Almost all educational institutions have shifted their academic activities to digital platforms due to the recent COVID-19 epidemic. Because of this, it is very important to assess how well teachers are performing with this new way of online teaching. Educational Data Mining (EDM) is a new field that emerged from using data mining techniques to analyze educational data and making decision based on findings. EDM can be utilized to gain better understanding about students and their learning processes, assist teachers do their academic tasks, and make judgments about how to manage educational system. The primary objective of this study is to uncover the key factors that influence the quality of teaching in a virtual classroom environment. Data is gathered from the students’ evaluation of teaching from computer science students of three online semesters at X University. In total, 27622 students participated in these survey. Weka, sentimental analysis, and word cloud generator are applied in the process of carrying out the research. The decision tree classifies the factors affecting the performance of the teachers, and we find that student-faculty relation is the most prominent factor for improving the teaching quality. The sentimental analysis reveals that around 78% of opinions are positive and “good” is the most frequently used word in the opinions. If the education system is moved online in the future, this research will help figure out what needs to be changed to improve teachers’ overall performance and the quality of their teaching.

The paper considers the symmetric traveling salesman problem and applies it to sixty-four (64) districts of Bangladesh (with geographic coordinates) as a new instance of the problem of finding an optimized route in need of emergency. It approached three different algorithms namely Integer Linear Programming, Nearest-neighbor, and Metric TSP as exact, heuristic, or approximate methods of solving the NP-hard class of problem to model the emergency route planning. These algorithms have been implanted using computer codes, used IBM ILOG CPLEX parallel optimization, visualized using Geographic Information System tools. The performance of these algorithms also has been evaluated in terms of computational complexity, their run-time, and resulted tour distance using exact, approximate, and heuristic methods to find the best fit of route optimization in emergence thus contributing to the field of combinatorial optimization.

Mutation testing is a popular software testing technique, that inject artificial faults into the program and requires test cases to reveal these faults. In this paper, an experimental comparison is analyzed for different types of mutation testing tools in C Sharp language in .NET framework. Different mutation testing tools are giving different mutation score for a program. The objective of this paper is to investigate why the mutation score is different for different tools, and the scope of generating mutants depending on different types of operators. Four tools, such as, Visual Mutator, Cream, Ninja Turtles, and Nester are selected and applied to a program and analyze the outcome. Among these four mutation testing tools, Visual Mutator is better because of its higher mutation score, and it generates mutants for both common and uncommon standard operators and object level operators.