At school, students must learn mathematics not only as a formality to participate in exams, but also to use their mathematical knowledge to solve problems in their daily lives. When students deal with numerical information, they tend to have a negative feeling or fear, and this is known as mathematical anxiety. The aim of the project is to develop a mobile application that integrates augmented reality technology into mathematical education and to evaluate the technological acceptance of the mobile application. This mobile app is reviewed and tested by 23 end users, most of whom are educators and parents. The technique to achieve the objective is to use the Acceptance Model Questionnaire (TAM). In the technology acceptance model there are three sections that are perceived as useful, user-friendly and intended for use. Based on the results, it can be concluded that the three technology acceptance sections are acceptable and that educators and parents are satisfied with it. For future work, the research recommends adding a variety of exercises with diversifying forms of questions with answers in the mobile application. It is further recommended that the augmented reality output to implement a 3D modeling design to project a better virtual images.

Since the recent incidence of global COVID-19 pandemic, expertise from different domains including scientists, clinicians, and healthcare experts keep on exploring for technologies to manage the COVID-19 data. Updated and accurate data collection is very critical for them to make a more effective and efficient decision on any aspects of the emergency consequences and events. Although some of them are inexpert data scientists, the important skills and knowledges to extract the recent data on COVID-19 is web data extraction and analysis. While tremendous of literature can be referred from the academic databases, it is difficult to find the report that presents the basis and fundamental methods for implementing web data analysis in a simple way with a rapid software platform. This paper demonstrates a simple framework for implementing web data extraction or web scraping to be analyzed in a rapid software platform. Python scripting language is the simple tool to conduct the web scraping method while RapidMiner is the rapid software for implementing the data visualization and analysis. Simple linear regression based on machine learning approach has been implemented with the RapidMiner to predict COVID-19 death based on the collected data. This paper will be useful for academicians and industry practitioners to conduct a more robust data analysis to accommodate a more challenge issue such as big data analytics in any domains.