of notification in place based specifically on text messages.MethodsCurrently, new laws introduced in Poland establish that it is the obligation of operators of mobile networks to send text messages to all customers of these networks who are within the area where there is a serious risk of a catastrophe. Such messages are in the form of a short alert, to be sent only in extraordinary situations when there is an immediate threat to health or life. The alert is intended to help in the avoidance of danger or to mitigate its impact.ResultsThis article presents the potential implementation of the early warning system based on text message alerts in Poland, and in particular focuses on decreasing the risks associated with natural disasters.ConclusionsWhile early text messaging is essential to disaster communications and mitigation, the article further states that means must be found to ensure equal access to the most vulnerable populations and all those, vulnerable and not, who do not have immediate access to text messaging systems. (Disaster Med Public Health Preparedness. 2019;13:709–712)


2013 ◽  
Vol 13 (1) ◽  
pp. 85-90 ◽  
Author(s):  
E. Intrieri ◽  
G. Gigli ◽  
N. Casagli ◽  
F. Nadim

Abstract. We define landslide Early Warning Systems and present practical guidelines to assist end-users with limited experience in the design of landslide Early Warning Systems (EWSs). In particular, two flow chart-based tools coming from the results of the SafeLand project (7th Framework Program) have been created to make them as simple and general as possible and in compliance with a variety of landslide types and settings at single slope scale. We point out that it is not possible to cover all the real landslide early warning situations that might occur, therefore it will be necessary for end-users to adapt the procedure to local peculiarities of the locations where the landslide EWS will be operated.


2010 ◽  
Vol 10 (11) ◽  
pp. 2215-2228 ◽  
Author(s):  
M. Angermann ◽  
M. Guenther ◽  
K. Wendlandt

Abstract. This article discusses aspects of communication architecture for early warning systems (EWS) in general and gives details of the specific communication architecture of an early warning system against tsunamis. While its sensors are the "eyes and ears" of a warning system and enable the system to sense physical effects, its communication links and terminals are its "nerves and mouth" which transport measurements and estimates within the system and eventually warnings towards the affected population. Designing the communication architecture of an EWS against tsunamis is particularly challenging. Its sensors are typically very heterogeneous and spread several thousand kilometers apart. They are often located in remote areas and belong to different organizations. Similarly, the geographic spread of the potentially affected population is wide. Moreover, a failure to deliver a warning has fatal consequences. Yet, the communication infrastructure is likely to be affected by the disaster itself. Based on an analysis of the criticality, vulnerability and availability of communication means, we describe the design and implementation of a communication system that employs both terrestrial and satellite communication links. We believe that many of the issues we encountered during our work in the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) on the design and implementation communication architecture are also relevant for other types of warning systems. With this article, we intend to share our insights and lessons learned.


2019 ◽  
Vol 1 (1) ◽  
pp. 194-202
Author(s):  
Adrian Costea

Abstract This paper assesses the financial performance of Romania’s non-banking financial institutions (NFIs) using a neural network training algorithm proposed by Kohonen, namely the Self-Organizing Maps algorithm. The algorithm takes the financial dataset and positiones each observation into a self-organizing map (a two-dimensional map) which can be latter used to visualize the trajectories of an individual NFI and explain it based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. Further, we use the map as an early-warning system that would accurately forecast the NFIs future performance (whether they would stay or be eliminated from the NFI’s Special Register three quarters into the future). The results are promising: the model is able to correctly predict NFIs’ performance movements. Finally, we compared the results of our SOM-based model with those obtained by applying a multivariate logit-based model. The SOM model performed worse in discriminating the NFIs’ performance: the performance classes were not clearly defined and the model lacked the interpretability of the results. In the contrary, the multivariate logit coefficients have nice interpretability and an individual default probability estimate is obtained for each new observation. However, we can benefit from the results of both techniques: the visualization capabilities of the SOM model and the interpretability of multivariate logit-based model.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jan Černý ◽  
Martin Potančok ◽  
Elias Castro Hernandez

PurposeThe study aims to expand on the concept of an early warning system (EWS) by introducing weak-signal detection, human-in-the-loop (HIL) verification and response tuning as integral parts of an EWS's design.Design/methodology/approachThe authors bibliographically highlight the evolution of EWS over the last 30+ years, discuss instances of EWSs in various types of organizations and industries and highlight limitations of current systems.FindingsProposed system to be used in the transforming of weak signals to early warnings and associated weak/strong responses.Originality/valueThe authors contribute to existing literature by presenting (1) novel approaches to dealing with some of the well-known issues associated with contemporary EWS and (2) an event-agnostic heuristic for dealing with weak signals.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0513.


2020 ◽  
Vol 122 (14) ◽  
pp. 1-24
Author(s):  
Joanna Hornig Fox ◽  
Robert Balfanz

Background/Context Over the past decade early warning systems which use predictive indicators to identify students in need of additional supports to stay on track to high school graduation have spread from a few schools to most states. There is now a growing interest in extending the utility of early warning systems from high school graduation to post-secondary readiness. Purpose/Objective/Research Question/Focus of Study Report on initial findings and insights from a three year effort to build a learning community of early adopters of early warning systems for high school graduation to figure out how they can be extended to keep students on track to post-secondary success. Intervention/Program/Practice A key outcome of the learning community which included 150 representatives of K-12 local and state school systems, as well as non-profits and institutions of higher education was the development a framework for keeping students on track to post-secondary success, called Pathways to Adult Success which has four main components (1) how to use Early Warning Systems to support postsecondary success, (2) how to provide better postsecondary navigation and guidance supports to all students, (3) how to develop cross-sector collaborations, and (4) how to improve data use and data systems. Research Design Participant-observation supported by interviews and surveys Conclusions/Recommendations The PAS learning community assembled a multi-part framework of recommendations and guideposts to help states, schools, and districts increase and improve pathways to adult success for all youth. This framework extends the early warning approach to support bridges to postsecondary outcomes, while acknowledging the need to improve and expand navigation and guidance supports for all students, and to increase cross-sector collaborations to improve and expand existing pathways. Finally, it stresses the power of and need for data, data systems, and data use to drive all elements of the Framework. The remaining challenge will be creating the conditions which enable its widespread implementation.


2022 ◽  
pp. 195-216
Author(s):  
Dejan Vasović ◽  
Ratko Ristić ◽  
Muhamed Bajrić

The level of sustainability of a modern society is associated with the ability to manage unwanted stressors from the environment, regardless of origin. Torrential floods represent a hydrological hazard whose frequency and intensity have increased in recent years, mainly due to climate changes. In order to effectively manage the risks of torrents, it is necessary to apply early warning systems, since torrential floods are formed very quickly, especially on the watercourses of a small catchment area. The early warning system is part of a comprehensive torrential flood risk management system, seen as a technical entity for the collection, transformation, and rapid distribution of data. Modern early warning systems are the successors of rudimentary methods used in the past, and they are based on ICT and mobile applications developed in relation to the requirements of end users. The chapter presents an analysis of characteristic examples of the use. The main conclusion of the chapter indicates the need to implement early warning systems in national emergency management structures.


Author(s):  
Filiz Eryılmaz

International organizations as private sector institutions started to develop Early Warning System [EWS] models aiming to anticipate whether and when individual countries can collide with a financial crisis. EWS models can be made most useful to help sustain global growth and maintain financial stability, especially in light of the lessons learned from the current and past crises. This paper proposes Early Warning Systems (EWS) for Turkish Currency and Banking Crisis in 2000 and 2001. To that end “KLR model” or “signaling window” approach developed by Kaminski, Lorezondo and Reinhart (1998) is testified in the empirical part of this research and applied to a sample of Turkey macroeconomic data for the 1998-2003 monthly periods.


2019 ◽  
Vol 100 (6) ◽  
pp. 1011-1027 ◽  
Author(s):  
Chris Funk ◽  
Shraddhanand Shukla ◽  
Wassila Mamadou Thiaw ◽  
James Rowland ◽  
Andrew Hoell ◽  
...  

AbstractOn a planet with a population of more than 7 billion, how do we identify the millions of drought-afflicted people who face a real threat of livelihood disruption or death without humanitarian assistance? Typically, these people are poor and heavily dependent on rainfed agriculture and livestock. Most live in Africa, Central America, or Southwest Asia. When the rains fail, incomes diminish while food prices increase, cutting off the poorest (most often women and children) from access to adequate nutrition. As seen in Ethiopia in 1984 and Somalia in 2011, food shortages can lead to famine. Yet these slow-onset disasters also provide opportunities for effective intervention, as seen in Ethiopia in 2015 and Somalia in 2017. Since 1985, the U.S. Agency for International Development’s Famine Early Warning Systems Network (FEWS NET) has been providing evidence-based guidance for effective humanitarian relief efforts. FEWS NET depends on a Drought Early Warning System (DEWS) to help understand, monitor, model, and predict food insecurity. Here we provide an overview of FEWS NET’s DEWS using examples from recent climate extremes. While drought monitoring and prediction provides just one part of FEWS NET’s monitoring system, it draws from many disciplines—remote sensing, climate prediction, agroclimatic monitoring, and hydrologic modeling. Here we describe FEWS NET’s multiagency multidisciplinary DEWS and Food Security Outlooks. This DEWS uses diagnostic analyses to guide predictions. Midseason droughts are monitored using multiple cutting-edge Earth-observing systems. Crop and hydrologic models can translate these observations into impacts. The resulting information feeds into FEWS NET reports, helping to save lives by motivating and targeting timely humanitarian assistance.


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