The impact of virtual learning on students’ educational behavior and pervasiveness of depression among university students due to the COVID-19 pandemic

Demographic characteristics

The total number of participants was 157 university students. Table 1 shows the demographic characteristics of the participants.

Table 1 Demographic characteristics of the respondentsStudents’ levels of depression and demographic variables

In the univariate analysis, chi-square tests were used to determine the associations between students’ demographic variables and the ZSDS level. Table 2 displays the association between depression levels with gender, age, and college. Among the demographic variables, only the association with gender was statistically significant at \(^\) = 20.229, and p < 0.001, while the association with age and college was not significant. A total of 74.4% of the students had various levels of depression. Of these, 37%, 21.7%, and 16% had mild, moderate, and severe depression levels, respectively. In addition, females (28%) had more depressive symptoms than males (4%).

Table 2 Association between depression levels and students’ demographic variables in Saudi ArabiaEducational distress factors associated with virtual learning and descriptive statistics

The factors related to virtual learning sequel to the COVID-19 pandemic, and its impact on students’ educational behaviors were divided into two categories. Questions on virtual learning's effect on students' feelings from an educational perspective (Category 1) had four items, each with a "Yes" or "No" answer. Likewise, questions on virtual learning and its effect on students’ understanding of the subjects/materials (Category 2) had five items, each with a “Yes” or “No” answer. Table 3 demonstrates the descriptive statistics. In the first category, the highest percentage was feeling worried and having a fear of exams (79%), followed by stress (75.2%), lack of motivation, and decreased productivity (70%, each). In the second category, the highest percentage was 78%, who felt they had to put extra self-effort into understanding and studying.

Table 3 Educational distress factors associated with virtual learning due to the COVID-19 pandemic and descriptive statistics

Furthermore, 74.5% felt that virtual learning was more challenging for them to understand than physical learning. In addition, 73% said virtual learning was slow and extra time was needed to understand and learn the concepts, while 64% found it boring. Finally, 58.6% had difficulty solving problems and submitting properly written answers (for math and computer science subjects).

Distress factors related to virtual learning and depressive symptoms

Multilinear regression analysis was used to study whether various distress factors related to virtual learning can influence depressive symptoms among students.

The first category, which dealt with students’ feelings from the educational point of view, hypothesized that lack of motivation, stress, worry/fear of examinations, and decreased productivity would significantly impact the development of depressive symptoms among students.

Multi-regression analysis was used to test the hypotheses, with the Zung depression index as a dependent variable. The results show that 24.6% of the variance in Zung’s depression index can be accounted for by four predictors, collectively\(, F(4, 152) = 12.414, p < 0.001\). Looking at the unique individual contribution of the predictors, the result shows that worry and fear of exams (\(\beta =0.290, t=3.589, p<0.001)\), stress (\(\beta =0.202, t=2.566, p=0.011<0.05)\), and decreased learning amount and not being productive (\(\beta =0.211, t=2.783, p=0.006<0.05)\), statistically significantly contributed to worsening depressive symptoms. The predictor, feeling lack of motivation, did not significantly impact developing depressive symptoms.

The second category dealt with virtual learning and its effect on students’ understanding of the subjects/materials. It was hypothesized that the need for extra self-effort to understand the subject, learning became harder, learning became slower, learning was boring, and difficulty in solving problems and writing answers properly would have a statistically significant impact on developing depressive symptoms among students.

Multi-regression analysis was used to test the hypotheses, with Zung’s depression index as a dependent variable. The test showed that 13% of the variance in Zung's depression index can be accounted for by the five predictors, collectively\(, F(5, 151) = 4.505, p < 0.001\). Looking at the unique individual contribution of the predictors, the result shows that learning is not much fun or exciting (\(\beta =0.250, t=3.060, p=0.003<0.05)\), and facing difficulty in solving questions and writing answers properly (\(\beta =0.176, t=2.067, p=0.05<0.05)\), were statistically significantly associated with worsening depressive symptoms. While the other three predictors, learning became harder, learning became slower, and the need to put extra self-effort did not contribute significantly to depressive symptoms, as shown in Table 4.

Table 4 Results of multi-regression analysis

Furthermore, we explored two distress factors, stress, and worry/fear of exams, which contributed statistically significantly to worsening depressive symptoms. Using the chi-square test, we examined the association of the distress factors with depression levels; that is, what extent does stress or worry/fear of exams contribute to moderate or severe depression. The results showed a statistically significant association between stress and moderate to severe levels of depression ( \(^\) = 17.179, and p < 0.001). Likewise, there was a statistically significant association between worry/fear of exams and moderate to severe levels of depression ( \(^\) = 30.236, and p < 0.001), Table 5.

Table 5 Chi-square test for association between a few distress factors and depression levels

The association between stress or worry/fear of exams and gender was examined using the chi-square test. There was a statistically significant association between these two factors and gender, with more females having higher stress levels (54%) than males (41%). Also, worry/fear of exams manifested in 60% of females and 40% of males during virtual learning, sequel to the COVID-19 pandemic. The results are presented in Table 6.

Table 6 Chi-square test for association between a few distress factors and genderOpen-ended questions

The questionnaire ended with an open-ended question, in which students were asked to write in their own words how the lockdown has affected their educational advancement. Excerpts of the negative comments from students are outlined below:

“Virtual teaching and exam resulted in increased cheating."

“Virtual teaching caused difficulty in understanding the subject, which resulted in lowering my grades.”

"I have to sit in the same room with my siblings while learning online, as my home is small. So, I cannot concentrate at all; it just makes me very frustrated.”

From their comments, it is clear that a virtual learning environment is entirely different from a physical classroom teaching environment where exams are conducted with invigilators proctoring.

Significantly few students provided positive comments.

"Virtual teaching made me understand better and increased productivity and my grades."

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