scholarly journals Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence

2014 ◽  
Vol 1 (3) ◽  
pp. 7-33 ◽  
Author(s):  
Everaldo Aguiar ◽  
G. Alex Ambrose Ambrose ◽  
Nitesh V. Chawla ◽  
Victoria Goodrich ◽  
Jay Brockman
Keyword(s):  
Academic Performance ◽  
Student Persistence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Student Attrition ◽  
Electronic Portfolios ◽  
Drop Out ◽  
New Techniques ◽  
First Semester

As providers of higher education begin to harness the power of big data analytics, one very fitting application for these new techniques is that of predicting student attrition. The ability to pinpoint students who might soon decide to drop out, or who may be following a suboptimal path to success, allows those in charge to not only understand the causes for this undesired outcome, but it also provides room for the development of early intervention systems. While making such inferences based on academic performance data alone is certainly possible, we claim that in many cases there is no substantial correlation between how well a student performs and his or her decision to withdraw. This is especially true when the overall set of students has a relatively similar academic performance. To address this issue, we derive measurements of engagement from students' electronic portfolios and show how these features can be effectively used to augment the quality of predictions.

scholarly journals SMOTE-BD: An Exact and Scalable Oversampling Method for Imbalanced Classification in Big Data

2018 ◽  
Vol 18 (03) ◽  
pp. e23 ◽  
Author(s):  
María José Basgall ◽  
Waldo Hasperué ◽  
Marcelo Naiouf ◽  
Alberto Fernández ◽  
Francisco Herrera
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Model Design ◽  
Minority Class ◽  
Imbalanced Classification ◽  
Design And Implementation ◽  
Learning Issues ◽  
Intelligent Model

The volume of data in today's applications has meant a change in the way Machine Learning issues are addressed. Indeed, the Big Data scenario involves scalability constraints that can only be achieved through intelligent model design and the use of distributed technologies. In this context, solutions based on the Spark platform have established themselves as a de facto standard. In this contribution, we focus on a very important framework within Big Data Analytics, namely classification with imbalanced datasets. The main characteristic of this problem is that one of the classes is underrepresented, and therefore it is usually more complex to find a model that identifies it correctly. For this reason, it is common to apply preprocessing techniques such as oversampling to balance the distribution of examples in classes. In this work we present SMOTE-BD, a fully scalable preprocessing approach for imbalanced classification in Big Data. It is based on one of the most widespread preprocessing solutions for imbalanced classification, namely the SMOTE algorithm, which creates new synthetic instances according to the neighborhood of each example of the minority class. Our novel development is made to be independent of the number of partitions or processes created to achieve a higher degree of efficiency. Experiments conducted on different standard and Big Data datasets show the quality of the proposed design and implementation.

Using Big Data Analytics to Assist a Smart City to Prevent Cyber Security Threats

2021 ◽  
pp. 107-124
Author(s):  
Fenio Annansingh
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Smart City ◽  
Data Analytics ◽  
Life Quality ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Smart Services ◽  
Prevention Model

The concept of a smart city as a means to enhance the life quality of citizens has been gaining increasing importance in recent years globally. A smart city consists of city infrastructure, which includes smart services, devices, and institutions. Every second, these components of the smart city infrastructure are generating data. The vast amount of data is called big data. This chapter explores the possibilities of using big data analytics to prevent cybersecurity threats in a smart city. It also analyzed how big data tools and concepts can solve cybersecurity challenges and detect and prevent attacks. Using interviews and an extensive review of the literature have developed the data analytics and cyber prevention model. The chapter concludes by indicating that big data analytics allow a smart city to identify and solve cybersecurity challenges quickly and efficiently.

IoT Based Agriculture as a Cloud and Big Data Service

2020 ◽  
pp. 1499-1521
Author(s):  
Sukhpal Singh Gill ◽  
Inderveer Chana ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Cloud Environment ◽  
Big Data Technologies ◽  
Sensor Networking ◽  
New Applications

Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service (AaaS) through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service (QoS) is also better in terms of QoS parameters.

scholarly journals FRAMEWORK FOR MODELLING MOBILE NETWORK QUALITY OF EXPERIENCE THROUGH BIG DATA ANALYTICS APPROACH

2017 ◽  
Author(s):  
Ayisat Wuraola Yusuf-Asaju ◽  
Zulkhairi Md. Dahalin ◽  
Azman Ta’a
Keyword(s):  
User Experience ◽  
Data Analytics ◽  
Network Performance ◽  
Laboratory Experiments ◽  
Quality Of Experience ◽  
Big Data Analytics ◽  
Mobile Network ◽  
Mobile Internet ◽  
Mobile Network Operators

The increase in the usage of different mobile internet applications can cause deterioration in the mobile network performance. Such deterioration often declines the performance of the mobile network services that can influence the mobile Internet user’s experience, which can make the internet users switch between different mobile network operators to get good user experience. In this case, the success of mobile network operators primarily depends on the ability to ensure good quality of experience (QoE), which is a measure of users’ perceived quality of mobile Internet service. Traditionally, QoE is usually examined in laboratory experiments to enable a fixed contextual factor among the participants even though the results derived from these laboratory experiments presented an estimated mean opinion score representing perceived QoE. The use of user experience dataset involving time and location gathered from the mobile network traffic for modelling perceived QoE is still limited in the literature. The mobile Internet user experience dataset involving the time and location constituted in the mobile network can be used by the mobile network operators to make data-driven decisions to deal with disruptions observed in the network performance and provide an optimal solution based on the insights derived from the user experience data. Therefore, this paper proposed a framework for modelling mobile network QoE using the big data analytics approach. The proposed framework describes the process of estimating or predicting perceived QoE based on the datasets obtained or gathered from the mobile network to enable the mobile network operators effectively to manage the network performance and provide the users a satisfactory mobile Internet QoE.  

scholarly journals Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2994 ◽  
Author(s):  
Bhagya Silva ◽  
Murad Khan ◽  
Changsu Jung ◽  
Jihun Seo ◽  
Diyan Muhammad ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

scholarly journals Clasificación de niveles de uso tecnológico: una propuesta con estudiantes de recién ingreso a la universidad

2012 ◽  
pp. 85-109
Author(s):  
M.C. Patricio Henríquez Ritchie ◽  
Dr. Javier Organista Sandoval
Keyword(s):  
Academic Performance ◽  
Technology Use ◽  
Baja California ◽  
New Technologies ◽  
First Semester ◽  
Nuevas Tecnologías ◽  
Service Training ◽  
Socio Economic Variables ◽  
The Web

Durante el primer semestre de 2008 se realizó una investigación considerando alumnos de recién ingreso a la Facultad de Ciencias Administrativas y Sociales (FCAyS) de la Universidad Autónoma de Baja California (UABC). El propósito fue establecer una clasificación (técnica conglomerado k-medias) a partir de los niveles de uso tecnológico y de la Web y, a su vez, caracterizar a los conglomerados resultantes según variables de desempeño académico, socioeconómicas y de capacitación previa. Se consideró una muestra intencional de 438 estudiantes a quienes se les aplicó una encuesta de uso tecnológico desarrollada en el marco de la investigación. Los resultados muestran que un buen nivel de uso tecnológico y de la Web se asocia con la disposición de equipo de cómputo, servicios de Internet y capacitación previa. No se encontró relación significativa con el desempeño académico de los participantes. Se sugiere propiciar un mayor uso pedagógico de las nuevas tecnologías para contribuir a mejorar la calidad de la educación.AbstractDuring the first semester of 2008 a research was realized considering the new pupils of the Faculty of Social and Administrative Sciences (FCAyS) from the Autonomous University of Baja California (UABC). The purpose was to establish a classification (k-means clusters technique) from technological use levels and the Web and, in turn, characterize the resulting conglomerates according to socio-economic variables, academic performance, and pre-service training. A survey of technology use, developed in the frame of the research, were applied to a sample of 438 studentes. The results show that a good level of technology use and of the Web is associated with the disposal of computing equipment, Internet and previoustraining. No significant relationship to academic performance of the participants was found. It is suggested to promote ahigherpedagogical use of new technologies to help improve the quality of education.Recibido: 25 de mayo de 2009 Aceptado: 30 de diciembre de 2009

Towards a Better and Smarter Healthcare and IoT

2019 ◽  
pp. 191-228
Author(s):  
Navin Kumar
Keyword(s):  
Big Data ◽  
Quality Of Care ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Healthcare Organizations ◽  
Healthcare Data ◽  
Value Based Care ◽  
Rich Data

The amount of healthcare data continues to exponentially grow everyday. The complexity of this data further limits the analytical capabilities of traditional healthcare systems. With value-based care, it is far more imminent for healthcare organizations to control the costs and to improve the quality of care in order to sustain their business. The purpose of the chapter is to gain insights into complexities and challenges that exist in current healthcare systems and how big data analytics and IoT can play a pivotal role to positively influence the quality of care and patient outcomes. The chapter also provides solutions and strategies for building cloud-based data asset that can deliver rich data analytics to both the healthcare systems and the patients.

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