A survey on big data-enabled innovative online education systems during the COVID-19 pandemic

With the spread of COVID-19 around the world, the education industry faces enormous challenges. Some colleges and universities have launched online teaching. Comprehensive online teaching and student health checkups help students complete the set teaching content and return to school as soon as possible. With the development of big data, combined with the epidemic risk we are facing, the rational use of big data and the internet for innovative online education has become a mainstream teaching method. Colleges and universities are not yet familiar with the development prospects and future of online education. Through the research of this paper, we can understand the combination of online education and the development of big data and promote its application in colleges and universities. Not only have innovative online education platforms such as MOOC and DingTalk been widely used, but innovative online education methods such as virtual classrooms also have been created. Based on the current epidemic background, this paper analyzes the development of online education, introduces the impact of the combination of online education and big data, and introduces innovative online education technologies and their effects. It helps online education under the influence of the new coronavirus epidemic, operating big data technology to analyze the current prospects and development of online education, showing the combination of big data technology and online education through the analysis of big data technology, and ending with more expectations on other aspects of the use of big data, which affects the online education industry as well as other industries. Finally, we summarize the combination of big data and innovative online education since the emergence of COVID-19 and introduce the concepts and methods of combining online education and big data technology in detail. The online education platform also makes a reasonable introduction. The thesis can be used to understand the problems and challenges faced by innovative online education in the context of the new coronavirus epidemic and look forward to the future on this basis.

The risks perceived by the consumer in the acceptance of electronic commerce. A study of Bolivia

The risks perceived by consumers in online shopping can negatively affect the acceptance of electronic commerce, however, the perceived risks are a cultural aspect that can affect consumers differently in different countries. This research examines what dimensions of perceived risk affect the acceptance of electronic commerce in Bolivia, a developing country. The design is quantitative, the TAM model is used with the variables: risk of misuse of information, risk of not obtaining the benefits of the product and risk of inefficiency of the functionality. The results show that the three risks examined do not affect the acceptance of electronic commerce, and that the variables perceived usefulness and ease of use have a positive effect on the acceptance of electronic commerce. The theoretical implications of this study provide empirical evidence from Bolivia, which shows strong variables of perceived usefulness and ease of use, which mitigates the effect of risk´s perception by the consumer, the results are explained in the context of the COVID-19 pandemic that accelerated the rapid acceptance of electronic commerce, increasing the perception of usefulness and ease of use of online shopping. As practical implications, this research provides managers and administrators of online businesses with knowledge about the effect of risk variables perceived by consumers.

SDA-Vis: A Visualization System for Student Dropout Analysis Based on Counterfactual Exploration

High and persistent dropout rates represent one of the biggest challenges for improving the efficiency of the educational system, particularly in underdeveloped countries. A range of features influence college dropouts, with some belonging to the educational field and others to non-educational fields. Understanding the interplay of these variables to identify a student as a potential dropout could help decision makers interpret the situation and decide what they should do next to reduce student dropout rates based on corrective actions. This paper presents SDA-Vis, a visualization system that supports counterfactual explanations for student dropout dynamics, considering various academic, social, and economic variables. In contrast to conventional systems, our approach provides information about feature-perturbed versions of a student using counterfactual explanations. SDA-Vis comprises a set of linked views that allow users to identify variables alteration to chance predefined students situations. This involves perturbing the variables of a dropout student to achieve synthetic non-dropout students. SDA-Vis has been developed under the guidance and supervision of domain experts, in line with some analytical objectives. We demonstrate the usefulness of SDA-Vis through case studies run in collaboration with domain experts, using a real data set from a Latin American university. The analysis reveals the effectiveness of SDA-Vis in identifying students at risk of dropping out and proposes corrective actions, even for particular cases that have not been shown to be at risk with the traditional tools that experts use.