Technology Dependency and Impact during COVID-19: A Systematic Literature Review and Open Challenges

The COVID-19 pandemic is still a challenge in many countries, although life must proceed while ensuring the pandemic is managed critically. Due to the delay in producing permanent medical intervention, despite the availability of vaccines, there is still a need to depend on technology in performing several tasks. A systematic literature review that provides comprehensive evidence on technology dependence and the impact of technology on individuals during the pandemic is lacking. This study systematically reviewed scholarly works related to technology dependency from a broad view since the pandemic and mapped the research findings into a taxonomy, thus establishing the trend in technology type, major areas of technology dependency, and the impact of technology during the pandemic. The mapped taxonomy is used to expound on open challenges and recommendations. The final set from the systematic search was 76 articles. Technology might be an avenue for administering and enhancing health services, improving outreaches, and supporting curbing the spread of diseases. However, the impact of technology dependence is both positive and negative. A systematic mapping was conducted to explore the literature on the impacts of technology, where there is a need for further research. Notwithstanding the category, most of the reviewed articles emphasized the usage and impact of technology at such a time of the pandemic and provided insights on the manner of addressing them. Realistically, there has been an acceleration of digitalization trends in the present era of the COVID-19 pandemic and the possibility of rapid development of novel digital technologies. Author

SIR model for the spread of COVID-19: A case study

In this article, we study the spread pattern of the epidemic of COVID-19 disease from the point of view of mathematical modeling. Considering that this virus follows the basic rules of epidemic disease transmission, we use the SIR model to show the spread process of this disease in Iran. Then we estimate the primary reproduction number (R0) of COVID-19 in Iran by matching an epidemic model with the data of reported cases. © 2022

The Preliminary Investigation on Micro-Credentials Practices in Malaysia

This COVID-19 has lately changed the way individuals study and teach by making it available at any time, from any location, and at a low cost. Traditional face-to-face teaching and learning are losing popularity as more students opt for hybrid or all-online learning. In today's fast-paced business, the advent of the gig economy needs the development of individuals and experts with specialised skill sets to fill increasingly specialised positions. Higher education providers needed a more dynamic and quick style of learning to match these demands, which Micro-Credential, a well-known player in 21st-century training and education, delivered. Micro-credentials are nanodegrees, also known as small qualifications, that demonstrate a person's talents, knowledge, and/or experience in a certain subject area or ability. After completing a micro-credentials course, the learner will receive a digital badge. The adoption of Micro-Credential courses in Malaysia was investigated using desktop research and a survey questionnaire in this study. This study examines how Micro-Credentials are used in Malaysia's top three public universities, as well as a poll of Malaysian students' Micro-Credentials habits. Micro-credentialing appears to be gaining popularity at Malaysian universities. The research will be broadened to collect and analyse data for the preliminary study, which will focus on learners' attainment of Digital Badges for Micro Credential Computing Courses using a quantitative research technique. © 2022 IEEE.

USING DATA MINING TECHNIQUES TO MEASURE STUDENTS’ ACADEMIC PERFORMANCE AND THE EFFECTIVENESS OF LEARNING METHODS: A LITERATURE REVIEW

Analyzing students' academic performance in online learning to improve the overall quality and effectiveness of education has been one of the main focuses of Higher Educational Institutions (HEIs). A practical analysis utilizing the academic performance data to improve the quality of online learning has become a vital issue urgently required to guide HEIs for the improvement of the academic performance of students. The changes affected in the Covid-19 framework have affected the academic performance of students and educators. This study aims to summarize the various aspects of educational data mining and how it can be utilized to improve the teaching process. Using EDM, the study analyzed students' academic performance for the past five years. It focused on the various learning methods that the students used. The study provided a detailed analysis of the multiple attributes that influenced the students' academic performance. We presented 12 out of 56 papers/documents that fit the inclusion and exclusion criteria of students' academic performance based on the educational setting. This study revealed that the most commonly used methods for assessing students' academic performance are not done in the face-to-face learning method. © 2022 Little Lion Scientific.

An Analysis of Students’ Academic Performance Using K-Means Clustering Algorithm

A massive amount of data is often used to evaluate the academic performance of students in higher education. Analysis can solve this challenge through various strategies and methods. Due to the spread of the pandemic Covid-19, traditional modes of education have shifted to include online learning. This study aims to analyze the academic performance of students through data mining techniques. The objective aims to investigate the academic performance of business students at a private university in Malaysia using Educational Data Mining techniques. Students’ academic performance data of a private university in Malaysia is used to analyze students’ performance using demographic and academic attributes. This study used students’ academic performance in the learning method to identify the patterns before and during Covid-19 using the K-Means data mining clustering technique. The results of the k-means clustering analysis showed that students were achieving higher CGPA during Covid-19 online learning compared to before Covid-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.