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Abstract

The study examined students’ attitudes toward online learning classes introduced during the pandemic lockdown among students at the University of Lusaka. The study adopted a quantitative methodology with descriptive and comparative design methods. The study developed an attitude scale toward online learning and administered it to 1,542 students registered at the University of Lusaka. The data obtained from the scale were analyzed by using SPSS 26.0 package software. The study results show that students hold a negative attitude toward online learning, implying that they are more unlikely to accept online classes. The Pearson Chi-Square test statistics show that gender, age, work status, study mode and internet access significantly influenced students’ attitudes toward online learning classes. The results are not encouraging regarding introducing online learning classes, especially for full-time and younger students. Therefore, we recommend that the introduction of online learning classes at the University of Lusaka be approached with caution to students in the full-time learning mode without neglecting the importance of face-to-face learning. Implications of this research are relevant to educational interventions, academic achievement, and technological advancement concerning university students.

Keywords

online learning Covid-19 pandemic education attitudes higher learning

Introduction

Online learning is not a new event, but it is something we can trace back to the late 1990s. However, after the emergence of the novel coronavirus disease, the world has witnessed the flourishment of online platforms (Ismaili, 2021). For instance, institutions of learning across the globe have explored and implemented the option of teaching and learning online (Uleanya et al., 2021). Moreover, the higher learning institutions in Zambia are no exception. Institutions like the University of Lusaka introduced an online or blended learning approach. Students attend a physical school part-time and spend the remaining hours in remote asynchronous instruction. Besides, all students were introduced to online learning classes through various platforms, such as Moodle, Google Meets, Microsoft Teams and Zoom, to attend classes. However, little is known about students’ experiences concerning online learning. This study, therefore, examined the attitudes of students in higher learning institutions toward online learning and the associated factors.

The COVID-19 pandemic dramatically changed the way learning was delivered globally. In Zambia, higher education institutions had to shift to online education delivery to curb further virus transmission. This trend has continued (Mukuka et al. (2021). However, the sudden shift to online classes raised concerns among many teachers and students due to the country’s unstable internet access and limited electronic devices.

Further, transitioning rapidly to online learning has been a severe struggle (Himoonga & Phiri, 2020). The challenge has seriously affected everyone, including institutions, teachers and students. According to Zambia Information and Communications Technology Authority (ZICTA, 2020), the high costs of ICT ownership, digital literacy, and access to Internet services remain significant barriers to access for many people. Besides, many people have lost their job or had a salary cut due to the coronavirus pandemic (ZICTA, 2020), negatively impacting their internet access. Hence, making the research on the effects of these challenges on student attitudes toward online education delivery more interesting.

Furthermore, several colleges and universities worldwide shifted to online learning, and a surge in a virtual learning environment was witnessed in many countries worldwide. The disruptions have compelled students to adjust their traditional learning habits and adopt new online learning habits. According to UNESCO (2020), more than 80% of the global student population has been affected by the crisis.

According to Chola et al. (2020), the Covid-19 pandemic brought panic among parents and students. Further, Veletsianos and Houlden (2019) observed that the pandemic has both health and social problem effects. For instance, many students experienced fear, loneliness, and uncertainty during lockdown concerning academic activities such as classes, exams, and graduation (Veletsianos & Houlden, 2019). Conventionally, online learning is now part of Internet learning for trainers and learners. However, its success depends on its acceptability by the learners. Therefore, lessons from the covid-19 predicament allow researchers to research how the lockdown during the pandemic affected the learners. Thus, the study focuses on students’ experiences in higher learning institutions by examining their attitudes toward online learning classes introduced during the lockdown due to the covid-19 pandemic.

Student attitude toward online learning is a critical factor in the learning environment supported by online learning tools. According to Albarracin and Shavitt (2018), people’s attitudes relate to their knowledge, belief, feeling, and behavior toward an attitude object. Maio and Haddock (2009) further indicate that strong attitudes influence behavior. Thus, learners’ positive attitudes lead to the effective delivery of learning strategies.

Several studies have assessed students’ attitudes toward online learning (Johnson, 2013; Kisanga & Ireson, 2016; Zhu et al., 2013). However, many of these were conducted during normal times. The emergency of the Covid-19 pandemic created a unique situation for us to examine students’ attitudes in virtual classrooms during the pandemic. While much is written on students’ perceptions of e-learning modalities in some countries, much less is known about students’ attitudes toward online learning in Zambia, especially after the introduction of online classes that arose due to the Covid-19 pandemic.

Internet connectivity remains poor and costly for most Zambians (ZICTA, 2020), thus, making the delivery of online classes more challenging to institutions, teachers and students. It was, therefore, essential to undertake this study and contribute to developments in delivering education online during pandemics such as Covid-19. The findings contribute to the potential prospects online education delivery can hold in the future, especially in resource-strained countries like Zambia.

The eLearning in Zambia

Like other countries, the education system in Zambia is based on traditional classroom education, where pupils and students attend physical classes (Nyashanu et al., 2023, p. 15). However, with the coming of Covid-19, the delivery of general and higher education systems changed (Lufungulo et al., 2021). Schools were forced to close and seek other alternative means of education delivery. The country became one of the many countries globally that prematurely closed all schools to adhere to the international and national guidance for social distancing, quarantine and self-isolation (Lufungulo et al., 2021). Thus, all higher learning institutions were recommended to shift to online learning. The use of e-learning helped several universities across the globe to ensure continuous delivery of education amid the pandemic in a manner that is flexible, at a low cost to education providers and a high cost to a learner, and user-centered, among many others (Nyashanu et al., 2023, p. 20). Today, e-learning has become part of many universities’ teaching and learning approaches nationwide. However, its adoption is still immature, like in other low- and middle-income countries (Gachanja et al., 2021). The adoption of e-learning faces many challenges that include individual, technological, cultural, environmental, and pedagogical barriers (Gachanja et al., 2021). For instance, many teachers had to adopt new pedagogy without sufficient training, with students facing internet challenges and navigating new learning platforms. Besides, higher learning institutions faced challenges converting materials to the online platform and insufficient technological infrastructure support (Nyashanu et al., 2023, p. 30). This paper examined students’ attitudes toward online learning classes introduced during the pandemic lockdown among students at the University of Lusaka.

Theoretical Framework

The attitude construct remains a crucial area of research in the social and behavioral sciences (Wood, 2000). Thurstone (1931) defined attitude as an effect for or against a psychological object; early theorists used to affect in a sense we now refer to as attitude (Ajzen & Fishbein, 2005). Looking into students’ attitudes toward online learning classes, the study adopted the Theory of Reasoned Action developed by Ajzen and Fishbein (1975) as its framework. The theory explains how behavioral intentions, attitudes and subjective norms are linked. Based on this understanding, we can say that attitudes are helpful predictors of actions. For Jogezai et al. (2021), the theory is relevant to anyone who intends to know about a person’s attitude toward an object (online learning). This study has theorized TRA with students’ attitudes toward online classes during the lockdown experienced due to the pandemic. The shift to online learning classes by higher learning institutions during lockdown was, thus, assumed to affect students’ behaviors due to its abrupt introduction.

Online Learning

Online learning, also called online education, is a vital element of e-learning (Jogezai et al., 2021). According to Uleanya (2022), this type of learning occurs virtually online. Thus, in this paper, online learning is conceptualized as a formal type of education where teaching and learning activities are conducted over the internet with students joining the classes from their homes.

Students’ Attitude to Online Learning

According to Brown (2012) and Rogers-Estable (2014), students’ attitudes and learning expectations strongly influence participation in online classes. Brown considered learners’ pedagogical beliefs and current practices as integral to students’ participation in learning activities (Brown, 2012). In literature, several studies have been done on the impact of the covid-19 pandemic on education, including those focusing on attitudes (Anwar & Wahid, 2021; Oraif & Elyas, 2021; Tanjung & Utomo, 2021). For instance, Tanjung and Utomo (2021) investigated EFL undergraduate students’ perception of online learning environments through a questionnaire that included background questions and students’ experiences of the learning environment before and after the pandemic. Research findings showed that students had different amounts of familiarity with social media and educational websites and that the use of these platforms has increased slightly due to the government’s instructions to use these platforms for learning (Tanjung & Utomo, 2021).

Furthermore, a study in Saudi Arabia explored 379 female EFL students’ engagement in online courses through Student Course Engagement Questionnaire (SCEQ). The findings revealed that teachers should be engaged in the learning process and change their position of authority to that of a supportive member. Other results showed that students were involved in online classes (Oraif & Elyas, 2021).

Anwar and Wahid (2021) have researched the students’ perceptions of online learning. The study questionnaire included close-ended and open-ended questions that captured learners’ participation, the teacher’s role, and instructional design and delivery. The authors found that students were content with online classes’ instructional design and delivery (Anwar & Wahid, 2021). However, they believed there was still room for improvement. Students also complained about internet connection problems. Another finding was that students were not satisfied with the level of interaction during online classes. The authors suggested that teachers be more encouraging and have the role of facilitator (Anwar & Wahid, 2021).

Adnan and Anwar (2020) studied 126 higher education students to perceive their perspectives on online education. They used an adapted version of Bernard et al.’s (2004) questionnaire. It was concluded from the data analysis that students believed traditional classes were more efficient than online classes. The writers stated that online learning could not have effective outcomes in underdeveloped countries. Another study was done by Lengkanawati et al. (2021), who examined EFL learners’ views or experiences with virtual classes. The study findings indicated that learners agreed to have classes virtually as the best learning method during the pandemics such as covid-19. However, face-to-face learning was still favored (Melvina et al, 2021). Another point was that instructors do not have enough experience with online teaching.

Further, Mukuka et al. (2021) explored the experiences of students in primary schools with remote learning during the school closures in Zambia. The study reported that most students had no access to ICT, electricity, and internet services. The researchers further recommended that developing countries build ICT infrastructure supporting virtual learning environments (Mukuka et al., 2021) because the online or virtual learning modes may not favor schools with limited resources such as ICT services and electricity (Olivier, 2020).

Factors Associated With Students’ Attitudes Toward Online Learning

The research explored factors associated with the attitudes of students toward virtual classes. Several factors influence online learning attitudes from the literature review, so combining them all in one study would be hard. Thus, we have reviewed some recent literature that aligns with this study’s objectives. For example, gender influences students’ attitudes (Cai et al., 2020). The meta-analysis shows that a male learner holds a more positive attitude toward online learning than a female learner (Cai et al., 2020), despite a reported small effect size. However, others, like Alothman et al. (2017), found no gender differences in the attitudes among undergraduate students toward virtual learning environments.

Further, access to high-speed internet is another factor that can influence positive learning experiences among students. Olaniran and Uleanya (2021) explored why international students leave their home country to study in a nation like South Africa at a postgraduate level. The availability of reliable and stable internet service was reported as a reason for this preference. This implies that internet connectivity is pivotal in students’ learning experience. According to Uleanya and Gamede (2019), online learning environments usually require computers and a reliable internet connection. Lack of these services tends to affect the learning abilities of students, consequently, their academic performances. According to different studies (Anifowoshe et al., 2020; Kapasia et al., 2020; Owusu-Fordjour et al., 2020), during the outbreak of Covid-19, many students had limited access to internet connectivity. This suggests that students’ learning abilities and academic performances during the pandemic were affected negatively based on limited internet connectivity.

Other factors include learners’ engagement with the learning environment, ability to control and manage their learning, and constant interactions with the instructor. For instance, Holley and Oliver (2010) explored students’ biographical narratives via their learning accounts. They revealed that students’ ability to control technology and manage learning experiences and expectations played a pivotal role in students’ engagement with online learning (Holley & Oliver, 2010). Additionally, Lee et al. (2011) found that students valued and benefited from virtual learning environments with constant interaction with instructors and their peers.

Furthermore, Gilbert et al. (2007) found that lacking resources and outdated materials could leave students dissatisfied and unhappy with their online learning experience. According to Felix (2001), several other external factors also impact satisfaction with online courses. Specifically, he reported that time flexibility, reinforced learning, privacy, a wealth of information, the ability to repeat exercises, and gaining computer literacy were all advantages of online learning by students (Felix, 2001). This is promising, as it suggests that students appreciate the pedagogical benefits of online education.

In summary, a literature review has shown that several factors can cause satisfaction and dissatisfaction and further influence their attitudes positively or negatively. Therefore, this research builds upon various studies by examining students’ attitudes toward online learning and factors that can significantly affect students’ perceptions. Among the factors reviewed are internet access, control of the learning environment, experience, expectations, and remote support. Specifically, the study provides answers to the questions below:

Research Questions

What are some of the prevailing student attitudes toward online learning?

What are some of the factors associated with students’ attitudes toward online learning?

Methods

The research is quantitative in methodology with descriptive and comparative design methods. The research design enabled us to collect quantitative data. The study was conducted at the University of Lusaka using a questionnaire. The research aimed to answer questions according to assumed differences in attitudes toward online learning due to demographic factors, internet access and previous experience with online learning among the students.

Measures

The study describes the prevailing attitudes toward online learning among students at the University of Lusaka. In addition, it determines whether attitudes toward online learning are associated with demographic factors, internet access and previous experience with online learning among the students. Socio-demographic variables include age, gender, mode of study, year of study, and work status. Other factors included were internet access and previous experience with online learning measured based on their questions (Figure 1).

Figure 1.

Assumed factors associated with attitudes towards online learning.

Data Collection Instrument

The research developed a standardized questionnaire. The questionnaire consisted of two sections: (a) demographic information, access to the internet and previous experience with online learning; and (b) attitudes toward online learning. The attitude scale consisted of 20 attitudinal items measured on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree) regarding students’ attitudes toward online learning. Based on the research about students’ online learning attitudes (Knowles & Kerkman, 2007; Yudko et al., 2008), the instruments measuring online learning attitudes included the students’ affective perception of belief in and intention of learning online. Similarly, this research consisted of such items.

Reliability of the Instrument

The questionnaire was pre-tested with separate students 3 months before the data collection phase. Both the pre-test and post-test results indicated high reliability of the scale items. The Cronbach’s Alpha for the pre-test and post-test were .874 and .908, indicating high overall internal consistency among the 20 items of students’ attitudes toward online learning at the University of Lusaka, respectively.

Participants

A sample of 1,542 students registered at the University of Lusaka was recruited for the study through a convenience sampling method. Among the 1542 participants who completed the online questionnaire, 52.8% were females, and 47.2% were males. Most students (35.7%) were in the age group of 21to 25 years old. Furthermore, most students were not working (66.4%), and most were studying full-time (61.5%). Concerning the year of study, the majority of the participants were first-year students (36.6%), followed by second-year students (30.4%). Table 1 shows more details on the descriptive statistics.

Table 1.

Descriptive Characteristics of the Participants.

Variables	Levels	Sample	Percentage (%)
Gender	Male	728	47.2
Gender	Female	814	52.8
Age categories	17–20	484	31.4
	21–25	550	35.7
	26–30	180	11.7
	31 and above	328	21.3
Work	Employed	518	33.6
Work	Unemployed	1,024	66.4
Study mode	Full-time	949	61.5
	Part-time	157	10.2
	Distance learning	436	28.3
Year of study	1st year	564	36.6
	2nd Year	469	30.4
	3rd Year	296	19.2
	4th Year	205	13.3
	5th Year	8	0.5

Data Collection

The study collected data through a questionnaire that had closed-ended questions. The questionnaire was administered online in Google Forms. Participation in this study was voluntary. Any student registered and studying at the University of Lusaka during data collection was eligible to participate. Data were collected for 1 month.

Ethical Consideration

Before data collection, permission from various schools to access the students was sought, and the management granted overall permission to conduct the study at the University of Lusaka. Further, the ethical waiver was obtained. The study obtained informed consent from all the respondents, and no personal details were captured during data collection.

Data Analysis

The data obtained from the scale were analyzed by using SPSS 26.0 package software. The analysis was divided into two sections; the first was descriptive. The second section consisted of inferential statistics. Pearson Chi-square test statistic was computed to explore the association of demographic factors, internet access and previous experience with attitudes toward online learning among the students. Pearson Chi-square test was useful due to its ability to compare observed frequencies with the statistically generated values that would be expected if there was no relationship between the two variables under investigation (Field, 2013). Further, the minimum assumption of greater or equal to five of the allowed frequency in any was observed (Field, 2013).

The scale consisted of a total score of 100 for a participant with a stronger positive attitude and 20 for a participant with a stronger negative attitude toward online learning. Thus, before the analysis, scores were grouped into two attitudinal categories: Negative attitude (0–55) and Positive attitude (56–100).

Results

Prevailing Students’ Attitudes Toward Online Learning

The average attitude score was 56, with minimum and maximum scores of 20 and 100, respectively. The score range was 79. According to Table 2, most participants scored from 20 to 55 and were categorized as having negative attitudes toward online learning (50.9%). Furthermore, 49.1% of the participants scored from 56 to 100 and were categorized as having positive attitudes toward online learning (Table 2).

Table 2.

Students’ Attitudes Toward Online Learning.

Attitude scale	Scores range	Frequency	Percentage (%)
Negative attitude	20–55	785	50.9
Positive attitude	56–100	757	49.1

Table 3 shows that male students had better attitudes toward online learning than female students, with average attitudinal scores of 57 and 55, respectively. The average attitudinal score for working students (65) was better than for non-working students (52). Similarly, the average attitude scores for the part-time (65) and distance learning (63) students were better than the score for the full-time (51) students. Across the years of study, third-year students scored the lowest average attitude score (53) compared to first-years (56), second-years (57), fourth-years (57) and fifth-year students (57). Across age categories, the older students expressed stronger positive attitudes toward online learning compared with younger students, 17 to 20 years (51), 21 to 25 years (52), 26 to 30 years (63), and 31 years and above (66).

Table 3.

Students’ Attitudes Toward Online Learning by Demographic Factors.

Variables	Levels	N	Mean (SD)	Min	Max
Gender	Male	728	57 (14.3)	21	96
Gender	Female	814	55 (14.7)	22	100
Work status	Employed	518	65 (13)	24	98
Work status	Not Employed	1,024	52 (13)	21	100
Mode of study	Full-time	949	51 (13)	21	96
	Part-time	157	65 (14)	29	98
	Distance	436	63 (13)	27	100
Year of study	First Year	564	56 (14)	21	98
	Second Year	469	57 (15)	24	100
	Third Year	296	53 (15)	21	98
	Fourth Year	205	57 (14)	27	91
	Fifth Year	8	57 (16)	40	81
Age	17–20 years	484	51 (13)	21	96
	21–25 years	550	52 (14)	21	94
	26–30 years	180	63 (13)	24	98
	31 years and above	328	66 (12)	30	100

Table 4 shows that students with access to the internet hold a stronger positive attitude toward online learning, with an average attitude score of 57 compared to those without internet access (45). Furthermore, students who had previously used the internet for online learning scored similarly to those who had not, with average attitudes scores of 56 and 55, respectively.

Table 4.

Students’ Attitudes Toward Online Learning by Internet Access and Previous Experience.

Variables	Levels	n	Mean (SD)	Min	Max
Internet Access: Do you have access to the internet?	Yes	1,438	57 (14)	22	100
Internet Access: Do you have access to the internet?	No	104	45 (13)	21	81
Previous Experience: Have you used the internet for online learning purposes before?	Yes	1,252	56 (15)	21	98
	No	290	55 (14)	21	100

Factors Associated With Students’ Attitudes

According to Table 5, the results show that 52.7% of the male and 45.8% of the female students favored online learning. At the same time, 47.3% of the male and 54.2% of the female students responded negatively to online learning classes. Further, the Pearson Chi-Square test of independence showed that there was a significant association between gender and attitude toward online learning, χ² (1) = 25.36, p < .007 (Table 5).

Table 5.

Cross-Tabulation of Gender Attitude Scale Cross-Tabulation.

			Attitude scale
			Negative	Positive
Gender	Male	Count	344	384
		Expected count	370.6	357.4
		% within	47.3%	52.7%
	Female	Count	441	373
		Expected count	414.4	399.6
		% within	54.2%	45.8%

Note. The minimum expected count is 357.39. Pearson Chi-Square test χ² (1) = 25.36, p < .007.

Data analysis revealed a statistically significant dependence between work status and students’ attitudes toward online learning, χ² (1) = 257.216, p < .000. Further study in Table 6 shows that 77.8% of the employed and 34.6% of the non-employed students favored online learning classes. On the other hand, only 22.2% of the employed and 65.4% of the non-employed students did not favor online learning.

Table 6.

Cross-Tabulation of Work Status and Attitude Scale.

			Attitude scale
			Negative	Positive
Work Status	Employed	Count	115	403
		Expected Count	263.7	254.3
		% within	22.2%	77.8%
	Not employed	Count	670	354
		Expected Count	521.3	502.7
		% within	65.4%	34.6%

Note. The minimum expected count is 254.30. Pearson Chi-Square test χ² (1) = 257.216, p < .000.

Interestingly, results revealed that part-time and distance learning students expressed stronger positive attitudes toward online learning than full-time students. Table 7 shows that only 33.3% of full-time students favored online learning classes compared with 66.7% of those who responded negatively. In comparison, part-time and distance students responded positively, 77.7% and 73.2%, respectively, against those who responded negatively, 22.3% among part-time students and 26.8% among distance students. The results also show interdependence between the mode of study and students’ attitudes toward online learning, χ² (1) = 247.260, p < .000.

Table 7.

Cross-Tabulation Mode of Study and Attitude Scale.

			Attitude scale
			Negative	Positive
Mode of study	Full-time	Count	633	316
		Expected Count	483.1	465.9
		% within	66.7%	33.3%
	Part-time	Count	35	122
		Expected Count	79.9	77.1
		% within	22.3%	77.7%
	Distance	Count	117	319
		Expected Count	222.0	214.0
		% within	26.8%	73.2%

Note. The minimum expected count is 77.07. The Pearson Chi-Square test χ² (1) = 247.260, p < .000.

Similarly, there is also an interrelation between age and attitudes toward online learning, χ² (1) = 251.032, p < .000 (Table 8). From the distribution of responses by age group in Table 8, it is clear that most of the respondents who would prefer online learning are from the age groups of 26 to30 years old (72.2%) and 31 years old and above (80.5%) showed positive attitudes toward online learning, respectively. On the other hand, only 37.3% of students with positive attitudes were 21 to 25 years old, and only 32.6% among those between 17 and 20 years old responded positively to online classes.

Table 8.

Cross-Tabulation of Age and Attitude Scale.

			Attitude scale
			Negative	Positive
Age categories	17–20 years	Count	326	158
		Expected count	246.4	237.6
		% within	67.4%	32.6%
	21–25 years	Count	345	205
		Expected count	280.0	270.0
		% within	62.7%	37.3%
	26–30 years	Count	50	130
		Expected count	91.6	88.4
		% within	27.8%	72.2%
	31 years and above	Count	64	264
		Expected count	167.0	161.0
		% within	19.5%	80.5%

Note. The minimum expected count is 88.37. The Pearson Chi-Square test χ² (1) = 251.032, p < .000.

Another factor associated with students’ attitudes toward online learning is internet access. Results in Table 9 revealed that internet access significantly influences students’ attitudes toward online learning, χ² (1) = 34.831, p < .000. Internet accessibility is associated with positive attitudes toward online learning; 51.1% of those who said yes to having Internet access showered positive attitudes. At the same time, only 48.9 of those who said yes to having internet access responded negatively (Table 9).

Table 9.

Cross-Tabulation of Internet Access and Attitude Scale.

			Attitude scale
			Negative	Positive
Do you have internet access?	Yes	Count	703	735
		Expected Count	732.1	705.9
		% within	48.9%	51.1%
	No	Count	82	22
		Expected Count	52.9	51.1
		% within	78.8%	21.2%

Note. The minimum expected count is 51.06. The Pearson Chi-Square test χ² (1) = 34.831, p < .000.

Therefore, the research results show that the significant dependencies are gender, work status, study mode, age and internet. However, factors like previous experience with online learning and year of study were found to be nonsignificant.

Discussion

This study aimed to describe the prevailing attitudes toward online learning among students at the University of Lusaka and to determine some associated factors. Firstly, the study found that students hold strong negative attitudes toward online, which was high among full-time students. In the same vein, the study reported negative attitudes among young students, contrary to the findings of Forsyth et al. (2018). Their study found that young people perceived technology as an instrument both for entertainment and learning. However, one limitation of their research is that it was conducted before the Covid-19 pandemic and leaned more toward distance students. Our findings are interesting because full-time students comprise a young population that tends to be more proactive in using technology. On the other hand, the results showed a positive attitude toward online learning among part-time and distance-learning students. Students in distance learning mode favored online learning, and these findings are consistent with the works of other scholars (Forsyth et al., 2018; Oraif & Elyas, 2021; Zhu et al., 2013).

Furthermore, the research results show that the significant factors associated with students’ attitudes toward online learning are gender, work status, study mode, age and internet access (results in Tables 5 –9, respectively). Specifically, we concluded from the statistical tests that male students hold more positive attitudes toward online learning than female students. This finding is important because gender differences in attitudes toward online learning are generally insignificant. However, some researchers have different results (Yu, 2021). Including conclusions of this research implies that gender differences do exist. The study findings are consistent with other researchers (Cai et al., 2020; Harreiter et al., 2011), who reported significant gender differences in attitudes toward online learning classes. For instance, Cai et al. (2020) found gender differences as a factor influencing students’ attitudes toward online courses in higher education. The meta-analysis showed that male learners hold more favorable attitudes toward online learning than female learners.

Further, we can conclude from the statistical tests in Table 7 show that students in the distance and part-time learning modes favored having classes online. This finding might be due to their employment, which makes most prefer part-time and distance learning modes. Besides, the results show that working students endorsed online learning more positively than non-working students and that their attitudes were significantly associated with work status (Table 6). Working students could have found it easier to learn online during the pandemic because remote learning was not entirely new compared to full-time students. Similar results are reported by Forsythet al. (2018), who found that employed students, part-time and distance students favored online learning. Therefore, we concluded that employed students have a greater need for online education, which would respond to their needs to a greater extent than those of unemployed students.

Further, these findings align with a well-replicated social psychology finding that people’s attitudes increase positively through mere exposure. The more experience a person has with the attitude object, the more favorably they will evaluate it (Zhu et al., 2013). The remote learning experience by distance and part-time students can be said to have increased their exposure to more online learning activities, which may lead to more positive attitudes toward online learning among them. These findings can further be supported by the fact that online learning is considered a critical component of distance learning, aiming to enhance users’ knowledge and improve learning quality (Jogezai et al., 2021). According to Uleanya et al. (2021), online learning is considered one of the different types of distance learning. Thus, part-time and distant students might already have an established favorable attitude toward online classes.

Furthermore, other factors determined are internet access and previous internet usage for online learning. Students with access to the internet hold a stronger positive attitude toward online learning than those without internet access. Besides, these results show that internet access significantly influences students’ attitudes toward online learning. The findings by An and Frick (2006) indicated that the students’ access to the internet could influence their attitudes toward online learning. Similarly, studies conducted during the pandemic, like Olivier (2020), indicated that an online mode of lesson delivery might not favor schools with limited resources whose teachers may not be sufficiently skilled and motivated.

Moreover, Mukuka et al. (2021) advanced that COVID-19 school closure in Zambia and elsewhere could be a wake-up call for education systems to build infrastructure that supports blended and online learning modes (pp. 23–25). Providing ICT products and services is bound to make teaching more accessible remotely and during physical classroom interactions. Furthermore, different studies conducted by Anifowoshe et al. (2020), Kapasia et al. (2020) as well as Owusu-Fordjour et al. (2020) during the outbreak of Covid-19 found that many students had limited access to internet connectivity. This suggests that limited internet connectivity affected their attitudes toward online classes.

Support for internet access in influencing attitudes is replete in literature before and after the pandemic. For instance, Gunnarsson (2001) and Suanpang (2007) reported a significant relationship between having a stronger internet connection and students’ online learning attitudes. However, the research results show that previous internet usage for online learning was not associated with differences in attitudes toward online learning. According to Table 4, students who had previously used the internet for online learning scored similarly to those who had not, with average attitudes scores of 56 and 55. The findings here are inconsistence with some researchers’ findings that students’ prior experiences of internet usage could influence their attitudes toward online learning (Stephens & Creaser, 2002).

Conclusion

The study results allow us to draw meaningful conclusions regarding students’ attitudes toward online learning at the University of Lusaka. It is evident from the results that students have a negative attitude toward online learning, which implies that they are more likely not to accept online classes. Regarding factors that could significantly influence students’ attitudes toward online learning, significant dependencies are gender, age, work status, study mode, and internet access. Generally, the results of this study are not encouraging in terms of the introduction of online learning classes at the University of Lusaka, especially for full-time and younger students. Therefore, the study recommended that the introduction of online learning classes be approached with caution to students in full-time learning mode without neglecting the importance of face-to-face learning. It is further recommended that the university should continue monitoring the attitude of students toward online learning because attitude is a variable that can change anytime.

Limitation

The data obtained in the present study are limited only to the responses received during data collection. In addition, the research findings can be generalized only to the students at the University of Lusaka.

Footnotes

Appendix

Section B.

Scaled Items.

No.	Items	For each statement, put a tick to show your level of agreement or disagreement (Strongly Disagree, Disagree, Agree, and Strongly Agree)
No.	Items	Strongly disagree	Disagree	Neutral	Agree	Strongly Agree
1	I believe learning online is good alternative to face-to-face learning during pandemic such the Covid-19
2	Attending classes online is interesting
3	I prefer reading articles online on the internet to reading hardcopies
4	It is easier to login and attend classes on online learning platforms like Moodle, Google meet, Zoom
5	Online learning requires extensive technical support from lecturers
6	I like receiving online support from the lecturers
7	Online learning reduces quality of knowledge
8	Learning online is often frustrating
0	A face-to-face method is more learner-centered than online learning methods
10	Learning online increases learners’ social isolation during the pandemic such as Covid-19
11	Learning online is tiresome
12	Communicating through social networks is fun
13	Available online learning platforms like Moodle, Google meet, Zoom are difficult to use
14	I make errors frequently when using a computer for online classes
15	Attending online classes or lectures at home is very frustrating
16	Online delivery of courses is a threat to quality of education
17	I find online discussions assignments unexciting
18	I prefer to submit papers (home assignments, course projects, scientific papers and essays, etc.) online instead in face-to-face mode
19	I use social networks for communication with lecturers
20	I prefer physical delivery of classes to online learning

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Compliance with Ethical Standards

The study complied with Ethical Standards.

ORCID iD

Brian Mumba

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

Adnan

Anwar

(2020). Online learning amid the COVID-19 pandemic: Students perspectives. Journal of Pedagogical Sociology and Psychology, 1(2), 45–51.

Ajzen

Fishbein

(1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261.

Ajzen

Fishbein

(2005). The influence of attitudes on behaviour. In Albarracin

Johnson

B. T.

Zanna

M. P.

(Eds.), The handbook handbook of attitudes attitudes (pp. 173–221). Erlbaum.

Albarracin

Shavitt

(2018). Attitudes and attitude change. Annual Review of Psychology, 69, 299–327. https://doi.org/10.1146/annurev-psych-122216-011911

Alothman

Robertson

Michaelson

(2017). Computer usage and attitudes among Saudi Arabian undergraduate students. Computers & Education, 110, 127–142. https://doi.org/10.1016/j.compedu.2017.02.010

Anifowoshe

Aborode

A. T.

Ayodele

T. I.

Iretiayo

A. R.

David

O. O.

(2020). Impact of COVID-19 on education in Sub-Saharan Africa. https://doi.org/10.20944/preprints202007.0027.v1

Anwar

I. W.

Wahid

J. H.

(2021). Learners’ perception on online learning implementation during Covid-19 pandemic. Journal of Languages and Language Teaching, 9(2), 126–138.

Y. J.

Frick

(2006). Student perceptions of asynchronous computer-mediated communication in face-to-face courses. Journal of Computer-Mediated Communication, 11, 485–499. https://doi.org/10.1111/j.1083-6101.2006.00023.x

Bernard

R. M.

Brauer

Abrami

P. C.

Surkes

(2004). The development of a questionnaire for predicting online learning achievement. Distance Education, 25(1), 31–47.

10.

Brown

C. G.

(2012). A systematic review of the relationship between self-efficacy and burnout in teachers. Educational and Child Psychology, 29, 47–63.

11.

Cai

Chen

Jiang

, et al. (2020). Psychological impact and coping strategies of frontline medical staff in Hunan between January and March 2020 during the outbreak of coronavirus disease 2019 (COVID19) in Hubei, China. Medical Science Monitor, 26, e924171. https://doi.org/10.12659/MSM.924171

12.

Chola

Kasimba

George

Rajan

(2020). COVID-19 and E-learning: Perception of freshmen level physics students at Lusaka Apex Medical University. International Journal of Academic Research and Development, 5(4), 67–76.

13.

Felix

(2001). A multivariate analysis of students’ experience of web based learning. Australasian Journal of Educational Technology, 17(1), 21–36.

14.

Field

(2013). Discovering statistics using IBM_SPSS. Sage Publications Ltd.

15.

Forsyth

R. P.

Yovkoca

Aleksieva

(2018). Factors affecting students’ attitudes towards online learning – the case of Sofia University. In AIP Conference Proceeding 2048. AIP Publishing LLC.

16.

Gachanja

Mwangi

Gicheru

(2021). E-learning in medical education during COVID-19 pandemic: Experiences of a research course at Kenya Medical Training College. BMC Medical Education, 21(1), 612. https://doi.org/10.1186/s12909-021-03050-7

17.

Gilbert

Morton

Rowley

(2007). E-Learning: The student experience. British Journal of Educational Technology, 38(4), 560–573.

18.

Gunnarsson

C. L.

(2001). Development and assessment of students: Attitudes and achievement in a business statistics course taught online. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 3(2), 13–14.

19.

Harreiter

Wiener

Plass

Kautzky-Willer

(2011). Perspectives on gender-specific medicine, course and learning style preferences in medical education: A study among students at the medical university of Vienna. Wiener Medizinische Wochenschrift, 161(5-6), 149–154. https://doi.org/10.1007/s10354-011-0866-x

20.

Himoonga

Phiri

(2020). Increasing the use of E-Learning platforms in tertiary learning institutions for blended distance programmes in Zambia. Open Journal of Social Sciences, 08, 174–190.

21.

Holley

Oliver

(2010). Student engagement and blended learning: Portraits of risk. Computers & Education, 54(3), 693–700.

22.

Ismaili

(2021). Evaluation of students’ attitude toward distance learning during the pandemic (Covid-19): A case study of ELTE university. Horizon: A Magazine of the Arts, 29(1), 17–30.

23.

Jogezai

N. A.

Baloch

F. A.

Jaffar

Shah

Khilji

G. K.

Bashir

(2021). Teachers’ attitudes towards social media (SM) use in online learning amid the COVID-19 pandemic: The effects of SM use by teachers and religious scholars during physical distancing. Heliyon, 7(4), 1–9.

24.

Johnson

R. F.

(2013). Student attitudes toward blended and online courses: A comparison of students in traditional classroom writing environments and students in blended writing environments. A Dissertation Submitted to the Faculty of the University of Tennessee at Chattanooga in Partial Fulfilment of the Requirements of the Degree of Doctor of Education.

25.

Kapasia

Paul

Roy

Saha

Zaveri

Mallick

Chouhan

(2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Children and Youth Services Review, 116, 105194.

26.

Kisanga

D. H.

Ireson

(2016). Test of e-Learning Related Attitudes (TeLRA) scale: Development, reliability and validity study. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 12(1), 20–36.

27.

Lee

S. J.

Srinivasan

Trail

Lewis

Lopez

(2011). Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning. Internet and Higher Education, 14, 158–163.

28.

Knowles

Kerkman

(2007). An investigation of students attitude and motivation toward online learning. Student Motivation, 2, 70–80.

29.

Lengkanawati

N. S.

Wirza

Alicia

(2021). EFL learners’ view on online learning implementation during Covid-19 outbreaks. In 4th Sriwijaya University Learning and Education International Conference (SULE-IC), 2020 (pp. 351–357). Atlantis Press.

30.

Lufungulo

E. S.

Mwila

Mudenda

Kampamba

Chulu

Hikaambo

C. N.

(2021). Online teaching during COVID-19 pandemic in Zambian universities: Unpacking lecturers’ experiences and the implications for incorporating online teaching in the university pedagogy. Creative Education, 12(12), 2886–2904. https://doi.org/10.4236/ce.2021.1212216

31.

Maio

G. R.

Haddock

(2009). The psychology of attitudes and attitude change. SAGE.

32.

Melvina Lengkanawati

N. S.

Wirza

Alicia

Yulmiati . (2021). EFL learners’ view on online learning implementation during Covid-19 outbreaks. Proceedings of the 4th Sriwijaya University Learning and Education International Conference (SULE-IC 2020) (pp. 351–357). Atlantis Press. https://doi.org/10.2991/assehr.k.201230.130

33.

Mukuka

Shumba

Mulenga

H. M.

(2021). Students’ experiences with remote learning during the COVID-19 school closure: Implications for mathematics education. Heliyon, 7(7), e07523. https://doi.org/10.1016/j.heliyon.2021.e07523

34.

Nyashanu

Mtambo

Karonga

Walker

(2023). Exploring the impact of covid-19 lockdown on learning among higher education students on the copperbelt province, Zambia. Cogent Education, 10(1), 2200631. https://doi.org/10.1080/2331186x.2023.2200631

35.

Olivier

(2020). Needed: Customised blended learning. The Cape Times.

36.

Oraif

Elyas

(2021). The impact of COVID-19 on Learning: Investigating EFL Learners’ engagement in online courses in Saudi Arabia. Education Sciences, 11(3), 99–19.

37.

Owusu-Fordjour

Koomson

C. K.

Hanson

(2020). The impact of Covid-19 on learning—The perspective of the Ghanaian student. European Journal of Education Studies, 7, 88–101.

38.

Rogers-Estable

(2014). Web 2.0 use in higher education. European Journal of Open, Distance and eLearning, 17(2), 129–141.

39.

Stephens

Creaser

(2002). Information science student IT experience and attitude toward computers: Results of a five-year longitudinal study. Innovation in Teaching and Learning in Information and Computer Sciences, 1(2), 1–21.

40.

Suanpang

(2007). Students experience online learning in Thailand. In Hongladarom

(Ed.), Computing and philosophy in Asia (pp. 9–10). Cambridge Scholars Publishing.

41.

Tanjung

F. Z.

Utomo

(2021). Investigating EFL students’ perception on online learning amidst Covid-19 pandemic. International Journal of Indonesian Education and Teaching, 5, 102–115.

42.

Thurstone

L. L.

(1931). The measurement of social attitudes. Journal of Abnormal and Social Psychology, 26(3), 249–269.

43.

Uleanya

(2022). Hidden curriculum versus transition from onsite to online: A review following COVID-19 pandemic outbreak. Cogent Education, 9(1), 1–10. https://doi.org/10.1080/2331186X.2022.2090102

44.

Uleanya

Ezeji

N. I.

Uleanya

M. O.

(2021). Inclusive education in the face of a global pandemic: Providing support. Multicultural Education, 7(5), 139–146. https://doi.org/10.5281/zenodo.4750496

45.

Uleanya

Gamede

(2019). Technology solution to quality rural university education. The International Journal of Interdisciplinary Educational Studies, 14(2), 59–74. https://doi.org/10.18848/2327-011X/CGP/v14i02/59-74

46.

UNESCO. (2020). 1.37 billion Students now home as COVID-19 school closures expand, ministers’ scale-up multimedia approaches to ensure learning continuity – UNESCO IITE. Retrieved October 28, 2021, from https://iite.unesco.org/news/1-37-billion-students-now-home-as-covid-19-school-closures-expand/

47.

Veletsianos

Houlden

(2019). An analysis of flexible learning and flexibility over the last 40 years of distance education. Distance Education, 40(4), 454–468.

48.

Wood

(2000). Attitude change: Persuasion and social influence. Annual Review of Psychology, 51, 539–570. https://doi.org/10.1146/annurev.psych.51.1.539

49.

(2021). A meta-analysis of gender differences in e-learning outcomes. Research Square. https://doi.org/10.21203/rs.3.rs-493822/v1

50.

Yudko

Hirokawa

Chi

(2008). Attitudes, beliefs, and attendance in a hybrid course. Computers & Education, 50(4), 1217–1227.

51.

Zambia Information and Communications Technology Authority. (2020). 2019 Annual report. Retrieved November 10, 2021, from https://www.zicta.zm/media/publications

52.

Zhu

W. K.

Yates

G. C.

(2013). University students’ attitudes toward online learning in a blended course. In White

(Ed.), AARE conference papers. https://www.aare.edu.au/data/publications/2013/Zhu13.pdf

Examining Attitudes Toward Online Learning Classes Amidst Covid-19 Pandemic Among Students at the University of Lusaka

Abstract

Keywords

Introduction

The eLearning in Zambia

Theoretical Framework

Online Learning

Students’ Attitude to Online Learning

Factors Associated With Students’ Attitudes Toward Online Learning

Research Questions

Methods

Measures

Data Collection Instrument

Reliability of the Instrument

Participants

Data Collection

Ethical Consideration

Data Analysis

Results

Prevailing Students’ Attitudes Toward Online Learning

Factors Associated With Students’ Attitudes

Discussion

Conclusion

Limitation

Footnotes

Appendix

Declaration of Conflicting Interests

Funding

Compliance with Ethical Standards

ORCID iD

Data Availability Statement

References