On modelling student’s resilience in learning statistics at a distance

Abstract

Learning statistics online during the COVID-19 pandemic became a challenging experience for most students in higher institutions. This study aimed to measure the students’ level of resilience and determine its influencing factors in distance learning during the pandemic. Data from an availability sampling of 129 engineering students were gathered with the aid of a Google form survey. The study used some descriptive measures and employed a regression modeling approach to extract detailed information from the survey data. Results showed that, on average, students were considered “resilient” in learning statistics during the pandemic. Statistical models revealed that sex, number of family members, household assets, and level of how conducive learning at home are significant predictors of students’ resilience. Additionally, the model showed that male students are more resilient compared to female students. Lastly, more family members and household assets can increase students’ resilience level as well as a comfortable place (at home) for learning. Hence, the study suggests that teachers must strengthen the interest of students especially female students by showing them a good attitude that promotes well-being. Furthermore, teachers must regularly monitor their learning progress, and provide comfortable and reasonable learning activities suitable for distance learning.