Students’ Academic Performance and Perceptions towards Online Learning during the COVID-19 Pandemic at a Large Public University in Northern Cyprus

Abstract

The COVID-19 pandemic has disrupted education systems in educational environments, especially in universities. In some educational institutions, including Eastern Mediterranean University, the decision was made to replace face-to-face learning with online learning to ensure the health of students and instructors. It is necessary to find out how a transition would impact the education quality and what the feedback will be among students. This study examines the level of satisfaction with the current online learning platforms, students’ preference between face-to-face and online learning, and the students’ continuous intention to use online learning. Students’ academic performance during the two years of the COVID-19 pandemic era and the two years before the COVID-19 pandemic era are compared together in this research to examine the change in their academic performance outcomes. To collect data, a cross-sectional study was conducted. A total of 1087 participants fully responded to the online survey. The findings of this study provide strong support for online learning against face-to-face learning. The evaluation of students’ academic performance showed a very slight drop, which showed that the level of satisfaction of students from online learning might not be linked to their academic achievements. The results of this study can help educational environments to improve the situation of online education, and policymakers will have a good view of students’ acceptance and satisfaction with online learning.

1. Introduction

The acute respiratory disease, which emerged as the Coronavirus (COVID-19) in Wuhan, China, in December 2019, has spread rapidly around the world. Due to this, the World Health Organization (WHO) declared the situation a pandemic on 11 March 2020. After the WHO’s declaration of the pandemic, life began to change in many countries. The COVID-19 pandemic has affected numerous aspects of life and impacted the delivery of knowledge and skills at education levels [1]. The COVID-19 pandemic had a big and immediate effect on higher education, forcing universities to shift from traditional face-to-face to online learning [2]. Eastern Mediterranean University also decided to replace online learning with face-to-face education in March 2020 temporarily. This decision was faced with challenges that the examination of the student’s perceptions towards these changes will present the university to improve the quality of education and prepare for possible similar situations in the future.

Britannica defines online learning as a “form of education in which the main elements include physical separation of teachers and students during instruction and the use of various technologies to facilitate student-teacher and student-student communication” [3]. Online learning has been advantageous to its users, like making communication easier between instructors and students [4]. Other advantages of online learning include feasibility, flexibility, and accessibility can help students to continue their learning in any situation [5]. On the other hand, accessing the technologies is stated as a challenge for online learning. Lack of social interaction among students and poor learning outcomes can be considered as other challenges for online learning [6]. It should also be borne in mind that students in some fields of study which require face-to-face learning will lack some skills when entering their profession [1]. Various studies predict that online learning will be as effective as traditional face-to-face learning [7,8]. Examining students’ academic performances and their level of satisfaction with online learning can inform the universities about the challenges facing online learning and take steps to solve them. Universities must ensure high levels of student satisfaction to improve the quality of education and attract students to participate in courses. Several studies show the relationship between the quality of online education and the level of satisfaction [9]. The level of satisfaction with online education, which is measured by criteria such as users’ perception of usability, the quality of the system or platform used, and the expected academic achievements, is the most important parameter in the success of online education [10].

2. Literature Review

Since the beginning of the COVID-19 pandemic, many educational institutions have been forced to temporarily replace traditional education with online education [11]. However, there were significant challenges. Some students had problems with technology use, mental health, time management, and balancing personal and academic life [12]. In some cases, motivation, self-efficacy, or cognitive engagement were reduced, and technology use increased [13]. Research has also been conducted on the negative and positive aspects of online education, which has considered the decrease in concentration [12], loss of motivation [14,15,16], social isolation [15,17,18], and decrease in the interaction between teachers and students [14,17] as negative effects. In contrast, flexibility in time and space [15,17,18,19,20], ease of access to resources and materials [17,18,21], and learning at their own pace [17] are considered positive aspects of online education.

Examining the level of satisfaction of students and their continuous intention to use online learning can help to improve efficiency and eliminate its shortcomings. Finding students’ preferences between two learning methods can also measure the acceptance of the current learning method. The satisfaction level is related to the responses of users about their feeling, preferences, and perceptions when using a system which is usually obtained by using questionnaires [22]. The continuance of intention to use is defined as one’s intention to continually use or reuse a system [23].

In recent years, a lot of research has been done on the level of satisfaction and acceptance of online education among students, and the number of these studies has been increasing after the COVID-19 pandemic era. In most studies, students had a positive attitude and sufficient satisfaction with online education and its effects [14,21,24,25,26,27,28,29,30,31,32,33]. In [34], a high level of online self-efficacy, generalized moderate anxiety, and little fear of COVID-19 caused a high level of satisfaction was shown. Stimulation and attractiveness can also be mentioned as influential factors in the level of satisfaction with online learning [35].

Some of the most important factors in the effectiveness of online learning are students’ characteristics, such as demographic characteristics and digital knowledge and skills [36]. Despite most of the results, some studies show that students prefer the blended system [17,37,38,39]. In some other research, students’ preferences for face-to-face learning were found to be higher than for online learning [19,40,41], and findings of [42] also point to the dissatisfaction of students with online education. The research done in [43] also shows that the perceived disadvantages of online learning are more than the advantages. Regarding the continuous intention to use the online learning method, different research results are observed. The results of some research largely explained continuance intention [23,44]. In contrast, the result of [45] showed that the majority of students did not want to continue learning by online method. Interaction between students and instructors and service quality can affect continuance intentions for online learning [46]. Also, teaching quality and time management benefits can influence students’ intention to use online learning in the future [47]. To our knowledge, no previous research has examined the relationship between the level of satisfaction and continuous intention.

According to some research, the level of satisfaction with online learning can affect their academic performance [48,49,50]. Findings of studies regarding students’ academic achievements in an online setting are not so uniform. Namely, some studies have shown that students perform better with online learning in their academic achievements [48,49,51,52,53,54,55,56], while other studies have shown that online learning can cause a drop in academic performance [57,58]. Yet again, the results of [59] showed that student performance remained similar in face-to-face learning compared to online learning. Findings of [41] conclude that there is a weak relationship between student academic achievements and perceptions.

The first case of COVID-19 disease in Turkey was announced on 10 March 2020 by the Ministry of Health of Turkey. Higher Education Board Members and university rectors made continuous and direct connections under the Presidency of the Council of Higher Education (CoHE) to initiate a transition to online education [60]. Subsequently, Eastern Mediterranean University made a sudden shift in delivering teaching-learning strategies by temporarily transitioning to online learning to meet its educational goals. To support this shift, many web-based content delivery systems (e.g., Moodle, Microsoft Teams) were used.

To our knowledge, no prior large-scale studies have examined the level of students’ satisfaction and academic performance with online learning during the COVID-19 pandemic in Northern Cyprus, and the results of this research can be effective in the future decisions of educational institutions. One of the main objectives of this research is to find out students’ attitudes toward and perceptions of online learning. The academic performance of students during the two years of the COVID-19 pandemic period with the online learning method and the two years before the COVID-19 pandemic period with the traditional learning method are compared together in this research. The current study tried to answer the following research questions:

• What is the level of satisfaction of Eastern Mediterranean University students with online learning during the COVID-19 pandemic?
• Are there significant differences regarding online learning satisfaction according to participants’ IT skill level and prior online learning experiences?
• What has been the impact of the redirection to online education on the academic performance of students during the COVID-19 lockdown period?

3. Methodology

3.1. Data Collection

The study was conducted from January to February 2022. A cross-sectional descriptive design was utilized to achieve research goals [61]. The data collected in this research consists of students’ attitudes toward online learning and their academic performance information.

3.1.1. Students’ Perceptions toward Online Learning

Data were collected from the participants using questionnaires. The data collection process was done through an internet-based questionnaire administrated by the Information Technologies Directorate at Eastern Mediterranean University, and the link to the online survey was shared amongst all the registered students by the official bulk email system of the university. Participants were also informed that this questionnaire was voluntary and anonymous before answering questions in the survey. To collect data, one of the most familiar methods, the 5-point Likert scale employed. This scale of measuring satisfaction had 5 answer options, including strongly agree, agree, neutral, disagree, and strongly disagree. These options allowed us to determine the level of satisfaction with the online learning system.

3.1.2. Students’ Academic Performances

In order to better and more accurately examine the effectiveness of online learning on students, detailed information on their academic performance was obtained. Students’ administrative academic records were obtained from the academic and student affairs of Eastern Mediterranean University without referring to the student’s information to maintain anonymity. This data includes differences between students’ overall grade point average (GPA) in 2 years of face-to-face education before the Covid-19 pandemic, including four academic semesters between 2018 and 2020, and 2 years of online learning during the COVID-19 pandemic between 2020 and 2022.

3.2. Sample Size

The study population for the survey consisted of 1580 respondents, 377 did not respond, and 116 did not respond fully. A total of 1087 graduate and undergraduate students from different fields of study fully responded to the survey. The data obtained for the academic performance of the students includes the information of all undergraduate, master, and doctoral students of the Eastern Mediterranean University during the research period. All participating students were from the same university.

3.3. Instrument

The survey tool employed in this paper included five items of sociodemographic characteristics of participants, with a further 25 items split across three sections, namely online learning satisfaction (OS), students’ preference between face-to-face and online education (SP), and continuance intention to use online learning (CI). The sociodemographic characteristics of participants included the age group of the participants, the level of education, faculty or school where they are studying, IT skill level, and prior online learning experience. The OS section was made up of ten items (see

Table 1. Students’ online learning satisfaction.

Code	Statement
OS1	It was accessible from all devices
OS2	It was flexible in time and space
OS3	The Materials were presented well
OS4	Learning contents were updated
OS5	It was possible to co-working with other students
OS6	Learning resources were accessible
OS7	There was good interaction between students and instructors
OS8	The assessment submission procedure was simple and reliable
OS9	Assessments were effective
OS10	The system prepared useful and accurate educational reports

), the SP section contained ten items (see

Table 2. Students’ preference between face-to-face and online education.

Code	Statement
SP1	Online learning is more flexible in time
SP2	Online learning is more flexible in space
SP3	Online learning resources are more accessible
SP4	Online learning has better-structured classes
SP5	Teacher-Student interaction is better in online learning
SP6	The delivery of content is better in online learning
SP7	Online learning tools provide better perception
SP8	Assessments are better in online learning
SP9	Online learning tools provide reports with more detailed and comprehensive
SP10	Online learning increase motivation and passion for learning

), and the CI section was made up of 5 items (see

Table 3. Continuance intention to use online learning.

Code	Statement
CI1	I intend to continue using online learning as a learning content delivery method
CI2	I intend to continue using online learning for assessment
CI3	I intend to continue using online learning as an interactive communication tool between students and instructors
CI4	I intend to continue using online learning as a resource-sharing space
CI5	I intend to continue using online learning in blended mode to assist my face-to-face learning

). Statements in the sections designed by the researcher are based on [46,62,63]. Three experts in educational technology and socio-behavioral sciences contributed to the thorough selection and evaluation of research statements to guarantee clarity, conciseness, and precision. There are three different scales used in this study to measure OS, SP, and CI in the questionnaire. Principal component analysis (PCA) was applied to all three scales separately in line with the data obtained from the current study. As stated in [64], we adopted a factor loading criterion of 0.40 in the analysis. According to the results obtained, the factor loads of 10 items in the OS scale range between 0.88 and 0.70. The OS scale is single-factor with an appropriate variance explained value of 65.2%. Factor loadings for the ten items in the SP scale were found to be between 0.90 and 0.68 and the value of variance explained for the single-factor scale was calculated as 71.1%. The factor loads of 5 items in the Cl scale are between 0.91 and 0.47. The explained variance value of the single-factor CI scale was found to be 68.9%.

3.4. Data Analysis

To validate and analyze the data collected from the participants, several statistical analysis techniques have been used in this study by the researcher. The collected data were processed and classified using the Statistical Package for the Social Sciences (SPSS) Version 26 and Microsoft Excel. Cronbach’s alpha technique was used to assess the reliability and internal consistency of the questionnaire sections. The data collected from the questionnaires were analyzed and presented using descriptive analysis. Skewness and kurtosis were used to determine whether the data were normally distributed in terms of the independent variables to which the analysis of the dependent variables we discussed in the study would be carried out [65]. All groups analyzed in the study were found to be normally distributed according to all dependent variables. MANOVA was used to find any differences between independent groups on dependent variables, and one-way ANOVA was used to find out statistical evidence that there is a significant difference between the means of independent groups. Also, we used the post hoc test in order to find out exactly which groups are different from each other.

4. Results

4.1. Sociodemographic Characteristics

This section provides some basic demographic data about the participants.

Table 4. Sociodemographic Characteristics of Sample Respondents.

Variable	Labels	Frequency Distribution	Percentage
Age Group	18 years and below	53	4.9%
	19–29	994	91.4%
	30–39	33	3.0%
	40 years and above	7	0.7%
Faculty/School	Architecture	34	3.1%
	Art and Science	164	15.1%
	Business and Economics	88	8.1%
	Communication and Media Studies	39	3.6%
	Health Sciences (Medicine, Dentistry, Health Services, Pharmacy)	195	18%
	Education	106	9.7%
	Engineering	243	22.4%
	Law	133	12.2%
	Tourism	31	2.8%
	Computing and Technology	54	5.0%
Level of Education	Undergraduate	1010	92.9%
	Master	27	2.5%
	PhD	50	4.6%
IT Skill Level	High	478	44.0%
	Moderate	65	6.0%
	Low	544	50.0%
Prior Online Learning Experience	Yes	501	46.1%
	No	586	53.9%

shows the responses of participants to the questions related to age group, faculty or school, level of education, IT skill level, and prior online learning experience.

According to Table 4, the majority of participants were in the 19–29 age group. 4.9% of participants are 18 years and below, 91.4% belong to the age group of 19–29 years, 3% are in the age group of 30–39 years, and 0.7% are in the age group of 40 years. As revealed in Table 4, participants from different faculties and schools participated in this online survey. Most of the participants are from the faculty of engineering (22.4%), faculty of art and science (15.1%), health sciences (18%), and faculty of law (12.2%). The level of education of the majority of students who participated in this study is undergraduate (92.9%). 4.6% of participants are studying for Ph.D., and 2.5% are studying for a master’s at university. Most participants in this study either had high IT skill levels (44%) or low levels of IT skills (50%). Also, 6% of them mentioned that they have a moderate level of IT skills. 46.1% of participants have already experienced online education, and 53.9% have experienced this type of education for the first time.

4.2. Reliability

Reliability refers to the consistency of measures on a specific instrument and should calculate before using an instrument [66]. The most widely used test to determine the internal consistency of an instrument is Cronbach’s alpha [67]. This study used Cronbach’s alpha method to test the reliability of each section. As shown in

Table 5. Reliability of Constructs.

Latent Variables	No. of Items	Cronbach’s Alpha
Student’s online learning satisfaction	10	0.940
Students’ preference between face-to-face and online education	10	0.954
Continuance intention to use online learning	5	0.872

, the Cronbach Alpha coefficients for each section of the questionnaire ranged from 0.872 to 0.954. From the values of Cronbach’s alpha, it can be concluded that the validity of this research for each section is very reliable. Two of them are classified as excellent (0.940 > 0.9, 0.9554 > 0.9), and one of them is in good classification (0.8 < 0.872 < 0.9) [68].

4.3. Student’s Online Learning Satisfaction

As presented in

Table 6. Students’ online learning satisfaction.

Code	SD (1)		D (2)		N (3)		A (4)		SA (5)
OS1	49	4.5%	51	4.7%	80	7.4%	477	43.9%	430	39.6%
OS2	71	6.5%	107	9.8%	179	16.5%	379	34.9%	351	32.3%
OS3	74	6.8%	98	9.0%	157	14.4%	362	33.3%	396	36.4%
OS4	57	5.2%	74	6.8%	121	11.1%	399	36.7%	436	40.1%
OS5	126	11.6%	141	13.0%	161	14.8%	348	32.0%	311	28.6%
OS6	65	6.0%	62	5.7%	99	9.1%	378	34.8%	483	44.4%
OS7	127	11.7%	113	10.4%	141	13.0%	343	31.6%	363	33.4%
OS8	133	12.2%	109	10.0%	117	10.8%	341	31.4%	387	35.6%
OS9	130	12.0%	98	9.0%	127	11.7%	354	32.6%	378	34.8%
OS10	94	8.6%	79	7.3%	176	16.2%	339	31.2%	399	36.7%

, most of the students agreed that online learning tools used in the university (Microsoft Teams and Moodle) were accessible from all devices (83.4%), and they also agreed regarding the flexibility of online learning on time and space (67.2%). Participants confirmed that materials were presented well (69.7%), learning resources were accessible (79.2%), and learning contents were updated (76.8%). More than half of the participants agreed that there was a possibility of co-working with other students (60.6%), and there was good interaction between students and instructors (64.9%). Regarding assessments, most of the students feel that assessments in online learning were effective (67.3%) and the submission procedure was easy and reliable (67%). From the students’ point of view, educational reports prepared by the online system were useful and accurate (67.9%). The highest satisfaction is related to the accessibility of online learning, and the lowest is related to co-working ability in online learning. In general, students who participated in this study held a high level of satisfaction with online learning (M = 3.81). The descriptive statistics of students’ online learning satisfaction are revealed in

Table 7. Descriptive statistics of students’ online learning satisfaction.

Code	Mean	Std. Error of Mean	Std. Deviation
OS1	4.093	0.031	1.027
OS2	3.765	0.036	1.190
OS3	3.835	0.037	1.207
OS4	3.996	0.034	1.122
OS5	3.531	0.040	1.333
OS6	4.060	0.035	1.140
OS7	3.646	0.041	1.344
OS8	3.681	0.041	1.366
OS9	3.692	0.041	1.345
OS10	3.800	0.038	1.249

.

4.4. Students’ Preference between Face-to-Face and Online Education

Table 8. Students’ preference between face-to-face and online education.

Code	SD (1)		D (2)		N (3)		A (4)		SA (5)
SP1	70	6.4%	96	8.8%	85	7.8%	375	34.5%	461	42.4%
SP2	49	4.5%	45	4.1%	82	7.5%	408	37.5%	503	46.3%
SP3	58	5.3%	66	6.1%	107	9.8%	356	32.8%	500	46.0%
SP4	125	11.5%	115	10.6%	164	15.1%	275	25.3%	408	37.5%
SP5	182	16.7%	143	13.2%	182	16.7%	247	22.7%	333	30.6%
SP6	136	12.5%	91	8.4%	149	13.7%	306	28.2%	405	37.3%
SP7	129	11.9%	107	9.8%	152	14.0%	306	28.2%	393	36.2%
SP8	151	13.9%	93	8.6%	122	11.2%	325	29.9%	396	36.4%
SP9	106	9.8%	77	7.1%	162	14.9%	327	30.1%	415	38.2%
SP10	202	18.6%	92	8.5%	140	12.9%	285	26.2%	368	33.9%

shows the students’ preferences for online and face-to-face learning. The majority of students agree that online learning is more flexible in time (76.9%) and space (83.8%). In addition, they agree that online learning resources are better in accessibility (78.7%). And also, most of the participants think that online learning has better-structured classes (62.8%). Teacher-Student interaction has the lowest degree of superiority of online learning over face-to-face learning, with just 53.4% of the students agreeing. 64.3% of students feel that online learning tools provide better perception and 65.4% of the participants believe that delivery of content is better in online learning. Most of the students also prefer online learning over face-to-face learning in assessments (66.3%), and they believe that online learning has more detailed (68.3%) and comprehensive report tools and increases motivation and passion for learning more than face-to-face learning (60.1%). The participants are more interested in online learning than face-to-face and agreed regarding their preference for online learning, considering the overall mean value shown in

Table 9. Descriptive statistics of students’ preference between face-to-face and online education.

Code	Mean	Std. Error of Mean	Std. Deviation
SP1	3.976	0.036	1.198
SP2	4.169	0.032	1.040
SP3	4.080	0.034	1.130
SP4	3.668	0.042	1.370
SP5	3.374	0.044	1.455
SP6	3.693	0.042	1.370
SP7	3.669	0.041	1.362
SP8	3.664	0.042	1.399
SP9	3.799	0.039	1.286
SP10	3.483	0.045	1.488

(M = 3.76).

4.5. Continuance Intention to Use Online Learning

The study intends to examine the continuous intention of students to use online learning tools. The findings listed in

Table 10. Continuance intention to use online learning.

Code	SD (1)		D (2)		N (3)		A (4)		SA (5)
CI1	121	11.1%	73	6.7%	93	8.6%	314	28.9%	486	44.7%
CI2	126	11.6%	71	6.5%	100	9.2%	320	29.4%	470	43.2%
CI3	105	9.7%	70	6.4%	110	10.1%	347	31.9%	455	41.9%
CI4	75	6.9%	29	2.7%	87	8.0%	386	35.5%	510	46.9%
CI5	118	10.9%	99	9.1%	148	13.6%	337	31.0%	385	35.4%

show that most of the participants agree to continue using online learning as a learning content delivery method (73.6%), assessment tool (72.7%), and interactive communication tool between students and instructors (73.8%). The majority of students intend to use online learning as a resource-sharing space (82.4%). And at the same time, more than half of the participants believe that they intend to continue using online learning in blended mode to assist with face-to-face learning (66.4%). Overall, they have a strong intention to continue using online learning, as figured out in

Table 11. Descriptive statistics of continuance intention to use online learning.

Code	Mean	Std. Error of Mean	Std. Deviation
CI1	3.893	0.041	1.339
CI2	3.862	0.041	1.346
CI3	3.899	0.039	1.280
CI4	4.129	0.034	1.123
CI5	3.710	0.040	1.323

(M = 3.90).

4.6. MANOVA

According to the MANOVA analyses performed, there was no significant difference between the age groups’ independent variables and dependent variables (λ = 0.996, F = 0.787, p = 0.580). Education level also showed no significant difference with dependent variables (λ = 0. 989, F = 1.880, p = 0.081). Results revealed that students’ IT skill levels resulted in significant differences in the dependent variables (λ = 0.932, F = 12.503, p < 0.001). The other independent variable that showed significant differences from the dependent variables is the Prior Online Learning Experience (λ = 0.983, F = 6.193, p < 0.001).

4.7. One-Way ANOVA

Based on the ANOVA analyses performed, the results regarding the significance of the effect of IT skill level and prior online learning experience independent variables on OS, SP, and CI dependent variables are presented in

Table 12. Univariate Tests.

Independent Variable	Dependent Variable	Sum of Squares	df	Mean Square	F	p
Age Group	Total-OS	1.0958	2	0.5479	0.5857	0.557
	Total-SP	0.2040	2	0.1020	0.0886	0.915
	Total-CI	1.2387	2	0.6193	0.5982	0.550
Level of Education	Total-OS	2.8595	2	1.4298	1.5283	0.217
	Total-SP	5.1988	2	2.5994	2.2575	0.105
	Total-CI	6.4277	2	3.2138	3.1041	0.045
IT Skill Level	Total-OS	62.2891	2	31.1445	33.2916	<0.001
	Total-SP	74.9218	2	37.4609	32.5337	<0.001
	Total-CI	52.7297	2	26.3649	25.4649	<0.001
Prior Online Learning Experience	Total-OS	13.5487	1	13.5487	14.4827	<0.001
	Total-SP	20.9846	1	20.9846	18.2245	<0.001
	Total-CI	12.5160	1	12.5160	12.0887	<0.001

.

As shown in Table 12, among the dependent variables considered in terms of the IT skill level independent variable, OS shows a significant difference (F = 62.28, p < 0.001). The analysis also revealed a significant difference between IT skills and SP with F = 74.92 and p < 0.001 and CI with F = 52.73 and p < 0.001. In other words, in the IT skill level, students with higher or lower levels of IT skills are not likely to be equal on the dependent variables.

Similarly, an ANOVA analysis was conducted to test the difference between students’ prior online learning experiences. It revealed significant differences between students with prior online learning experiences and students without prior online learning experiences for OS with F = 13.55 and p < 0.001, SP with F = 20.98 and p < 0.001, and CI with F = 12.52 and p < 0.001. In other words, all univariate ANOVAs were significant.

4.8. Level of IT Skill Post Hoc Tests

Tukey HSD, one of the Post-Hoc tests, was applied to determine the source of the difference in the IT skill level independent variable. As can be seen from

Table 13. Post Hoc Comparisons—IT Skill Level.

	Comparison
Dependent Variable	IT Skill Level	IT Skill Level	Mean Difference	SE	df	t	p
Total OS	High	Moderate	0.192	0.0607	1084	3.16	0.005
	High	Low	1.027	0.1279	1084	8.03	<0.001
	Low	Moderate	−0.836	0.1270	1084	−6.58	<0.001
Total SP	High	Moderate	0.245	0.0676	1084	3.62	<0.001
	High	Low	1.108	0.1425	1084	7.77	<0.001
	Low	Moderate	−0.863	0.1415	1084	−6.10	<0.001
Total CI	High	Moderate	0.144	0.0641	1084	2.25	0.074
	High	Low	0.951	0.1353	1084	7.03	<0.001
	Low	Moderate	−0.807	0.1343	1084	−6.01	<0.001

, the OS scores of students with high IT skill levels were found to be significantly higher on average than those with low IT skill levels, and students with low IT skill levels showed significantly lower averages than students with a moderate level of IT skill.

According to the results shown in Table 13, students with a high level of IT skills had a higher average SP score than students with a moderate and low level of IT skills. In addition, students with a low level of IT skills had a lower average SP score than those with a moderate level of IT skills.

The CI scores of the students with a high level of IT skills also were found to be averagely higher than those with a moderate level of IT skill level, according to Table 13. Also, students with a low level of IT skills had a lower average CI score than students with a moderate level of IT skills.

4.9. Prior Online Learning Experience Post Hoc Tests

Regarding the main effect of the prior online learning experience, a significant effect was found. For the OS score for the prior online learning experience, statistically significant differences were not found, as shown in

Table 14. Post Hoc Comparisons—Prior Online Learning Experience.

	Comparison
Dependent Variable	Prior Experience	Prior Experience	Mean Difference	SE	df	t	p
OS	Yes	No	0.180	0.101	1081	1.78	0.075
SP	Yes	No	0.359	0.0666	1085	5.39	<0.001
CI	Yes	No	0.273	0.0631	1085	4.33	<0.001

.

We note statistically significant differences between students with experience and without experience in online learning. Students with online learning experience had significantly higher average SP scores than other students (Table 14).

The results also show a significantly higher average CI score for students with online learning experience than students without online learning experience (Table 14).

4.10. Students’ Academic Performances

According to

Table 15. Students’ Academic Performance Changes.

Level of Study	GPA Changes
Doctorate	Decreased by 3.31%
Master	Decreased by 6.38%
Undergraduate	Decreased by 0.24%
Grand Total	Decreased by 3.64%

, changes in the GPA of undergraduate, master, and doctoral students can be seen before and during the COVID-19 pandemic. The GPA of undergraduate students was almost unchanged, with a decrease of 0.24%. The biggest change in GPA is for master students, who had a sharp drop in their academic performance, with a 6.38% decrease. Doctoral students have also faced a 3% drop in GPA.

Academic results of students in the whole university have faced a slight drop. The GPA of students during the two years of the COVID-19 pandemic, when the education was online, faced a drop of 3.64% compared to the 2-year face-to-face period before the COVID-19 pandemic.

5. Discussion

This research tried to find the perceptions of Eastern Mediterranean University students regarding online learning. The overall mean score obtained in each section of this study indicates that students are highly satisfied with online learning and prefer to continue using it. Many aspects of online learning have the highest level of satisfaction, such as the possibility to access from all devices, up-to-date learning content, and learning resource accessibility. According to information obtained from the university, 52% of students use personal computers and 48% of mobile phones and tablets to access online learning platforms. Despite this variety in the devices used to access online learning platforms, students had the highest satisfaction with accessibility.

5.1. Discussion Related to the First Research Question

The results of this study show that the level of satisfaction and continuous intention can be related. Also, this study indicates that students are more interested in online learning than traditional face-to-face learning, along with a high level of satisfaction and continuous intention. The results of this research found clear support for online learning. One of the factors that can affect the perception level of participants can be fear of COVID-19 disease, which is considered effective on the satisfaction level of online learning [34]. Findings of [69] indicate that the online learning readiness of students can affect their satisfaction level. Learner-content interaction can be the other factor that can affect students’ satisfaction [70].

5.2. Discussion Related to the Second Research Question

One of the important questions that is still not discussed in the literature is the existence of a relationship between students’ IT skills and their satisfaction with online learning. The analysis revealed that students with a high and medium level of IT skills had a higher level of satisfaction and continuous intention to use online learning than students with a low level of IT skills, and students with a higher level of IT skills preferred online learning to face-to-face learning more than students with a lower level of IT skills. Online learning can help students with lower levels of IT skills to improve their skills due to the reason that one of the positive aspects of online learning is creating an opportunity for students to improve their IT skills [50,71,72]. Therefore, online learning could be a new and different experience for students who have no previous participating experience in online learning, and therefore it can cause them dissatisfaction for the first time. But the results of this study show that not only the satisfaction of students who did not have online education experience is high, contrary to the findings of some previous studies where students preferred face-to-face learning to online learning [19,40,42], most of the students want to continue the online learning and prefer it to face-to-face learning. Although, the satisfaction level of the students with previous experience with online learning was slightly higher than those who experienced online learning for the first time. The results also showed that students believed that flexibility in time, flexibility in place, and ease of access to resources were the most important reasons for preferring online to face-to-face learning. In online learning, the students can learn asynchronously at any time [73] and any place during the day, and it is possible to access, view, and download the resources easily.

5.3. Discussion Related to the Third Research Question

According to the analysis carried out in this research, the students of Eastern Mediterranean University had a slight drop in their academic results during online learning, which shows that the academic achievements of students from online learning do not have a great impact on their level of satisfaction. These negative changes in academic performance caused by online learning can be associated with symptoms of depression and anxiety [74], and contrary to the findings of [75], digital technologies have not had a significant impact on academic achievement.

Challenges such as difficulty in communication and the use of technology, absenteeism [76], less motivation to work, and noticeably more procrastination [77] can affect students’ academic performances. The findings of this research also show that master’s and doctoral students have more drop in academic achievements than undergraduate students. This result is inversely proportional to many other research studies, which found that the overall academic achievement of graduate students improved in online learning [63]. Moreover, our results indicate that academic achievements and the level of satisfaction of students with online learning at Eastern Mediterranean University do not affect each other.

In this research, online learning was examined from the perspective of students, and its results can be important in terms of the number of participants. To fill one of the existing research gaps, the relationship between students’ IT skills and their previous experience of using online education was also analyzed. The relationship between students’ satisfaction with online learning and their academic achievement was also investigated, and unlike most previous studies, no significant relationship was found between them.

6. Conclusions

The aim of the study was to find out the perceptions of individual students at Eastern Mediterranean University about online learning during the COVID-19 pandemic period. This study showed acceptable levels of satisfaction regarding online learning. This study highlights Eastern Mediterranean University students’ perceptions and attitudes, which showed online learning as a flexible and useful learning method during the COVID-19 pandemic period. Participants reported that they preferred online learning over traditional face-to-face learning. Besides, students’ responses demonstrated that flexibility in time and place and ease of access to resources are the most important factors in preferring online learning over traditional learning. The participant’s responses to the online survey conducted in this study showed that the majority want to continue using online learning. Students’ level of satisfaction is not affected by their academic performance, and findings indicate a slight drop in their academic achievements. A practical implication of the mentioned findings is that the assessment methods should be improved to get better results from the academic achievements of students. There is also a practical implication related to IT skills, in that the university should try to teach computer skills to students. This is the first study exploring the perceptions, attitudes, and academic performance of students during the COVID-19 pandemic at Eastern Mediterranean University. One of the strong points of this study is considered to be its large sample size in which 1087 students participated. Finally, it should be noted that this research should be done on the perception and attitudes of instructors toward online learning at Eastern Mediterranean University in the future. Examining the relationship between the results of students’ and instructors’ satisfaction levels with online learning can also be useful. Another critical challenge that we need to study in the future is to examine the relationship between the digital divide and the level of student satisfaction with online learning. Future research should also consider the effects of other variables, such as the learning style of students and the teaching style of instructors, on students’ level of satisfaction and academic achievements.

7. Limitations

The current research had several limitations. Firstly, participants were not able to explain or give reasons for their responses to the questionnaire. Secondly, all of the students that participated in our research were from the same university, which restricts the generalization of our results to a greater number of participants.