Propose a New Quality Model for M-Learning Application in Light of COVID-19

The coronavirus disease (COVID-19) prevented millions of students around the world from receiving their lessons, because of the closure of thousands of schools. The new COVID-19 global epidemic invaded the barriers of time and space. Using mobile phones in education is a new form of the distance learning system. M-learning is characterized by many characteristics, the most important of which are providing an interactive educational environment, flexibility in space and time, better adaptation to individual needs, acquisition of knowledge, interactive effectiveness, and developing self-learning skills for students. The main aim of this paper is to suggest a quality model for M-learning applications for children which contains the most common characteristics of M-learning, which must be taken into account when designing M-learning applications. Through previous studies related to the quality model for M-learning applications, we proposed two quality characteristics, technical and pedagogical. We proposed 8 subcharacteristics with their features following the structure of the IOS/IEC 912 and DeLone and McLean IS model to find the effect of technical and pedagogical factors on user satisfaction with M-learning applications for children. Results show that the proposed model can be useful and effective to ensure the development of high-quality M-learning applications. © 2022 Ahmad Althunibat et al.

Big data quality factors, frameworks and challenges

Big Data applications are widely used in many fields such as artificial intelligence, marketing, commercial applications and health care, as demonstrated by the role of Big Data in coping with the COVID-19 pandemic. Therefore, it is essential to ensure the quality of the generation and use of Big Data applications. Consequently, Big Data applications must satisfy quality factors suited for these applications. Furthermore, quality frameworks need to be applied and tested for the quality factors of Big Data applications. Nevertheless, the quality measurement process needs to overcome some challenges for it to become applicable and trustworthy. This research lists different quality factors and dimensions and describes quality frameworks that are commonly used to measure the quality of Big Data. Furthermore, it lists the frequent challenges that researchers and data scientists face throughout the Big Data quality measurement process. Finally, it outlines the solutions that need to be developed for confronting the challenges of Big Data quality. © 2020 National Institute of Science Communication and Information Resources (NISCAIR). All rights reserved.