Nursing Student's Satisfaction With Virtual Learning During COVID-19 Pandemic in India

Introduction

The COVID-19 pandemic significantly impacted the world in 2020. Every country adopted quarantine measures to prevent the transmission of the coronavirus infection. These measures resulted in dramatic changes in the daily lives of most people. In the academic world, students faced a shift from the traditional classroom-based teaching to virtual distance learning platforms. This shift in nursing education posed challenges both to the instructors and students as they were not fully prepared for this transition.

Objective

The study assessed the nursing students' satisfaction with the virtual learning experience during the COVID-19 pandemic in selected nursing colleges in India.

Methods

The study was conducted in four nursing colleges in India. A total of 1,166 Diploma, Post Basic BSc (N), BSN, and MSN nursing students participated in the study. Ethical approval was obtained from all the nursing colleges included in the study. The Google Forms satisfaction survey included student, teacher, course, technology, environmental, and practical dimensions.

Results

The response rate was 86.31% (n = 1,166). The overall satisfaction with virtual theory and practical classes has a mean and SD score of 67.14 + 11 and 16.21 + 3.46, respectively. The results showed that overall 51% of the students had good satisfaction with virtual theory classes while 48% had moderate satisfaction. In terms of overall satisfaction with virtual practical classes, approximately 39% had good satisfaction, 58% had moderate satisfaction and nearly 3% had poor satisfaction. In addition, the students expressed that they had good satisfaction with Teacher dimension (64.3%), Student dimension (63%), and Course dimension (57.2%). On the contrary, they expressed poor satisfaction in the Technical dimension (11.3%) and Environmental dimension (5.6%). The results showed that the courses, the place of attending class, and health issues were significantly associated with the student's level of satisfaction with virtual learning.

Conclusion

Although the majority of the students rated their satisfaction level as being "good" in virtual theory classes and "moderate" in virtual practical learning, most of them were dissatisfied with the Technical and Environmental dimensions of virtual learning. This results calls for blended learning strategies to be designed to enhance better learning outcomes and, to ensure deepened level of satisfaction with virtual learning activities.

Post hoc identification of student groups: Combining user modeling with cluster analysis

This study aims to discover groups of students enrolled in the emergency remote teaching online course based on the various course-related data collected throughout the first year of COVID-19 pandemic. Research was conducted among 222 students enrolled in the course "Business Informatics" at the Faculty of Organization and Informatics of the University of Zagreb in the academic year 2020/2021. Overlays were used to model students' success on the various quizzes and exams within the course. The k-means clustering was employed to classify students into groups, based on combination of students' overlay values, frequency of accessing course lessons and the final grades. Three distinct clusters (i.e., students' groups) were discovered and explained in the given context. The identified groups of students can be used for future adaptations of the online course design in order to improve the retention and their final grades.

Effects of the COVID-19 Pandemic on Chinese Graduate Students' Learning Activities: A Latent Class Analysis

To investigate the impact of the pandemic on graduate students' learning activities, a series of questionnaires were distributed to graduate students in universities across China, and 2,818 responses were collected. A latent class analysis was performed to classify the effects of the pandemic on graduate students' learning activities. Then, a multinomial logistic regression analysis and an analysis of variance analysis were carried out to explore the impact of demographic variables on the classification and their mental health status. The analysis identified four latent classes: "the overall less affected" (34.83%), "the overall more affected" (31.97%), "course activities were more affected" (19.40%), and "social activities were more affected" (13.79%). The multinomial logistic regression analysis indicated that during the pandemic, the learning activities of graduate students in all grades were affected to varying degrees, and the impacts on second-year and third-year graduate students were greater than those of first-year graduate students. The analysis of variance revealed that the scores for anxiety, depression, and social anxiety of "the overall more affected" were significantly greater than those of the other three groups, and nearly one-third of students belonged to this class, suggesting that more attention and care should be given to these students. As a result of the COVID-19 pandemic, a number of graduate students have suffered mental problems (anxiety and depression). Under the current backdrop of a new normal, schools and teachers should pay attention to graduate students' mental health, providing targeted assistance to different types of students.