Development and Analysis of a Machine Learning Based Software for Assisting Online Classes during COVID-19

Amid the Covid-19 widespread, it has been challenging for educational institutions to conduct online classes, facing multiples challenges. This paper provides an insight into different approaches in facing those challenges which includes conducting a fair online class for students. It is tough for an instructor to keep track of their students at the same time because it is difficult to screen if any of the understudies within the class are not present, mindful, or drowsing. This paper discusses a possible solution, something new that can offer support to instructors seeing things from a more significant point of view. The solution is a facial analysis computer program that can let instructors know which students are attentive and who is not. There’s a green and red square box for face detection, for which Instructors can watch by seeing a green box on those mindful students conjointly, a red box on those who are not mindful at all. This paper finds that the program can automatically give attendance by analyzing data from face detection. It has other features for which the teacher can also know if any student leaves the class early. In this paper, model design, performance analysis, and online class assistant aspects of the program have been discussed.

The Application of ARCS Motivation Model in Online College English Class

Since the epidemic,online classes have been a common practice,and learning motivation plays a key role in the efficiency of the online English,which should not be underestimated in improving online foreign language teaching.This paper discusses how to effectively apply ARCS motivation model in online English teaching design with examples of college English textbooks,so as to motivate students and improve students’online classroom efficiency by improving their learning motivation.

The Driving Strategy of Online Ideological and Political Class in Universities Based on SWOT Analysis

The epidemic provided practical opportunities for online classes and also tested its drawbacks. To analyze the internal strengths (S) and weaknesses (W) as well as the external opportunities (O) and threats (T) of the online ideological and political class with SWOT theory, based on which to explore targeted solutions, is a “symptomatic” approach to improving the quality of the online ideological and political class. The analysis results are as follows: S includes “not limited by time and space”, “comprehensive teaching monitoring data”, “diverse and innovative interactive forms”, and “abundant high-quality teaching resources”. W refers to “increased difficulty in classroom control”, “teachers’ psychological pressure caused by online teaching”, and “limited by hardware conditions”. O means the opportunity provided by the modernization of ideological and political education and epidemics. T indicates external threats, such as limitations from the network and the severe impact of wrong values on the internet. The driving strategy for above case could be “upgrading of online teaching platforms”, “building a teaching team skilled in information technology teaching”, “improving hardware facilities” and “joining forces to confront value crisis”.

Multitasking Behavior and Perceptions of Academic Performance in University Business Students in Mexico during the COVID-19 Pandemic

The current study measures the influence of multitasking behavior and self-efficacy for self-regulated learning(SESRL)on perceptions of academic performance and views in university students during the COVID-19 pan-demic in Mexico.264 university students fulfilled an online questionnaire.It was observed that multitasking beha-vior negatively influences SESRL(-0.203),while SESRL showed a positive influence of 0.537 on perceptions of academic performance,and multitasking behavior had an influence of-0.097 on the perception of academic per-formance.Cronbach’s alpha and Average Variance Extracted values were 0.809 and 0.577(multitasking behavior),0.819 and 0.626(SESRL),0.873 and 0.725(perceptions of academic performance),respectively.The results of the bootstrapping test showed that the path coefficients were significant.The study outcomes can support new plans in universities to ensure the best academic outcomes.Our study showed evidence of the COVID-19 impact on education behavior.This study’s novelty is based on using the partial least square structural equation modeling(PLS-SEM)technique to evaluate these variables.