Attitude and Readiness to Online Learning and Challenges among First-Year Medical Students

2.1 Study area and design

This study was conducted at Addis Ababa University (AAU), the College of Health Sciences, and the School of Medicine. AAU is the first higher education in Ethiopia established in 1950. School of medicine, school of pharmacy, school of public health, and school of allied and health sciences are schools within the College of Health Sciences. Among other medical Schools in Ethiopia, the School of Medicine is the biggest and oldest School established in 1964 as a Faculty of Medicine to produce medical doctors (23). A quantitative cross-sectional design was used in this study.

2.2 Study population

The population in this study were year I undergraduate medical students. These students were enrolled at Addis Ababa University, School of medicine from all over the country, Ethiopia. Students were invited to participate in an online survey regardless of their prior knowledge and the type of their schools (private versus government). These students belonged to the first cohort of medical students who had learning activities using an online learning platform. All courses in the first semester of the year (2021) were given to these students using Cisco WebEx online teaching tool with no physical interaction in the classroom environment. It was not hybrid which can offer flexibility to students between different learning platforms. Some of these students complied about this online learning platform and not others. There were dilemmas about the continuation of education using an online platform in that this research has been conducted to evaluate the attitude readiness and challenges based on the implemented online learning modality given in the first semester using Cisco WebEx. The study was conducted in the academic year 2021–2022.

2.3 Sample size

Regarding the sample size, the online survey questionnaire was sent to all year I undergraduate medical students (n = 300) after they have been communicated and the objectives of the study elucidated. One hundred fifty (150) year I undergraduate medical students who belonged to two sections of the same courses participated and responded to the questionnaire.

2.4 Data collection tool

In this study, primary quantitative data were collected using an online survey questionnaire. After the purpose of the study has been discussed with the students, self-administered google derive questionnaire was sent to the student using their email addresses.

Socio-demographic characteristics and students’ computer knowledge and skills were collected using closed-ended questions. The attitude of the students toward online learning, level of readiness, and the challenges (personal, institutional, curricular, and technology) were assessed using the five points Likert-scale with the following scores: 5 = Strongly Agree (SA), 4 = Agree (A), 3 = Partially Agree (PA), 2 = Disagree, and 1 = Strongly Disagree (16, 17). Based on a previous study (24), the mean score for each item was analyzed with the cutoff point at intervals of 0.8 (4/5 = 0.8, as there are 5 categories and 4 ranges of data (i.e., 5 − 1 = 4), the values were categorized as follows (Table 1).

2.5 Statistical analysis

The collected data were analyzed using SPSS (version 26). An independent t-test was used for quantitative data to compare the mean score difference between the two categories. One-way ANOVA followed by Post Hoc Turkey multiple comparison tests were used to compare the mean score difference between three and more categories. The data were expressed as means ± standard deviation (SD). Differences with p < 0.05 were considered statistically significant.

3.1 Socio-demographic characteristics of the Students

Out of 300 medical students who received the online survey questionnaire, 150 students responded with a response rate of 50%. Among students who completed the online survey questionnaire, 54.7% were females, while 45.30% were females. Most of the students (59.3%) joined the University from public high schools. Only 38.7% of students had extra computer training in their high school, and more than half of the students (55.3%) had voluntary activities in high school (Table 2).

3.2 Basic computer and other online learning-related skills

Most of the students had excellent basic computer (51.3%), online learning access (61.3%), and internet use (53.3%) skills. 39.3% and 32.7% of the students had excellent software downloading and network organizing skills, respectively (Table 3).

3.3 Students' attitudes toward online learning practices

Few (9.3%) and larger (71.3%) proportion of the students agreed that online learning increases knowledge and grade, respectively. Larger proportion of students (97.4%) agreed on the betterment of online learning than conventional in terms of grade but not in terms of knowledge. Generally, the grand mean Likert-scale score of students ' attitudes toward online learning was 3.49 (Table 4).

M: Mean, SD: Standard deviation, Likert-scale mean scores of 1.00-1.80: Strongly Disagree (SD)/very low attitude, 1.81–2.60: Disagree (DA/ low attitude), 2.61–3.40: Partially Agree (PA) /moderate attitude, 3.41–4.20: Agree (A) / high attitude and 4.21-5.00: Strongly Agree (SA) / high attitude.

3.4 Students’ online learning attitude differences between categories

Students who had extra computer training in their high school showed significantly higher attitudes toward online learning than those who had no training (t (148) = 2.57, p = .010). Students’ attitude toward online learning was also significantly affected by basic computer skills (F (3,146) = 5.65, p:.001), software downloading, installation & application skills (F (3,146) = 4.92, p = .003), network organizing skills (F(3,146) = 3.19, p = .025), online learning accessing skills (F(3,146) = 2.71, p = .048) and online learning destructors eliminating skills (F(3,146) = 15.99, p = .000). The Post Hoc multiple comparison test showed that students who were excellent in basic computer skills had significantly higher attitudes than those who were poor (mean = 3.63; SD = .36 vs mean = 3.12; SD = .67, p = .007; SD = .53, p = .002) and good (mean = 3.63; SD = .36 vs mean = 3.39; SD = .38, p = .024) in basic computer skills. Students who were excellent in application downloading skills had significantly higher attitudes than those who were poor (mean = 3.63; SD = .36 vs mean = 2.93; SD = .53, p = .002) in application downloading skills. Students having excellent online learning destructors eliminating skills had significantly higher attitudes toward online learning than having poor skills (mean = 3.71; SD = .26 vs mean = 3.07; SD = .39, p = .000). However, sex and high school category had no effects on students’ attitudes toward online learning (Table 5).

DF: Degree of Freedom, t: test statistics, F: Fisher, Likert-scale mean scores of 1.00-1.80: Strongly Disagree (SD)/very low attitude, 1.81–2.60: Disagree (DA/ low attitude), 2.61–3.40: Partially Agree (PA) /moderate attitude, 3.41–4.20: Agree (A) / high attitude and 4.21-5.00: Strongly Agree (SA) / very high altitude.

3.5 Online learning readiness

Although 82% of students feel stressed while they are using online learning, 90.6% of the students agreed that they are motivated to use online learning for all courses and 92.5% for selected advanced courses. However, the all-over grand mean score of students’ online learning readiness was 3.23 (Table 6).

M: Mean, SD: Standard deviation, Likert-scale mean scores of 1.00-1.80: Strongly Disagree (SD)/very low readiness, 1.81–2.60: Disagree (DA/low readiness, 2.61–3.40: Partially Agree (PA) /moderate readiness, 3.41–4.20: Agree (A) / high readiness and 4.21-5.00: Strongly Agree (SA) / very high readiness.

3.6 Online learning readiness difference between Categories

Males were more ready than females for online learning (t (148) = 2.17, p = .032). Significant difference in readiness (F (3, 146) = 2.76, p = .042) was observed between students categorized based on online learning destructors eliminating skills (Table 7). However, the Post Hoc Tukey multiple comparisons test didn’t show significant differences.

DF: Degree of Freedom, t: test statistics, and F: Fisher, M: Mean, SD: Standard deviation, Likert-scale mean scores of 1.00-1.80: Strongly Disagree (SD)/very low readiness, 1.81–2.60: Disagree (DA/low readiness, 2.61–3.40: Partially Agree (PA) /moderate readiness, 3.41–4.20: Agree (A) / high readiness and 4.21-5.00: Strongly Agree (SA) / very high readiness.

3.7 Online learning challenges

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.