Impacto del COVID-19 en la Educación Superior en Pakistán: un estudio exploratorio

The outbreak of novel coronavirus infection (COVID-19), reported initially in December 2019 by China, has caused disruption all over the world.  To control the spreading of this virus all the countries around the world imposed strict lockdown leading to shutting down of all businesses, educational institutions, entertainment centers, etc. Higher Educational Institutions (HEI), across the world, switched to online mode of learning instantly to continue their degree programs. Following the trend, Higher Education Commission of Pakistan encouraged HEIs to begin online classes.  Although, online learning seemed to be the best possible solution during indefinite closure of institutes, but the sudden change in teaching and learning paradigm was not well accepted and unprecedented challenges emerged. This study aims at identifying the barriers specific to students and teachers in this abrupt shift. Moreover, it examines the satisfaction level of undergraduate students regarding online education practices during the COVID-19 epidemic. The study employed survey design and carried it out through two distinct questionnaires that are, for students and teachers which were distributed online via social media platforms. A total of 1280 students participated in students’ questionnaire while 112 teachers contributed to filling out teachers’ survey. Content Quality (CQ), Content Availability (CA), Teacher Interaction (TI), and Mode of Lecture Delivery (MLD) were considered as the predictor variables for student satisfaction. Regression and correlation analyses were performed to find out the contribution of the aforementioned variables. The survey results concluded that the lack of interaction among students and teachers is the major hurdle in online learning.  Regression results revealed that the overall model with all four predictors was significantly predictive of student satisfaction. The results further revealed that MLD is the strongest and most significant of all. We believe the findings of this study can provide beneficial insights in improving the paradigm shift with greater efficiency in this pandemic.El brote del COVID-19 obligó a las Instituciones de Educación Superior (IES) de todo el mundo a continuar sus programas de grado en línea al instante. Siguiendo la tendencia, la Comisión de Educación Superior de Pakistán alentó a las IES a comenzar las clases en línea. Aunque el aprendizaje en línea parecía ser la mejor solución posible durante el cierre indefinido de los institutos, el cambio repentino en el paradigma de enseñanza y aprendizaje no fue bien aceptado y surgieron desafíos sin precedentes. Este estudio tiene como objetivo identificar las barreras específicas para estudiantes y profesores en este cambio abrupto. Además, examina el nivel de satisfacción de los estudiantes de pregrado con las prácticas de educación en línea durante la epidemia del COVID-19. El estudio empleó un diseño de encuesta y lo llevó a cabo a través de dos cuestionarios distintos para estudiantes y profesores. Un total de 1280 estudiantes participaron en el cuestionario de los estudiantes, mientras que 112 maestros contribuyeron a completar la encuesta de docentes. La calidad del contenido (CQ), la disponibilidad del contenido (CA), la interacción del maestro (TI) y el modo de impartición de la conferencia (MLD) se consideraron como variables predictoras de la satisfacción del estudiante. Se realizaron análisis de regresión y correlación para conocer la contribución de las variables mencionadas. Los resultados de la encuesta concluyeron que la falta de interacción entre estudiantes y profesores es el principal obstáculo en el aprendizaje en línea. Los resultados de la regresión revelaron que el modelo general con los cuatro predictores fue significativamente predictivo de la satisfacción de los estudiantes. Los resultados revelaron además que MLD es el más fuerte y significativo de todos. Creemos que los hallazgos de este estudio pueden proporcionar información beneficiosa para mejorar el cambio de paradigma con mayor eficiencia en esta pandemia

Survey, Analysis and Issues of Islamic Android Apps

Mobile devices like Smartphones, tablets and PDAs have become an indispensable part of every person’s day to day activities. The growth and propagation of the smartphones has created new opportunities for religious app developers to develop apps that will provide utilities and an easy accessibility to religious information. The purpose of this research is to conduct a survey and to classify Islamic apps that are available on Google Play Store. The user surveys were conducted to evaluate and investigate the usage pattern of the Islamic apps in everyday life of the Muslims. The results identify the need of authentication of the app content that gives rise to many critical issues and myths. Also, it stresses the need for a “Religion” category in Google Play Store. The benefit of this research is twofold, as it focuses on identifying which app features Muslim users are more interested in using and secondly, the Islamic apps/features that need to be developed

Usability Evaluation of Islamic Learning Mobile Applications

Abstract : The trend of using mobile devices for the purpose of learning is gaining momentum. Apart from traditional education, various applications are being developed for religious learning. Pakistan is inhabited by around 98% of Muslims.  Hence, the informal learning of Islam is essential for Muslim child development. This research presents a usability study of different Islamic learning mobile applications available on the android platform for children. The purpose of this research is to evaluate the usability of different Islamic learning mobile applications for children of diverse age groups in order to understand what design principles must be followed that increase the usability of the application. The main focus of this research is to observe and evaluate how easily children of different age groups respond to different applications, how effectively the children understand the core features of the applications and how easily they are able to use the application by themselves.

A Systematic Review and IoMT Based Big Data Framework for COVID-19 Prevention and Detection

The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public health. COVID-19 is a highly infectious virus that is spread by person-to-person contact. Therefore, minimizing physical interactions between patients and medical healthcare workers is necessary. The significance of technology and its associated potential were fully explored and proven during the outbreak of COVID-19 in all domains of human life. Healthcare systems employ all modes of technology to facilitate the increasing number of COVID-19 patients. The need for remote healthcare was reemphasized, and many remote healthcare solutions were adopted. Various IoMT-based systems were proposed and implemented to support traditional healthcare systems with reaching the maximum number of people remotely. The objective of this research is twofold. First, a systematic literature review (SLR) is conducted to critically evaluate 76 articles on IoMT systems for different medical applications, especially for COVID-19 and other health sectors. Secondly, we briefly review IoMT frameworks and the role of IoMT-based technologies in COVID-19 and propose a framework, named ‘cov-AID’, that remotely monitors and diagnoses the disease. The proposed framework encompasses the benefits of IoMT sensors and extensive data analysis and prediction. Moreover, cov-AID also helps to identify COVID-19 outbreak regions and alerts people not to visit those locations to prevent the spread of infection. The cov-AID is a promising framework for dynamic patient monitoring, patient tracking, quick disease diagnosis, remote treatment, and prevention from spreading the virus to others. We also discuss potential challenges faced in adopting and applying big data technologies to combat COVID-19

A Systematic Review and IoMT Based Big Data Framework for COVID-19 Prevention and Detection

The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public health. COVID-19 is a highly infectious virus that is spread by person-to-person contact. Therefore, minimizing physical interactions between patients and medical healthcare workers is necessary. The significance of technology and its associated potential were fully explored and proven during the outbreak of COVID-19 in all domains of human life. Healthcare systems employ all modes of technology to facilitate the increasing number of COVID-19 patients. The need for remote healthcare was reemphasized, and many remote healthcare solutions were adopted. Various IoMT-based systems were proposed and implemented to support traditional healthcare systems with reaching the maximum number of people remotely. The objective of this research is twofold. First, a systematic literature review (SLR) is conducted to critically evaluate 76 articles on IoMT systems for different medical applications, especially for COVID-19 and other health sectors. Secondly, we briefly review IoMT frameworks and the role of IoMT-based technologies in COVID-19 and propose a framework, named ‘cov-AID’, that remotely monitors and diagnoses the disease. The proposed framework encompasses the benefits of IoMT sensors and extensive data analysis and prediction. Moreover, cov-AID also helps to identify COVID-19 outbreak regions and alerts people not to visit those locations to prevent the spread of infection. The cov-AID is a promising framework for dynamic patient monitoring, patient tracking, quick disease diagnosis, remote treatment, and prevention from spreading the virus to others. We also discuss potential challenges faced in adopting and applying big data technologies to combat COVID-19

Smart Learning Companion (SLAC)

Augmented Reality (AR) tends to merge the computing world with the real world, giving way to an incredible user experience. This field is not only limited to entertainment but has been utilized in various domains including healthcare, education and training. Realizing the potential of Augmented Reality in improving the learning experience, researchers have explored many ways of incorporating AR in the field education. Consequently, this research is focused on providing interactive and customized learning experience to book readers. We present a mobile application, Smart Learning Companion (SLAC), for physical books that provide a virtual content for a book. The virtual content include, 3D animations, Quizzes, explanation of content in native language and many other features. The virtual content is activated as soon as pages are scanned with a mobile phone or tablet. Smart Learning Companion explains animated educational content and provides an interactive user experience. The aim of SLAC is to encourage students to learn on their own by making books more interactive. Smart Learning Companion provides explanation in Urdu, solutions of exercises with animations, quizzes for each section, and overall result that shows the student progress. This will help to reduce the dependency of students on others for learning making them capable of self-learning. Smart Learning Companion applications are developed for four books to conduct the experiments. Experimental study is conducted to show the effectiveness of Smart Learning Companion application. The results show that our application helped students to understand the concepts more easily as explanation was provided in national language of Pakistan, that is, Urdu

Secure and failure hybrid delay enabled a lightweight RPC and SHDS schemes in Industry 4.0 aware IIoHT enabled fog computing

These days, the Industrial Internet of Healthcare Things (IIT) enabled applications have been growing progressively in practice. These applications are ubiquitous and run onto the different computing nodes for healthcare goals. The applications have these tasks such as online healthcare monitoring, live heartbeat streaming, and blood pressure monitoring and need a lot of resources for execution. In IIoHT, remote procedure call (RPC) mechanism-based applications have been widely designed with the network and computational delay constraints to run healthcare applications. However, there are many requirements of IIoHT applications such as security, network and computation, and failure efficient RPC with optimizing the quality of services of applications. In this study, the work devised the lightweight RPC mechanism for IIoHT applications and considered the hybrid constraints in the system. The study suggests the secure hybrid delay scheme (SHDS), which schedules all healthcare workloads under their deadlines. For the scheduling problem, the study formulated this problem based on linear integer programming, where all constraints are integer, as shown in the mathematical model. Simulation results show that the proposed SHDS scheme and lightweight RPC outperformed the hybrid for IIoHT applications and minimized 50% delays compared to existing RPC and their schemes

Secure and failure hybrid delay enabled a lightweight RPC and SHDS schemes in Industry 4.0 aware IIoHT enabled fog computing

These days, the Industrial Internet of Healthcare Things (IIT) enabled applications have been growing progressively in practice. These applications are ubiquitous and run onto the different computing nodes for healthcare goals. The applications have these tasks such as online healthcare monitoring, live heartbeat streaming, and blood pressure monitoring and need a lot of resources for execution. In IIoHT, remote procedure call (RPC) mechanism-based applications have been widely designed with the network and computational delay constraints to run healthcare applications. However, there are many requirements of IIoHT applications such as security, network and computation, and failure efficient RPC with optimizing the quality of services of applications. In this study, the work devised the lightweight RPC mechanism for IIoHT applications and considered the hybrid constraints in the system. The study suggests the secure hybrid delay scheme (SHDS), which schedules all healthcare workloads under their deadlines. For the scheduling problem, the study formulated this problem based on linear integer programming, where all constraints are integer, as shown in the mathematical model. Simulation results show that the proposed SHDS scheme and lightweight RPC outperformed the hybrid for IIoHT applications and minimized 50% delays compared to existing RPC and their schemes.This work is financially supported by the Research grant of PIFI 2020 (2020VBC0002), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (SIAT,CAS), Shenzhen, China.</p