Situational Analysis of Remote Learning Using Logistics Regression and Decision Trees Amidst Covid19 Pandemic

Amidst the continuous upsurge of COVID19 pandemic worldwide, classes are still postponed or continuously implementing remote learning. Based on the previous experience, there are still significant number of faculty and students facing similar challenges with the online mode. Four important parameters, among others, are considered, to be the most important parameters to be able to survive or manage the full online classes. 420 faculty and students participated in a survey to gauge their situations according to the availability of IT resources, internet bandwidth, IT skills proficiency and the institutional subsidy funds. Logistics regression and decisions trees are used to analyze the participants’ situation where remote learning is employed. Results show that the logistics regression has a confidence level of 96.03% whereas the decision tree predicts that IT skills proficiency and internet bandwidth are the most significant parameters for improvement during this pandemic.