4.1 Sociodemographic factors
Out of the total students who received the online survey questionnaire, 50% had given a response to the questionnaire. This response rate is higher than the average online survey response rate (44%). However, the response rate in this study was less (55.6%) than in the study conducted before (25). The difference in results could be attributed to the total number of students who received the online survey questionnaire. Wu (26) stated that sending an online survey to more students did not generate a higher response rate, rather sending surveys to a clearly defined population, like in the current study, positively impacts the online survey response rate. The difference in results could be attributed to the year of students in that in the current study only year I undergraduate medical students participated, while year I, II, III, and IV medical undergraduate students participated in the previous study (25).
Most of the students who participated in this study were females and joined the university from public high schools in Ethiopia. Only 38.7% of the students had extra computer training in their high school, the rest had computer courses in the normal educational curriculum, but not extra computer training. When compared with another study done before (27), the number of students who had extra computer training in their high school was less in this study. Students' and families' perceptions of computer training on its impact on education, accessibility of computer training centers, economy, and time could be some of the reasons for the less number of students who had extra computer training in their high school.
4.2 Students’ attitude and readiness toward online learning
Nowadays, the realization of education and knowledge goes beyond the confines of educational institutes (28). Online learning aims to seek changes in the pattern of the academic process and is becoming one of the alternative platforms to the traditional on-site educational format (1, 28).
In this study, according to the Likert scaling score and interpretation (Table 1), the student’s attitude toward online learning was high (mean = 3.49). Other studies also indicated that students’ attitude toward online learning was positive and high (18, 28, 29). However, most of the students who participated in the current study agreed that online learning practices increase grades, but not knowledge. Similarly, a study conducted before showed that students had difficulty of focusing and understanding the content of the subject matter during online learning (30). This could be attributed to the less motivation of students during online learning than in face-to-face. Some studies indicated that online learning does not motivate students to learn (31, 32, 33), indicating that unless and otherwise, online learning has interactive features, online learning causes less motivation for learning. Another study also indicated that students taking the face-to-face course were more satisfied and motivated than their online counterparts (34).
On the other hand, like the previous study (17), the basic computer skills and knowledge related to online learning were high in this study and affected the students’ attitudes towards online learning. Other studies also showed that computer use and previous exposure were correlated with higher online learning attitudes (29, 35).
Although the attitudes of the students toward online learning were high in this study, students’ readiness was not high but was moderate. However, a previous study showed that 70.9% of students had a high level of readiness toward online learning (19). Contrarily, a study conducted by Kabira et al. (36) revealed that more than half of students had a sub-optimum level of readiness. Variations in institutional, personal, and technological-related capacities between the study participants could be some of the reasons for the difference in results.
In this study, females were less ready for online learning than males. This could be attributed to online learning stress effects. Yet, studies are required about the stress effects of online learning, a sudden shift from face-to-face learning to the online learning platform can be more stressful to females than males. According to the previous study, most students manifested different levels of perceived e-learning stress (36) that could affect online learning readiness. Another study also showed that the prevalence of depressive symptoms and anxiety among students was significantly higher during the distance learning period compared to the full-time study period (37). At the same time, female students showed higher education-related stress levels than males (38–40). On the other hand, prolonged and inappropriate use of videoconferencing has an enormous stress potential (41) in that video conference fatigue might be high in female students compared to males. Thus, this online learning-related stress and anxiety might be attributed to the less readiness in females for the online learning platform in this study.
Unlike the current study, other studies didn’t show gender differences in online learning readiness (42–45). Other studies reported that females were found to be more ready than males (30, 46). The difference in these results could be attributed to the differences in socioeconomic, perceptions, and computer skills between students. The type of online learning tools and variations in the field that the students are learning might be other reasons for the different findings. In the current study, all students were the year I and learning medicine at which stress is high, while the students in the previous study were learning business and management in different study years (43). At the same time, while Cisco WebEx online learning tool was used in the current study, google meet and other online learning tools were used in the previous studies.
4.3 Online learning challenges
In this study, personal and technological-related factors were more important challenges affecting online learning than institutional and curricular-related challenges. Students’ perceptions, experiences, skills, socioeconomic status, sex, and anxiety to online learning were some of the personal related challenges assessed in this study. According to the students’ responses, perception of online learning was the first challenge in this study affecting online learning. Other studies also showed that students’ understanding and perception of online learning was among the challenges affecting online learning educational platform (26, 28, and 47). Learners’ perceived usefulness, perceived enjoyment, and perceived self-efficacy were among the personal factors affecting students' online learning readiness (22, 48, and 49). Online- learning attitude of learners has a positive effect on students’ online learning readiness (50). Similarly, Yang (51) indicated that students’ self-discipline abilities and learning motivation are the primary personal-related challenges affecting online learning readiness and effectiveness.
Regarding technological challenges, unavailable of quality online learning tools for online learning was the first challenge affecting students’online learning in the current study. A study conducted before indicated that the appropriateness of Cisco WebEx, which is also used in the current study, for education was much less than the other online learning applications (52). Ratnawati and Nurhasanah (53) reported that students positively responded to utilizing Google meet for written synchronous and Zoom Cloud Meet for virtual synchronous. Another study also revealed that Zoom was significantly more attractive than Cisco WebEx (54). According to the studies conducted before, unfamiliarity with online learning technology was one of the challenges affecting online learning practice negatively (18, 55). These findings indicate that although the use of online learning applications is very helpful in the teaching-learning process, selecting appropriate and effective online learning tools to suit students’ preferences for a specific need is important.
While poor internet access in the institution was the third institutional challenge in the current study, in the study conducted by Chung et al., (30) in Malaysia, the first challenge was internet connectivity. Although institutional challenges weakly affected online learning when compared with technological or personal related challenges, lack of quality leadership in the institution was the first challenge in the current study. According to the previous literature review, leadership is one of many vital components in the successful implementation of distance learning (56). Among many factors affecting students’ online learning, quality leadership is the main one (57). Another study also showed the presence of a positive correlation between quality institutional leadership and online learning effectiveness (58).
In conclusion, students' attitude toward online learning was high, while their reediness was moderate and affected by the sexes of the students. Students’ attitude was significantly affected by computer and online learning-related skills. Extra computer training in high school also affected the students’ attitude toward online learning. The readiness of the students for online learning was more challenged by personal and technological-related factors.
To this end, extra computer training in high school is important to increase students’ attitudes toward online learning. Students should increase their computer skills for their online learning. It is required to motivate females to practice online learning and selecting appropriate online learning tools are important. Further research including instructors, educational management staff, and other stakeholders is warranted to explore the same topic at a large scale and national level to obtain a more holistic and accurate picture.