Analysis of factors affecting lecturer performance at a university during the COVID-19 pandemic using logistic regression and genetic algorithms

Abstract

The Covid-19 pandemic has forced us to change all aspects of our lives, including higher education. As a result, lecturers get an impact in terms of technology literacy obligations. This situation certainly affects their performance in implementing the Tri Dharma of Higher Education. The purpose of this study is to analyze the factors that affect the performance of lecturers during the Covid-19 pandemic. These factors include age, education, motivation, satisfaction, perception of appreciation, supervision, learning facilities, and technological literacy. The method for collecting data was questionnaires and open interviews with 150 lecturer respondents at a university. Furthermore, the data obtained were analyzed using a logistic regression model, where the parameter estimation was conducted using a genetic algorithm. The estimation process is assisted by Matlab 7.0 software. The results of the analysis show that the factors of age, education, motivation, satisfaction, perception of supervision, learning facilities, and technological literacy have a significant effect on lecturer performance. This study implies that the University needs to consider significant factors for improving lecturers' performance so that teaching and learning activities can run effectively.

Keywords: Covid-19, · teaching and learning, lecturer performance, Logistic regression, Genetic algorithm.