Perceptions and Satisfaction with Online Learning during the COVID-19 Pandemic Among Nursing Students at Rusangu University, Kitwe, Lusaka and Monze Districts of Zambia

During the COVID-19 pandemic, online learning became a substitute for conventional learning methods, leading to new challenges for both students and faculty.The study aimed at assessing the Perception and Satisfaction of nursing students with online learning at Rusangu University and factors associated with students' perception and satisfaction with online learning. Methods: The research design adopted for this study was a quantitative analytical cross-sectional study design. The sample was 292 Bachelor of Science in Nursing students comprising pre-service and in-service students who participated in online learning during the Covid-19 pandemic. The students were selected using a stratified proportional simple random sampling technique, encompassing full-time and Block Release Learning nursing students who participated in both online and face-to-face learning at Rusangu University's campuses in Kitwe, Lusaka, and Monze Districts of Zambia. Data was collected through a self-administered questionnaire with validated subscales. Data was entered in SPSS to analyze the data, descriptive statistics were presented using pie charts, bar charts, frequency tables, Chi square and Fishers exact tests were used to test association between variables and a multivariate logistic regression model (95% confidence interval) were employed for data analysis. Results : Out of the 292 respondents, 61% had a positive perception, while 39% had a negative perception towards online learning. Regarding satisfaction, 49% expressed satisfaction, and 51% were dissatisfied with online learning during the pandemic. Multivariate logistic regression analysis revealed that poor computer efficacy p-value 0.0001, instructor's knowledge

structured questionnaire was used to collect data [33,1,22] used this tool with an Alpha score of 0.8 .All study participants from all three campuses responded to the same questions.This helped to attribute any variations in responses to be due to actual differences among the study participants and not as a result of variations in the study tool.The structured questionnaire consisted of 28 items (5-point Likert scale) covering four major student perception domains (learners' dimensions, technological characteristics, instructors' characteristics, course management and coordination) and satisfaction were distributed to the students physically to all study respondents from all three campuses.Ethical approved was obtained from the University of Zambia Biomedical Research Ethics Committee (UNZABREC REF.NO.3173-2022).Further, clearance to proceed with the study was obtained from the National Health Research Authority (Ref.No.NHRA-R-R-1024/25/08/2022. Additionally, permission from Rusangu University's was approved before conducting the study.Participants were assured that their responses will not affect their academic performance or study at Rusangu University.Participants have a right to withdraw from the study without any reprisal.Data Analysis; Questionnaires were sorted out according to questions.The responses were verified, coded and entered into SPSS version 25.0.Descriptive statistics were presented (mean values and standard deviations) using frequency tables, pie and bar charts.Analysis of the relationships between variables was done using Fisher's exact test and Chi-square test.A multivariate and Univariate logistic regression model was constructed to identify the predictors of overall satisfaction & perception towards online classes.Fisher's exact test and Chi-square tests were used to determine the association between independent and dependent variables (perception and satisfaction towards online learning)  2 shows that about two-thirds, (65.4%) of respondents were aged between 20 and 29 years and over three quarters, (75.5%) were in their fourth year of study.Most respondents were on full-time mode of study (65.4%) and were pre-service students (64.7%).Majority of respondents, (79.5%) used data packages for internet connectivity and Zoom was the most common used online learning platform (73.3%).Over half, (56.2%) of the respondents were from Monze campus, over a quarter ( 28.4%) Kitwe campus and (15.4%) were from Lusaka campus.

ASSOCIATION BETWEEN VARIABLES
This section presents results from analysis of association between study variables.To establish the associations, the dependent variables (satisfaction and perception) were cross tabulated with respondents' baseline characteristics, instructors' knowledge and characteristics, course management and co-ordination, computer self-efficacy and technological characteristics Results are shown in Table 9.

Discussion
Studies have shown that online learning boosts student engagement and satisfaction levels while reducing the number of dropouts from online courses.A balance between online and offline activities can also be achieved, according to some literary suggestions.A few things are crucial for the impact of the online learning environment.More research is needed to gain a clearer understanding of the factors that affect the e-learning experience in a more productive way, especially in light of the COVID19 pandemic.A lot of work has been done to determine the significance of the factors that increase the positive outcomes of online learning, but this work has not yet been concluded.The discussion of the findings is based on data collected of a sample of two hundred and ninety-two (292) respondents.The respondents were nursing students at Rusangu University.The main objective was to determine the Satisfaction and Perceptions of Nursing students towards online learning at Rusangu University during the COVID 19 pandemic in Zambia at Kitwe, Lusaka and Monze campuses.Data was collected using an adapted self-administered questionnaire.This chapter is arranged as follows; the demographic factors of the respondents, computer self-efficacy, technological factors, course management, student satisfaction, student perceptions and finally the relationships perceptions and satisfaction of students towards online learning in order to determine to what extent these factors having a bearing on students satisfaction and perceptions with online learning.

Demographic characteristics
Table 2 shows that about two-thirds, (65.4%) of respondents were aged between 20 and 29 years and over three quarters, (75.5%) were in their fourth year of study.Most respondents were on full-time mode of study (65.4%) and were pre-service students (64.7%).Majority of respondents, (79.5%) used data packages for internet connectivity and Zoom was the most common used online learning platform (73.3%).Over half, (56.2%) of the respondents were from Monze campus, over a quarter (28.4%)Kitwe campus and (15.4%) were from Lusaka campus.This data shows that the sample was diverse and represented the characteristics of the population and hence the findings can be generalised as the true reflection of the student populace with online learning at Rusangu University.The following factors were determinants of satisfaction and perception of online learning among nursing students at Rusangu University weather positively or negatively and are discussed in details below.

Computer Self-efficacy
As shown in figure 5 ), the statistical analyses have verified that Rusangu University students' perceived usability and value of online learning platforms are directly influenced by their level of computer self-efficacy.This means that the perceived usability and effectiveness of online learning platforms among university students depend on their perceptions of their own abilities to use them for academic purposes (Jiang et al,2021).The benefits and advantages of online learning platforms are likely to be gradually accepted by university students as their digital self-efficacy improves.They will probably eventually find it simple to use online learning tools.The findings of this study is that computer self-efficacy influenced satisfaction with online learning as shown in table 9 with the P-value of 0.001 this precisely agrees with Jiang et al, 2021 in which it was established that students self -computer efficacy contributed to the technology acceptance model of learning among universities in China, similarly with the findings of Akimanimpaye and Fakude, 2015 whose study established that computer self-efficacy contributed to students attitude towards online learning at the University of Western Cape.Further the study determined that students who has poor computer self-efficacy had 0.12 odds of taking an online course compared to those with good computer self-efficacy had 0.39 odds of taking an online course, this shows that computer selfefficacy is a determinant of satisfaction with online learning at Rusangu University.Table 9 shows that computer self-efficacy contributed to the student's perception of online learning with the pvalue of 0.001 this is agreement with Khaffaf 2013, who established that computer self-efficacy influenced that perception of online learning among nursing students at Mosul university.This study agrees with Opeyemi et al, 2019 and established that the those students who had poor computer self-efficacy had 0.19 odds perception of not taking an online course compared to those with good computer.Therefore, computer self-efficacy is a determinant of students perception with online learning.

Instructor's Knowledge and Characteristics
The facilitation skills and knowledge of the instructor have an impact on the success and learning outcomes of the students.The instructors who were providing online instruction during the pandemic had an added duty.They had to adapt to a shifting environment, honing their technical abilities in the process and helping students who were unfamiliar with the setting with their technical knowledge.As can be observed from figure 6, over two-thirds of the respondents, (66.8%) perceived their instructors knowledge and characteristics as good while one-third (33.2%) expressed a poor perception of their instructor's knowledge and characteristics.The findings demonstrate that, in the face of the epidemic, instructor facilitation and knowledge are significant determinants of students' learning outcomes and happiness with their online learning experiences.These findings are consistent with the finding of Hsu et al. 2019 in their study that found that the instructor's knowledge is of great importance in the students learning when introducing a mode of learning.Instructor's knowledge of how to engage students with online platforms is of paramount importance to students satisfaction and perception with online learning.Table 9 shows that instructors knowledge to engage students online significantly contributes to students satisfaction and perception with the p-value of 0.001, this shows that there is a strong relationship between instructor's knowledge and student's perception and satisfaction with online learning hence rejecting the null hypothesis.This study also establishes in table 10 and table 11 that instructors who had poor knowledge had 0.21 odds of students not likely to take up their course to compared to those with good knowledge with engaging students with online learning.This is in agreement with willet et al, 2022 established that students' perceived engagement with faculty and classmates predicted their perceived overall effectiveness of the online course.This aligns with the larger literature on best practices in online learning design.Extensive research prior to the pandemic has confirmed that the effectiveness of online learning is determined by a number of factors beyond the tools used, including students' interactions with the instructor and classmates.Online students may feel isolated due to reduced or lack of interaction , therefore in designing online learning experiences, it is important to remember that learning is a social process .Faculty's role is not only to transmit content but also to promote the different types of interactions that are an integral part of the online learning process (Zheng et al, 2021).The online teaching model in which faculty uploads materials online but teach it in the same way as in the physical classroom, without special effort to engage students, doesn't make the best use of the online format.Putting the "sage on the screen" during a live class meeting on a video conferencing system is not different from "sage on the stage" in the physical classroom both provide limited space for engagement.Such one way monologue devalues the potentials that online learning presents.In light of the critical role that social interaction plays in online learning, faculty are encouraged to use the interactive features of online learning platforms to provide clear channels for student-instructor and student interactions.In the open-ended comments, students highlighted several instructional strategies that they perceived to be helpful for learning.For live online classes, these included conducting breakout room activities, using the chat box to facilitate discussions (Zheng et al,2021).

Course management and co-ordination
According to Eom et al. (2017), evidence shows that the course format had an impact on both student satisfaction and learning outcomes.As shown in figure 7, (75.7%) of the respondents indicated that course management and co-ordination was poor, while (24.3%) reported that it was good.The findings indicate that student satisfaction and perceived learning outcomes are directly influenced by the course layout.The findings are in contrast to those of Eom et al. ( 2017) and support those of Gray and DiLoreto (2020).The online course structure during the COVID19 pandemic was not suited for online learning.The framework of the course was created for conventional, offline learning.To accommodate online learning, the course format had to be changed, which might have improved student learning outcomes.In an online setting, an instructor serves as both a designer and a facilitator (Martin, Wang, and Sadaf, 2018).Table 9 shows that good course management is a strong determinant of online satisfaction and perception with the p-value of 0.001 meaning if the online courses are well managed the likelihood of students enjoying the course is high compared to when the course is not well managed, the study found in table 10 and 11 respectively that poor course management had 0.12 odds of satisfaction and 0.10 of perception compared to those who said they were satisfied and had a positive perception, this aligns with the findings of Zheng et al 2021, who started that a variety of interaction methods such can have students experience with online worthwhile or more interesting hence there is need combine both synchronous and asynchronous mode.For live online classes, these included conducting breakout room activities, using the chat box to facilitate discussions, polling, and integrating gameplay with apps such as Kahoot (a student response tool for all platforms, allows teachers to run game like quizzes and build presentations with embedded quizzes.For self-paced classes, students appreciated that faculty held virtual office hours or subsequent live online discussion sessions to reinforce understanding of the preassigned materials (Zheng et al, 2021).

Technological characteristics associated with online learning
A growing variety of technology platforms have become popular recent years.adopted to aid learning in higher education (Habib et al., 2021, Mpungose, 2020, Su & Chen, 2020, and Yen Yunusa & Umar, 2021; et al., 2018).Learning management systems (LMSs) for example, one of the most significant and necessary.Figure 8 shows that just over half of the respondents, (51.7%) described technological characteristics associated with online learning as poor whereas, about half, (48.3%) described the technological characteristics as good.These findings are consistent with other studies which have observed done by Jiang et al, 2020 among university students in China that found that poor technology have an imparct on students perception and satisfaction of online learning ,conversely if the learning platforms and technology is good the perception and satisfaction of students is enhanced.Further -more the study establishes in table 9 that good technological characteristics such as the students ability to navigate the virtual learning environment has a great significant on the students satisfaction and perception with the p-value of 0.001 this aligns to the finding of Baber, 2020, who stated that there are numerous pros of online learning, especially in modern times, but conversely, there are some concerns that lead to the attrition of online learners and that eventually impede the progress of online courses and the solution can be found by examining students satisfaction and perception of online learning .The study also found in table 10 and 11 that students with poor technology had a 0.13 odds of satisfaction compared to those with good technological characteristics further those with poor technological characteristics had 0.12 odds of perception compared to those with good technological characteristics.

Factors associated with satisfaction and perception of online learning during the COVID-19 pandemic at Rusangu University.
Table 9 shows that satisfaction with online learning during COVID-19 was significantly influenced by mode of study (p=0.005),level of training (p=0.005),computer self-efficacy (p<0.001),instructor's knowledge and characteristics (p<0.001),course management and co-ordination (p<0.001) and technological characteristics (p<0.001).These findings are consistent with the study done by Elshami et al, 2021 who stated that Student satisfaction has a significant impact on the results of online courses, and it is related to a number of elements, including content, user interface, learning community, and learning performance.Conversely on the aspect of satisfaction the study found that the students were not satisfied with online learning as shown in figure 10, 51% (n=149) of the respondents were poorly satisfied with online learning during the COVID-19 pandemic while 49% (n=143) expressed satisfaction, these findings are consistent with the findings in study done in United Arab Emirite which stated that Overall satisfaction correlated with technology satisfaction, and the most common source of dissatisfaction was related to the instructor's accessibility and availability.The sudden shift to online delivery of the curriculum due to COVID-19, in which there was insufficient time for preparation, as well as the stressful working conditions of the pandemic itself, could be blamed for decreased satisfaction (Elshami et al,2021).Even with the best-designed online course, many authors have claimed that technical issues might make learning ineffective.The same authors also claimed that aspects of technology, such as how much technical help they can count on and how user-friendly their courses' technological infrastructure is, may affect how satisfied students and teachers are with online education ( Jiang et al,2020).Table 9 further shows that mode of study (p<0.001),level of training (p<0.001),computer self-efficacy (p<0.001),instructor's knowledge and characteristics (p<0.001),course management and co-ordination (p<0.001) and technological characteristics (p<0.001) were significantly associated with perception towards online learning during the COVID-19 pandemic..These findings were consistent with study in South Korea by Hansnan Baber, 2020 that stated "The students perceive interaction, motivation, course content, and the role of instructor to be key determinants of the positive learning outcome.The positive learning outcome also has an impact on student satisfaction, therefore, the higher the perceived learning outcome in online learning, the higher satisfaction of students amid the pandemic".

Study Limitation
There may have been individual differences between students in the online and the face-to face cohorts, such as motivation, learning style, and prior knowledge, that could have impacted the observed outcomes.Additionally, even though course content and assessment methods were largely the same in 2019 and 2020, changes in other aspects of the course could have impacted students' course performance.Some faculty may have been more compassionate with grading (e.g., more flexible with assignment deadlines) in summer quarter 2020 given the hardship students experienced during the pandemic.On the other hand, remote proctoring in summer quarter 2020 may have heightened some students' exam anxiety knowing that they were being monitored through a webcam.The existence and magnitude of effect of these factors needs to be further investigated.This present study only examined the correlation between students' perception and satisfaction with online learning and their determinants .Other factors that might impact their perception and satisfaction of online format need to be further researched in future studies.Another future direction is to examine how students' perceived online engagement correlates with their actual course performance.This could not be done because the data collected for the present study was anonymous, therefore cannot match students' perceived online engagement data with their course grades to run this additional analysis.

Recommendations
From the findings of the study, if the online learning program is to be improved and meet the students learning needs since online learning is one of the technological advancement in Nursing education and indeed education in general.It is important for the University to consider the following recommendations.1.The course structure of online courses during the pandemic COVID19 was not designed for online learning.The course structure was design for normal, offline learning.The course structure had to be modified to fit the needs of online learning, which may have enhanced the student learning outcome.Therefore the University should design course materials that meet the need for online learning as shown in results that course structure was poor 2. The university should offer orientation to both faculty and students on the use of online platforms such as moodle which can be used for both synchronous and asynchronous modes and other platforms which are used for online learning.3. The quality of online learning is also influenced by the robustness of learning platforms, the university should invest in technology and good internet access as this support online learning for both students and faculty without interruption.Studies done in the developed world have also shown that good learning platforms have been recognized as irreplaceable emergency educational tools in the transition to online learning during the COVID-19 pandemic (Zhu & Peng, 2020).4. The university to orient faculty on online class handling, preparations of online assessment tools, submission of assignments, marking and grading of assessments and tests.5.The study has revealed that computer self-efficacy is an influential factor of satisfaction that cannot be ignored.Therefore, University students should gradually strengthen their basic computer competence in different ways so as to enhance their computer self-efficacy.The university should enter into memorandum of understanding with companies that provide computers at affordable prices so that students can acquire these gadgets for their learning.6.The University to consider offering same courses on blended learning mode so as to sensitize the students to begin adapting to online learning than switching to online learning abruptly.

Conclusion
Based on the results obtained nursing students perception and satisfaction had generally favorable towards online learning during the COVID-19 pandemic and that their perceived engagement with faculty, computer self-efficacy, course coordination and technological characteristics predicted their perception and satisfaction of the online learning.Most notably, this demonstrated that online learning during the pandemic could achieve similar or better learning outcomes than face-to-face learning before the pandemic.Findings of the study could contribute significantly to the literature on online learning during the COVID-19 pandemic in health sciences education.The results could also inform future online learning design as we re-envision the future of online learning.

Table 10 : Univariable and multivariable logistic regression analysis results on satisfaction with online learning during the COVID-19 Pandemic at Rusangu University Variables Univariable analysis Multivariable analysis cOR CI (95%) p-value aOR CI (95%) p-value
0.10, 0.33 < 0.001 cOR= Crude Odds Ratio, aOR= adjusted Odds Ratio, CI= Confidence Interval Multivariable analysis results in table