Evaluating Post-Pandemic Undergraduate Student Satisfaction with Online Learning in Saudi Arabia: The Significance of Self-Directed Learning

Abstract

Presently, numerous universities in Saudi Arabia have embraced online learning following the experience gained during the COVID-19 pandemic. While several studies have assessed the quality of online learning and student satisfaction during that period, limited research has explored students’ satisfaction post-pandemic control despite several universities planning to offer some courses online. Investigating student satisfaction post-pandemic is crucial for predicting the quality of online learning and assisting universities in enhancing the outcomes of online learning. Consequently, this study aims to examine student satisfaction with online learning by proposing a model derived from the updated Information System Success Model. The updated model factors include system quality, service quality, and information quality, supplemented by student–student interaction quality and self-directed learning. Data were collected from a sample of 150 undergraduates at King Saud University in the second semester of the 2023–2024 academic year. The research model was validated using the PLS approach. The findings indicated that only self-directed learning significantly affects students’ satisfaction with online learning. This study contributes theoretically by expanding the existing literature and enhancing the understanding of the factors that drive student satisfaction. Moreover, it provides practical contributions to decision-makers and educators developing online learning strategies focusing on enhancing self-directed learning abilities.

1. Introduction

China formally announced the presence of a novel strain of coronavirus (COVID-19) in Wuhan on 31 December 2019. One month later, on 30 January 2020, the World Health Organization (WHO) officially announced that COVID-19 was a global health emergency. Furthermore, on 11 March 2020, it was declared a pandemic [1]. Nations worldwide employed various precautionary measures to reduce the dissemination of the virus and contain the epidemic. Social distancing was one of the key approaches that was implemented to contain the propagation of the disease. Studies have shown that adhering to social distancing measures and avoiding large gatherings are essential for reducing both the magnitude and rate of transmission of COVID-19 [2]. Thus, public spaces, such as playgrounds, have been closed, cultural events have been canceled, and famous tourist destinations have discontinued their operations.

Moreover, higher education institutions around the globe had to shift their courses from on-campus to online learning to comply with the social distancing measures. On 25 March 2020, approximately 150 countries implemented temporary closures of colleges and educational institutions, affecting over 80% of the global student population [3]. Academic institutions in Germany, Spain, Italy, the USA, and China converted their traditional classes to virtual online classes. In similar ways, universities in Asia, specifically Saudi Arabia and several other Gulf states, have transitioned to distance learning. Saudi Arabia has implemented a temporary closure of all face-to-face traditional educational activities in higher institutions as a means of reducing the transmission of COVID-19, shifting them to online platforms instead. During the pandemic, all universities and colleges in Saudi Arabia transferred to online education. A total of 76 thousand educators conducted over 35 million learning sessions, benefiting more than 1.4 million students [4].

Although numerous universities in Saudi Arabia were inadequately prepared to transition their entire educational process to an online format during the pandemic, the overall experience can be deemed satisfactory. Thus, it is expected that online education will continue even after the pandemic has been controlled. Nevertheless, it is essential to acknowledge that the rapid response to the pandemic and the shift to online education forced higher education institutions to neglect the quality of education [5].

The issue of online learning quality is a major challenge facing universities in Saudi Arabia. Several universities in Saudi Arabia exhibit hesitancy in integrating online learning due to their perception of its lower level of effectiveness and quality with respect to traditional education [6]. This is due to the perception that students in online educational settings frequently encounter increased confusion, isolation, and frustration, leading to a subsequent reduction in their educational effectiveness, quality, and overall satisfaction [7]. However, the emergence of COVID-19 has highlighted the potential for Saudi universities to improve the quality and outcomes of online learning.

Multiple studies have viewed students’ satisfaction with online education as a key measure of the quality of its outcomes [8,9,10]. The association between students’ assessments of e-learning quality and their satisfaction can be stated as follows: students express higher levels of satisfaction when the quality of their online learning is high. That is, dissatisfaction arises if they discover inconsistencies between the desired and actual quality of online learning provided by their university [11].

The satisfaction level of students regarding online learning is influenced by features provided by e-learning systems [12] and by their ability for self-directed learning [13]. Regarding e-learning systems, several studies have demonstrated that factors such as the quality of system, service, and information strongly influence students’ satisfaction [14,15]. Moreover, the quality of peer interactions offered by the e-learning system is a crucial factor that affects students’ satisfaction. Kim and Kim [16] stated that students’ satisfaction can be improved by facilitating peer interactions and providing sufficient opportunities for active communication among students.

Self-directed learning involves students taking charge of their own learning process, while instructors act as facilitators rather than simply transmitting information [13]. Students have the ability to exert control over their study time, resources, goals, and learning approach, thereby enhancing their autonomy and sense of responsibility [17]. This implies that learners should have the ability to customize their learning experience according to their preferences, resulting in an improvement in students’ satisfaction with the online learning outcomes [13].

Although self-directed learning abilities have an impact on student satisfaction with online learning, there is a lack of research that specifically examines the relationship between self-directed learning and student satisfaction in the context of Saudi Arabia. Moreover, many prior studies examining students’ satisfaction with distance learning in Saudi Arabia have focused on the pre-pandemic and pandemic periods. Little research was conducted following the COVID-19 pandemic, as widely recognized universities in Saudi Arabia may be more inclined to provide online learning. Through the examination of the student’s satisfaction with online learning experiences, higher education institutions may develop strategies that ensure that the quality of these courses aligns with the university’s desired outcomes.

Hence, the aim of this study is to evaluate students’ satisfaction with the quality of online learning, focusing on the e-learning system and self-directed learning at King Saud University in Saudi Arabia post-pandemic. The study will address the following questions:

• What are the primary factors that have exerted the greatest influence on students’ levels of satisfaction with online learning post-pandemic, particularly regarding e-learning systems?
• Has the practice of self-directed learning played a role in enhancing students’ satisfaction with online learning?
• To what degree are undergraduate students satisfied with the quality and experience of online learning in the post-pandemic period?

Investigating these questions assists in exploring the current state of online learning in the post-pandemic period. By identifying factors affecting students’ satisfaction with online learning systems, such as the quality of service, systems, information, and student–student interaction, the study will highlight the elements that need improvement. Hence, this study aims to offer valuable contributions regarding areas that require enhancement and adjustment in terms of e-learning systems. Furthermore, given that online learning requires students to have self-directed learning skills, such as time management and independent homework completion, examining the influence of self-directed learning on students’ satisfaction with online learning offers crucial insight into the significance of enhancing these skills. Furthermore, as student satisfaction serves as an indicator of online learning quality, the research will help universities implement measures aimed at enhancing the overall quality of online learning, thereby assisting universities in aligning learning outcomes of online learning with their strategic goals.

The paper’s structure is outlined as follows: Section 2 offers an overview of online learning in Saudi Arabia and student satisfaction. Section 3 details the proposed model and the hypotheses. Section 4 covers the research methodology. Section 5 presents the findings of the study and analysis, while Section 6 explores the discussion. Section 7 addresses the theoretical and practical implications. Section 8 discusses limitations and future research. Finally, Section 9 presents the conclusion.

2. Literature Review

2.1. Definition of Online Learning

Online learning is a practice that has been widely discussed for over a decade. Several authors extensively examined the subject during the 1990s. For example, in 1995, ref. [18] performed a study to assess the effectiveness and impact of online learning in graduate education at George Washington University. In addition, Warschauer [19] conducted research on online learning from a sociocultural perspective in 1998. The purpose was to identify the factors that influenced a computer-based English as a Second Language writing course at Miller College in Hawaii. These factors include the complex dynamics between the teacher, researcher, and students. Besides that, Harasim [20] designed a framework called Virtual-U for distance learning in 1999. This framework facilitates active learning, cooperative work, diverse viewpoints, and knowledge-building through a range of instructional methods, such as seminars, collaborative projects, and laboratory exercises.

The early emergence of the concept of online learning led to a unified understanding of the definition of online learning. Various pieces of research generally define online learning as a method of delivering educational materials over the internet in the form of online videos, virtual meetings, and digital quizzes. That is, online learning courses can be delivered in two ways: synchronously through interactive online sessions or asynchronously, allowing students and instructors to engage without the need for simultaneous presence. In asynchronous distance learning, students are able to access educational resources without being restricted by time or location [6,21].

Online learning has been used interchangeably with terms such as e-learning, distance learning, and distance education [22]. For instance, Alghamdi et al. [5] employed the term “distance learning” interchangeably with “online learning” while examining distance learning in Saudi Arabia both prior to and following the COVID-19 pandemic. Moreover, Sadeghi [23] exclusively employed the term “distance learning” to study the limitations and advantages of shifting from traditional classrooms to distance learning. Although Sadeghi [23] did not explicitly employ the phrase “online learning”, the definition of distance learning aligns with the definition of online learning presented in the preceding paragraph. Additionally, Curtain [24] used the term “e-learning” similarly to online learning as he assessed the cost and effectiveness of online learning compared to traditional teaching methods.

Al Momani and Alnasraween [25] held distinct perspectives regarding the utilization of the terms e-learning, distance learning, and blended learning in relation to online learning. They categorized these terms as forms of online learning rather than synonymous phrases. Furthermore, they incorporated blended learning as an additional category. Al Momani and Alnasraween [25] defined e-learning as the use of technologies, including CDs, software, and educational applications, in the education process. Distance learning is defined in their paper as a method of providing educational materials asynchronously through the internet without requiring physical contact between educators and learners. On the contrary, blended learning is an educational method that integrates e-learning, distance learning, and traditional learning methods to offer students a flexible and interactive education.

The previous discussion provided an overview of the general definitions of online learning, as well as the various types and synonyms of online learning from several perspectives. The paper will use e-learning, online learning, and distance learning interchangeably. The following section will explore online learning within the context of Saudi Arabia.

2.2. Online Learning in Saudi Arabia

It is conceivable to categorize studies on online learning in Saudi Arabia into two main categories based on the emergence of COVID-19. The first group of research was performed before the COVID-19 pandemic began when distance learning was optional. The second group of studies was conducted during the pandemic when educational institutions were compelled to move their educational processes to online channels.

Before the spread of COVID-19, most studies were centered around exploring online learning, including its challenges, successes, barriers, policies, adoption, and future trends in this field. For example, in 2014, Al-Asmari and Rabb Khan [26] conducted an exploration study to assess the growth of distance learning in Saudi Arabia, evaluating its challenges in education, including universities, schools, and training centers.

Furthermore, Al Gamdi and Samarji [27] conducted a study in 2016 to examine the barriers and obstacles hindering the implementation of online learning at the university level. Data from 214 faculty members were quantitatively analyzed using MANOVA to evaluate how demographic factors influence faculty members’ perceived barriers to adopting e-learning. The results of the analysis indicated that external barriers like insufficient internet connectivity and limited IT assistance were identified as the main barriers to implementing e-learning. Internal obstacles, including a lack of incentives, were ranked second in importance, while barriers across internal and external sources, such as faculty overload, were least mentioned.

Additionally, Alhabeeb and Rowley [28] performed research in 2017 to provide insights into the critical success factors for e-learning, specifically from the viewpoint of e-learning managers in three main universities of Saudi Arabis, including Kind Saud University, Majmaah University, and Qassim University. The data were gathered through in-person interviews with seven key individuals at these academic institutions, which were subsequently analyzed qualitatively. The results of the study revealed that instructors’ familiarity with educational technologies, students’ proficiency in digital systems, and the IT infrastructure were the key success factors identified.

The above-mentioned studies were conducted before the emergence of COVID-19. These studies exhibited similarities in terms of the main ideas and the topic. They are focused on the challenges, obstacles, and success factors that could impact the implementation of online learning. It indicates that educational institutions at that time faced obstacles that caused them to be hesitant to implement online learning on a large scale. The appearance of COVID-19 forced educational institutions to implement online learning, impacting the research field at that time. Researchers continued to study the challenges and obstacles of online learning, as well as the experience, attitudes, and perceptions of online learning among students and educators.

A study was carried out by Mahyoob [29] in 2021 to examine undergraduates’ attitudes toward the effectiveness of online learning activities through the Blackboard platform. This study was conducted at three Saudi universities, Taibah University, Hail University, and Al-Baha University, during the COVID-19 pandemic. These activities included the assigned assessment tasks, online learning preference, efficiency, participation, and achievements. The data were collected via an internet-based questionnaire, and the analysis was conducted based on 333 responses from bachelor’s degree students majoring in various education disciplines. The study’s results demonstrated that distance learning has the potential to be effective. However, students expressed concerns regarding assignments, examinations, and assessment methods.

Comparable to the aforementioned study, Almaiah et al. [30] examined the attitudes of school students toward online learning during the pandemic, with a particular emphasis on the Madrasati platform. Madrasati is an internet-based platform that functions as a virtual classroom, facilitating simultaneous virtual meetings between instructors and students, or at their convenience, via pre-recorded lessons. An online random sampling technique was employed to survey 3000 students enrolled in grades 1 through 12 across various schools. The research found that system, service, and content quality, along with technological infrastructure, security considerations, and training, are pivotal in enhancing student engagement with the platform.

Additionally, Abdelwahed et al. [31] conducted a study aimed at demonstrating the effects of various factors—such as time constraints, lack of support, technical difficulties, internet accessibility issues, and insufficient technical skills—involved in distance learning in Saudi Arabia amid the COVID-19 epidemic. Furthermore, they investigated how online learning affected students’ stress and anxiety levels. Employing a structural equation model, the scholars analyzed data collected from 260 students. The results indicated that factors like time constraints, lack of support, and technical issues negatively influenced online learning. Moreover, the study demonstrated the significant influence of internet accessibility and costs on online learning. Additionally, the research identified online learning as a being a significant predictor of stress and anxiety during the pandemic.

Drawing from the preceding discussion, all universities within Saudi Arabia underwent and implemented online learning during the COVID-19 period. Consequently, several institutions initiated the offering of online learning following the pandemic [5]. Nonetheless, the majority of prior research was centered on examining online learning before or during the COVID-19 period, even in recently published studies. There have been limited quantitative studies into the present state of online learning despite the widespread adoption of this educational approach by most Saudi universities. Therefore, it is essential to assess students’ satisfaction with online learning in the post-pandemic period. Such an investigation into student satisfaction holds the potential to improve the effectiveness and quality of distance learning.

2.3. Students’ Satisfaction

This paper concentrates on assessing student satisfaction with online learning in higher education. As such, student satisfaction serves as the dependent variable, influenced by various other factors. Examining the key factors that influence students’ satisfaction with e-learning quality can offer evaluators valuable insights to enhance the quality of a specific course or program [32]. Numerous studies have been carried out to explore and evaluate students’ satisfaction with distance learning.

Jiménez-Bucarey et al. [33] introduced a model that assessed student satisfaction in higher education by examining teacher quality, technical service quality, and service quality. Data from 1430 medical school students were analyzed using a Partial Least Squares Structural Equation Model (PLS-SEM, Version 3.0). Subsequently, the researchers utilized Importance-Performance Map Analysis (IPMA) to determine the areas requiring improvement in order to increase student satisfaction. The results revealed that all of the proposed quality constructs, including teacher, technical service, and service, quality positively influenced students’ satisfaction with online learning. Furthermore, the results showed that improving the quality of technical services, especially through better training, and motivating instructors to use methods that foster student engagement, are essential for boosting student satisfaction levels.

Moreover, Cidral et al. [14] performed a study that aimed to identify the success factors influencing user satisfaction in higher education institutions in Brazil. After collecting 301 responses from undergraduate and graduate students, the data were analyzed using the PLS model. The study proposed a theoretical model that combines e-learning satisfaction with IS success theories. The outcomes of the analysis showed that various factors, such as the quality of information, system, teacher’s perspective on e-learning, variety in assessment methods and engagement between learners, all contribute to user satisfaction. On the other hand, both service quality and collaboration quality did not show a statistically significant relationship with student satisfaction.

Furthermore, Dangaiso et al. [12] examined how student satisfaction and loyalty are influenced by their perception of e-learning service quality in Zimbabwe. Data were collected from students attending four public and private universities in Zimbabwe and analyzed based on 321 responses. The results of the structural equation modeling (SEM) analysis demonstrated that the quality of the system, information, and service are significant in terms of influencing student satisfaction and loyalty in relation to e-learning platforms.

In the same regard, Al-Adwan et al. [13] explored how students are adopting e-learning systems across three distinct private universities in Jordan. Throughout their study, they investigated the factors affecting student satisfaction and their willingness to keep using e-learning systems. Using an expanded IS success model that includes service quality, system quality, and information quality as key components, they also added self-directed learning as a factor affecting student satisfaction with e-learning systems. Utilizing PLS-SEM, they analyzed data collected from 590 undergraduate and postgraduate students. The results revealed that the quality of service, system, and information had a positive influence on student satisfaction and their willingness to continue using the systems. However, self-directed learning exhibited a negative influence on both student satisfaction and their intention to continue usage.

In the context of Saudi Arabian, Alsomali et al. [34] performed a study to assess science student satisfaction with e-learning (asynchronous) and virtual classes (synchronous) amid the COVID-19 pandemic, considering demographic variables, such as students’ area of specialization, academic level, and GPA. They collected a sample of 116 science students from Imam Abdulrahman Bin Faisal University for the 2019/2020 academic year. Data analysis was conducted using statistical methods, including ANOVA tests, mean, and standard deviations. The results indicated that there were no statistically significant differences in the use and satisfaction of e-learning and virtual classes among students based on their field of study, academic level, or GPA.

Additionally, Aldhahi et al. [35] assessed undergraduate satisfaction with online learning during the COVID-19 pandemic and explored the relationship between online learning self-efficacy and satisfaction. A survey was distributed to 22 Saudi Arabian universities, with 1226 respondents participating. Statistical tests were used to analyze satisfaction levels based on educational variables, revealing varying degrees of satisfaction. The study found that student satisfaction with online learning was impacted by their self-efficacy in areas such as time management, technology, and learning, highlighting the importance of enhancing these skills to improve overall satisfaction with remote learning experiences.

Moreover, Alenezi et al. [36] examined student performance and satisfaction following the shift to online learning, emphasizing the challenges and changes encountered during the pandemic, specifically within medical school at King Saud University, with a focus on psychiatry. Through a quantitative research analysis of secondary data, the researcher analyzed student satisfaction and performance in a psychiatric course during the academic years 2020 (prior to the pandemic, conducted onsite) and 2021 (amid the pandemic, conducted online). The sample involved 193 medical students; 80 students received traditional onsite learning and assessment, while 113 students underwent fully online learning and assessment. The results revealed that those who underwent full online learning and assessment exhibited significantly higher levels of satisfaction and grades compared to those who underwent onsite learning.

The studies mentioned earlier demonstrate various factors contributing to student satisfaction with online learning, particularly regarding e-learning systems. These factors encompass service quality, information quality, system quality, interaction quality, and self-learning capabilities. Studies on these elements have been conducted in several countries, such as Brazil, Zimbabwe, Jordan, and Tanzania [12,13,14,15].

Regarding Saudi Arabia, a comparable pattern with online learning research arose, with most studies focusing on assessing satisfaction levels during the COVID-19 pandemic.

Table 1. A summary of previous studies in Saudi Arabia.

Source	The Purpose	The Methodology	Key Findings
Studies on Online Learning in Saudi Arabia Before COVID-19
[26]	Investigating the growth of e-learning focuses mainly on the obstacles and future prospects.	Descriptive approach	The obstacles encountered in e-learning encompassed technical, financial, and administrative aspects.
[27]	Examining the barriers hindering the adoption of e-learning in higher education.	Quantitative approach (MANCOVA)	Lack of internet and IT assistance were the major e-learning obstacles. Lack of incentives was second, while faculty overload was least essential.
[28]	Providing insights into the critical success factors for e-learning	Qualitative approach	Instructor expertise of learning technologies, student computer ability, and technological infrastructure were major success factors.
Studies on Online Learning in Saudi Arabia After COVID-19
[29]	Examining undergraduates’ attitudes toward the effectiveness of online learning activities through the Blackboard during COVID-19.	Quantitative approach	Students believed that online learning was effective. Nevertheless, their primary concerns centered around the assignments, examinations, and assessment methodologies.
[30]	Examining the attitudes of school students toward online learning during the pandemic with a particular emphasis on the Madrasati platform.	Quantitative approach	The main factors impacting the use of the platform were the system, service, content, technological infrastructure, awareness, security challenges, and training.
[31]	Examining the effects of various factors—such as time constraints, lack of support, technical difficulties, internet accessibility issues, and insufficient technical skills—on online learning during COVID-19. Furthermore, they investigated the influence of online learning on the stress and anxiety levels of students.	Quantitative approach	Time constraints, lack of support, and technical issues negatively impacted online learning. Online learning was a significant predictor of stress and anxiety during the pandemic
Studies in Students’ Satisfaction with Online learning in Saudi Arabia
[37]	Exploring the satisfaction of science students with e-learning (asynchronous learning) and virtual classes (synchronous learning) during the COVID-19 crisis, considering demographic variables such as students’ area of specialization, academic level, and GPA.	Quantitative approach (ANOVA)	There were no statistically significant differences in satisfaction levels of students between e-learning and virtual classes based on their specialization, academic level, or GPA.
[35]	Assessing undergraduate students’ satisfaction with e-learning amid the COVID-19 pandemic and explore the relationship between online learning self-efficacy and satisfaction.	Quantitative approach	Students’ satisfaction with e-learning was influenced by their self-efficacy in areas such as time management, technology, and learning.
[34]	Examining students’ performance and satisfaction during the pandemic in psychiatry course through conducting a comparative evaluation using pre- and post-pandemic student survey data.	Quantitative approach	Students’ satisfaction and grades were higher during online course (during the pandemic).

provides a summary of previous studies within the context of Saudi Arabia.

Table 1 is divided into three sections, each providing key findings of previous research within the context of Saudi Arabia. The first section presents studies published before the COVID-19 pandemic, encompassing data published up to 2019. The second section focuses on research published during and after the pandemic, indicating studies published from 2020. The third section presents studies related to student satisfaction in Saudi Arabia published after 2019. The main goal of the table is to demonstrate the trend of online learning studies in Saudi Arabia before and after COVID-19, with a specific emphasis on student satisfaction.

It is evident that before COVID-19, studies were primarily exploring the potential of online learning. However, after COVID-19, even studies published in 2023 continue to investigate student satisfaction with distance learning during the pandemic. There is a notable gap in studies assessing student satisfaction in Saudi Arabia after COVID-19 as universities transitioned to providing online learning. Therefore, this study seeks to investigate student satisfaction with online learning following the pandemic based on e-learning systems and self-directed learning abilities. Such an investigation could potentially lead to improved or modified educational strategies that improve the quality of online learning.

3. Research Model and Hypotheses

3.1. Research Model

The majority of factors examined in this study were drawn from the updated Information System Success Model (the updated D&M). This model was first established in 1992 by DeLone and McLean and subsequently revised in 2003 to accommodate for the evolving roles and advancements in information systems [38]. The revised model suggests that service quality, information quality, and system quality directly impact user satisfaction, willingness to use, and actual utilization. As the focus of this research was on student satisfaction, these three factors—service quality, information quality, and system quality—will be examined in this study.

DeLone and McLean [38] emphasized that the updated D&M model should not be viewed as a universally applicable measure to assess information systems. It was suggested that researchers modify the number of factors as necessary to ensure the model’s relevance to their specific context. Various research has adapted the revised D&M model and subsequently utilized it in the domain of distance learning to assess the success and satisfaction of online learning systems [15,39].

This study introduced a framework derived from the updated D&M model, with two additional factors affecting student satisfaction: student–student interactions and self-directed learning. The rationale behind the inclusion of student–student interactions or a peer interaction factor was due to their significant role in terms of impacting student satisfaction with online learning. In distance learning environments, students encounter feelings of isolation, lack of motivation, and a lack of belonging to an academic community, which has a negative effect on their satisfaction [40,41]. However, these feelings could potentially diminish if e-learning systems incorporate features designed to promote interaction and collaboration among students.

With regard to self-directed learning, it was added as a context-specific factor that was mainly related to online learning. Self-directed learning has been viewed as a factor that impacts student satisfaction with online learning [13]. In traditional educational settings, students rely heavily on instructors to guide the learning process, including setting objectives, choosing teaching methods, and assigning tasks. However, in online learning environments, the reliance on instructors is reduced compared to face-to-face education. Consequently, students must possess the skills to independently direct and manage their learning experiences [42]. Therefore, assessing self-directed learning abilities is a key element when assessing satisfaction with online learning (see Figure 1).

3.2. Hypotheses

3.2.1. System Quality (SysQ)

System quality relates to users’ perceptions of the performance and effectiveness of an online learning platform. It focuses on aspects such as system reliability, responsiveness, ease of use, and overall functionality [43]. In the domain of distance learning, the satisfaction of learners is notably impacted by the excellence of the system, particularly with regard to factors such as simplicity of use, ease of learning, and user-friendliness [15]. That is, learners are inclined to consistently engage and experience higher levels of satisfaction when the system is user-friendly and navigable. Hence, the first hypothesis was formulated as follows:

H1: 

System quality positively influences student satisfaction.

3.2.2. Service Quality (SerQ)

Service quality is assessed by how closely the provided service level matches user expectations [43]. With regard to online learning, service quality refers to the necessary features and characteristics that ensure efficient support services for students [15]. It includes the assistance provided by IT personnel via helpdesk support, training, and emergency assistance. According to Cidral et al. [14], service quality in e-learning encompasses aspects such as responsiveness, empathy, trust, and security provided by the supporting staff in e-learning systems, which are crucial for enhancing the satisfaction level of the users and the overall success of the online learning system. By focusing on service quality, e-learning systems can provide a supportive and reliable environment that meets students’ needs and contributes to positive student satisfaction. According to [44], several studies revealed that the quality of distance learning and the level of satisfaction experienced by students are positively related. Therefore, the second hypothesis was developed as follows:

H2: 

Service quality positively influences student satisfaction.

3.2.3. Information Quality (IQ)

Information quality in online learning is the students’ subjective assessment of the information they receive from an e-learning platform. This refers to the quality of content that students can access, which includes aspects such as clarity, understandability, style (text, audio, video), sufficiency, currency, usefulness, and reliability. The quality of information is crucial when evaluating the effectiveness of the content delivered by the e-learning system, impacting student satisfaction, engagement, and learning outcomes [12]. Regarding how the quality of e-learning information affects student satisfaction, Puriwat and Tripopsakul [32] emphasized that high information quality leads users to view the system as important and beneficial for their tasks, impacting their overall attitude and satisfaction with the system. This highlights the need to maintain high information quality in e-learning systems to improve user learning experiences and satisfaction. Hence, the third hypothesis was developed as follows:

H3: 

Information quality positively influences student satisfaction.

3.2.4. Student–Student Interaction (SST)

Student–student interaction, also known as peer interaction, refers to the engagement, interaction, and collaboration among students, with or without being facilitated by educators. Student–student interaction in online learning courses allows enrolled students to exchange ideas, insights, and perspectives related to their coursework. It facilitates group work, discussions, and collaborative assistance with assignments. According to Xu et al. [41], implementing peer interaction in online learning enhances the sense of belonging and social involvement between students. Moreover, they argued that students in online courses characterized by high levels of interaction tend to be more satisfied compared to those in less interactive courses. This highlights the importance of peer interaction features in online learning environments for enhancing students’ satisfaction; the hypothesis was developed as follows:

H4: 

Student–student interaction positively influences student satisfaction.

3.2.5. Self-Directed Learning (SDL)

Self-directed learning arises when students take responsibility for and actively control their own learning process. Self-directed learning entails students taking charge, whether or not they receive guidance from instructors, to determine their educational needs and objectives, choose appropriate learning methods, and assess the outcomes of their learning [45]. The capability of students to manage and regulate their learning serves as a significant predictor of the success of the online learning experience [46]. Al-Adwan et al. [13] argued that the absence of self-directed learning skills and abilities among students presents a significant obstacle to their satisfaction and success in online learning. In other words, it can be stated that greater self-directed learning abilities correlate with higher levels of student satisfaction. Hence, the proposed hypothesis was formulated as follows: H5—Self-directed learning positively influences students’ satisfaction.

After formulating the five hypotheses related to the research model, the subsequent section will discuss the research methodology implemented in this study to test and evaluate the proposed model.

4. Research Methodology

4.1. Sample and Data Collection

This study aims to evaluate undergraduate satisfaction with online learning in Saudi Arabia following the pandemic. As universities now offer online courses to undergraduates, this evaluation will help enhance the quality of online learning outcomes to ensure they align with the universities’ objectives. To collect the data, a closed-ended questionnaire was distributed to undergraduate students at King Saud University who were registered for the second semester of the 2023–2024 academic year. Two steps were implemented to ensure the precision and clarity of the questionnaire. The survey was translated from English to Arabic, given that the recipients’ primary native language was Arabic. Afterward, it was reviewed by three translation experts to verify consistency and alignment between the Arabic and English versions. The survey contained a total of 22 questions. Questionnaire items were evaluated using a five-point Likert scale, where 1 indicated strong disagreement and 5 represented strong agreement. The 22 questions used in this research were created based on validated items from previous studies and modified to meet the purpose of this research. The measurement items utilized in this study, along with their corresponding sources, are detailed in

Table 2. The measurement items.

Construct	Items	Adapted from
System Quality (SysQ)	SysQ1: The E-learning systems used by my university is convenient to use SysQ2: The E-learning systems used by my university enable me to quickly locate the information I need. SysQ3: The E-learning systems used by my university system are well structured SysQ4: The E-learning systems used by my university are easy to use.	[14]
Service Quality (SerQ)	SerQ1: The service personnel are always ready to assist anytime I require assistance with the e-learning systems. SerQ2: The service personnel are keen to offer timely services and support regarding e-learning systems. SerQ3: The service personnel are knowledgeable enough to address my issues regarding the E-learning systems.
Information Quality (IQ)	IQ1: Information and course content provided by the E-learning systems are always available during the semester IQ2: Information and course content provided by the E-learning systems are easy to understand IQ3: Information and course content provided by the E-learning systems appear clear and well formatted IQ3: Information and course content provided by the E-learning systems are in a readily usable form	[47]
Student–Student Interaction (SSI)	SSI1: The E-learning systems used by my university facilitate easy communication with classmates. SSI2: The E-learning systems used by my university facilitates the efficient and effective sharing of information with my classmates. SSI3: The E-learning systems used by my university allow for convenient storage and sharing of documents with my classmates. SSI4: The E-learning systems used by my university enable me to quickly locate my classmates’ contact information.	[14]
Self-Directed Learning (SDL)	SDL1: Regarding my learning and studying style, I can see myself as a self-directed person. That is, I can set my learning goals and organize my learning activities independently without constant guidance and supervision from others. SDL2: I exhibit self-discipline in my studies and find it effortless to allocate time for studying and finishing assignments SDL3: I establish goals for my studies and demonstrate a strong sense of initiative. SDL4: I can properly organize my study schedule and meet deadlines for tasks.	[13]
Students Satisfaction (SS)	SS1: Online learning is a wonderful experience. SS2: I am satisfied with the online learning experience. SS3: I would like to study more online courses.	[48]

.

We redesigned the questionnaire to include an equal number of positively and negatively worded questions for each construct, aiming to enhance validity and reduce acquiescence bias. For instance, a positive statement like “The e-learning system is easy to use” was paired with a negative counterpart, such as “The e-learning system is difficult to navigate.” This approach ensures a balanced assessment of participants’ attitudes. Additionally, we randomized the order of all items to prevent participants from identifying response patterns, further improving the quality and reliability of the collected data.

The questionnaire was developed using SurveyMonkey and distributed through various digital mediums such as email and WhatsApp (Android: 2.24.16.77). To verify the reliability and validity of the study instrument, pilot testing was completed by 20 students. The purpose of pilot testing is to identify any potential flaws or challenges with the research instrument before initiating the data collection process. Following the pilot study, the survey was distributed to around 400 students, resulting in the gathering of 170 replies. After data cleaning, a total of 150 responses were deemed appropriate for analysis. While the sample size is relatively small, several studies have shown that PLS-SEM is well-suited for analyzing samples exceeding 100 [49,50]. Among the respondents, 27% were male and 73% were female. All of the participants were undergraduate students, with ages ranging from 18 to 22 years old.

4.2. Data Analysis Procedures

The data were analyzed utilizing the Partial Least Squares Structural Equation Model (PLS-SEM). The PLS-SEM has been a preferred method for analyzing user satisfaction indices in various countries, such as Sweden, the United States, Europe, and Portugal [51]. Since this study focuses on student satisfaction, the Partial Least Squares (PLS) method will be appropriate for the analysis. In the Partial Least Squares (PLS) path method, there are two models used to analyze the collected data: the measurement model and the structural model [51]. The analysis was performed using SmartPLS (version 4.0.9.6 for macOS) in two phases. The initial step involved evaluating convergent and discriminant validity by examining the measurement model. This was followed by assessing the structural model to test the proposed hypotheses.

5. Results and Analysis

5.1. Measurement Model

During this step, an evaluation was conducted to establish the reliability and validity of the measurement model [52,53]. The indicator reliability aimed to assess the trustworthiness and the ability of each item to accurately measure its respective construct, whereas Cronbach’s alpha and CR measured internal consistency [54]. This means they evaluated how well a set of items consistently reflected their corresponding construct. The results shown in

Table 3. The measurement model statistics.

Variable	Items	Indicator Loading	Cronbach’s Alpha	Composite Reliability	AVE
SysQ	SysQ1	0.888	0.853	0.899	0.692
	SysQ2	0.703
	SysQ3	0.847
	SysQ4	0.877
SerQ	SerQ1	0.792	0.907	0.904	0.760
	SerQ2	0.996
	SerQ3	0.813
IQ	IQ1	0.735	0.861	0.904	0.702
	IQ2	0.884
	IQ3	0.884
	IQ4	0.841
SSI	SSI1	0.839	0.871	0.911	0.719
	SSI2	0.888
	SSI3	0.878
	SSI4	0.781
SDL	SDL1	0.850	0.893	0.925	0.754
	SDL2	0.887
	SDL3	0.884
	SDL4	0.852
SS	SS1	0.930	0.893	0.933	0.823
	SS2	0.946
	SS4	0.842

revealed that all 22 items exhibited loading factors surpassing 0.7, indicating that the threshold for indicator reliability was met. Moreover, both Cronbach’s alpha and CR exceeded 0.70, suggesting that the above criterion was satisfied, as both measures demonstrated values above 0.7, thus indicating an adequate level of internal consistency.

With regard to the validity of the measurement model, it was evaluated through convergent validity and discriminant validity. Convergent validity, the degree to which two distinct measures or items effectively capture and assess the same construct, was evaluated using the average variance extracted (AVE), which required the AVE value to exceed 0. According to Table 3, all values of AVE for all constructs fell within the range of 0.692 to 0.823, which indicated that the requirement of convergent validity was satisfied.

Table 4. Discriminant validity: square root of AVE.

Variables	IQ	SDL	SerQ	SSI	SS	SysQ
IQ	0.838
SDL	0.325	0.868
SerQ	0.342	0.071	0.872
SSI	0.500	0.334	0.352	0.848
SS	0.180	0.389	0.020	0.218	0.907
SysQ	0.572	0.318	0.346	0.631	0.276	0.832

displays the square root of AVE for all constructs and indicates that these values exceed the correlations of each construct with other constructs.

With regard to the criteria of cross-loading, it is essential that a particular item demonstrates higher loadings on its perspective construct compared with its loading on other constructs that are being addressed in the study. The values of the cross-factor for all items are presented in

Table 5. Cross-loading values.

Variables	IQ	SDL	SerQ	SSI	SS	SysQ
IQ1	0.735	0.279	0.404	0.422	0.110	0.361
IQ2	0.884	0.316	0.286	0.400	0.179	0.499
IQ3	0.884	0.230	0.236	0.461	0.181	0.502
IQ4	0.841	0.285	0.267	0.400	0.093	0.569
SDL1	0.378	0.850	−0.006	0.340	0.413	0.336
SDL2	0.175	0.887	0.078	0.312	0.289	0.262
SDL3	0.318	0.884	0.127	0.294	0.281	0.282
SDL4	0.224	0.852	0.074	0.203	0.330	0.204
SS1	0.157	0.338	−0.010	0.184	0.930	0.229
SS2	0.202	0.413	0.036	0.230	0.946	0.314
SS3	0.115	0.288	0.026	0.170	0.842	0.188
SSI1	0.469	0.228	0.297	0.839	0.178	0.514
SSI2	0.430	0.380	0.306	0.888	0.158	0.615
SSI3	0.419	0.312	0.304	0.878	0.236	0.541
SSI4	0.378	0.204	0.288	0.781	0.141	0.470
SerQ1	0.361	0.093	0.792	0.296	−0.001	0.300
SerQ2	0.340	0.062	0.996	0.354	0.021	0.343
SerQ3	0.322	0.128	0.813	0.269	0.003	0.307
SysQ1	0.525	0.324	0.278	0.558	0.277	0.888
SysQ2	0.426	0.346	0.343	0.538	0.124	0.703
SysQ3	0.509	0.143	0.332	0.523	0.236	0.847
SysQ4	0.445	0.291	0.249	0.516	0.239	0.877

, which confirms that the requirement for cross-loading was met.

After evaluating the reliability and validity of the measurement model, the next step is assessing the structural model, which was performed as shown in the following section.

5.2. Structural Model

This step was performed to assess the relationship among variables and evaluate the predictive capabilities of the model. The assessment of the structural model was primarily based on the significance of path coefficients, confidence interval, and the R2. The PLS analysis involved the implementation of a bootstrapping procedure with a sample size of 5000. This procedure was conducted to assess the structural models and determine the path coefficients, as well as their corresponding levels of significance.

Table 6. Hypothesis testing results.

Hypothesis	β	T Statistics	p Values	Confidence Interval		Result
				Lower Level 2.5%	Upper Level 97.5%
(IQ) → (SS)	−0.028	0.257	0.797	−0.225	0.218	Not supported
(SDL) → (SS)	0.333	4.509	0.000	0.185	0.476	Supported
(SerQ) → (SS)	−0.069	0.736	0.462	−0.266	0.084	Not supported
(SSI) → (SS)	0.020	0.202	0.840	−0.181	0.222	Not supported
(SysQ) → (SS)	0.198	1.531	0.126	−0.072	0.427	Not supported

demonstrates the hypothesis test results.

Table 6 reveals that the hypotheses concerning information quality, service quality, student–student interaction, and system quality did not exhibit statistically significant relationships with student satisfaction regarding online learning experiences. Starting with information quality, the beta coefficient of −0.028 indicated a negative association with student satisfaction. However, this relationship is not statistically significant, as indicated by the t-statistic of 0.257 and the p-value of 0.797. Similar to information quality, the beta coefficient of service quality was −0.069, which also demonstrated a negative association with student satisfaction; however, this relationship is not statistically significant, with a t-statistic of 0.736 and a p-value of 0.462. Student–student interaction and system quality had a positive association with student satisfaction, as indicated by beta coefficients of 0.020 and 0.198, respectively. Nevertheless, these associations were not statistically significant, as indicated by t-statistics of 0.202 and 1.531, and p-values of 0.840 and 0.126. Conversely, self-directed learning demonstrated a statistically significant relationship with student satisfaction, as supported by the t-statistic of 4.509 and the p-value of 0.000. Hence, there is strong evidence to conclude that self-directed learning positively influences student satisfaction with online learning. We conducted a comparative analysis of key studies to situate our findings within the broader context of research on student satisfaction in online learning.

Table 7. Comparison of factors influencing student satisfaction across studies.

Study	Country	Sample Size	System Quality	Service Quality	Information Quality	Student–Student Interaction	Self-Directed Learning
Current Study	Saudi Arabia	150	Not Significant	Not Significant	Not Significant	Not Significant	Significant
Cidral et al. (2018) [14]	Brazil	301	Significant	Not Significant	Significant	Significant	Not Studied
Dangaiso et al. (2022) [12]	Zimbabwe	321	Significant	Significant	Significant	Not Studied	Not Studied
Al-Adwan et al. (2022) [13]	Jordan	590	Significant	Significant	Significant	Not Studied	Not Significant
Jiménez-Bucarey et al. (2021) [33]	Peru	1430	Significant	Significant	Not Studied	Not Studied	Not Studied

presents our results alongside those from studies conducted in various countries and settings. This comparison highlights how our results align with or diverge from other investigations in the field.

The results of our research differ from many other studies, particularly in terms of the non-significant impact of e-learning system elements on student satisfaction. While studies from Brazil, Zimbabwe, Jordan, and Peru identified system quality, service quality, and information quality as key factors driving student satisfaction, our study in the Saudi Arabian context did not reflect these findings. Instead, self-directed learning emerged as the only significant factor influencing student satisfaction in our research.

Confidence interval is also utilized in this study as an additional measurement for testing hypotheses. For a relationship to be statistically significant, zero should not fall within the confidence interval range. This implies that the interval between the lower and upper bounds should exclude zero. Based on data presented in Table 6, the confidence interval condition is only applied to the relationship between self-directed learning and student satisfaction. With the lower and upper bounds at 0.185 and 0.476, respectively, this suggests that the relationship between these two variables is statistically significant. Across all other relationships, the confidence intervals encompassed zero between their upper and lower bounds, indicating that these relationships are not statistically significant.

Including both positively and negatively phrased questions in the updated questionnaire enabled a more effective assessment of participant consistency. We utilized Cronbach’s alpha to evaluate internal consistency, and the results indicated strong reliability across all dimensions. Furthermore, no significant response bias was detected between the positive and negative items, confirming that the randomization of item order successfully minimized any potential response patterns.

To evaluate the model’s predictive power, the R² value was used. R² is a statistical metric that shows the extent to which variation in the dependent variable is explained by the independent variables. The acceptable threshold for R² is at least 0.10, with the condition that some hypotheses must be statistically significant. The R² value for the model was 0.18, indicating that information quality, self-directed learning, service quality, student–student interaction, and system quality collectively explained 18% of the variation in student satisfaction, surpassing the acceptable level.

6. Discussion

The objective of this study was to evaluate the satisfaction of undergraduate students at King Saud University with online learning following the COVID-19 pandemic. Post-COVID-19, numerous universities in Saudi Arabia have continued to deliver essential courses to undergraduate students through online platforms. Evaluating student satisfaction with these courses is crucial for decision-makers to evaluate the effectiveness of these courses and make informed decisions regarding future educational strategies and resource allocations. This study incorporated two primary factors to evaluate student satisfaction with online learning. The first set of factors were related to the e-learning system, including information quality, service quality, student–student interaction, and system quality. The second factor involved self-directed learning, as online learning requires students to manage their learning process, including time allocation, resource utilization, and selecting their preferred learning approach. The analysis of this study revealed that self-directed learning was the only factor with a statistically significant positive impact on student satisfaction. The remaining factors that were related to the e-learning system, including information quality, service quality, student–student interaction, and system quality, did not demonstrate significant relationships with student satisfaction in this analysis. The study’s findings regarding the e-learning system contradicted those of several previous studies, including [12,13,14,33], which reported significant influences of certain e-learning system factors on student satisfaction with online learning. Dangaiso et al. [12] demonstrated that the quality of the system, information, and service of online learning systems had a positive influence on student satisfaction with online learning. Additionally, Jiménez-Bucarey et al. [33] found that service quality and system quality positively affected student satisfaction with online learning. Furthermore, Cidral et al. [14] found that information quality, peer interaction, and system quality significantly affected student satisfaction with online learning. Moreover, Al-Adwan et al. [13] revealed that information quality, system quality, and service quality positively influenced student satisfaction with online learning.

The results obtained by Al-Adwan et al. [13] also contradicted the findings of this research regarding the self-directed learning factor. While this study suggests that self-directed learning significantly impacts student satisfaction with online learning, the results obtained by Al-Adwan et al. [13] indicate that self-directed learning did not significantly impact student satisfaction in the context of online learning.

In the context of online learning, service quality encompasses various factors, including the reliability of services supporting the learning environment, the availability and efficiency of technical assistance, and the responsiveness of support staff. In our study, cultural and contextual aspects of the Saudi Arabian education system may help explain why service quality did not have a significant impact on student satisfaction.

One possible explanation is that Saudi students may view service quality as a basic expectation rather than a key driver of satisfaction. In a well-funded academic environment, where reliable and accessible support services are considered a given, students may prioritize other aspects of their learning experience, such as the quality of course materials or their ability to manage their learning independently—factors that emerged as more significant in this research.

Nevertheless, service quality remains essential for ensuring a smooth online learning experience, particularly in settings where technical issues could disrupt instruction. Institutions may need to focus on providing more proactive support, such as real-time troubleshooting, clear instructions for using e-learning platforms, and personalized assistance tailored to students’ individual needs. While service quality may not always directly affect overall satisfaction, maintaining high standards in this area can help foster a positive and supportive learning environment.

The findings regarding the lack of significant influence of e-learning system factors on student satisfaction could be attributed to two main reasons. Firstly, it is possible that all aspects related to e-learning systems, including system quality, service quality, information quality, and student–student interaction quality expectations improved following the COVID-19 pandemic, thereby meeting students’ expectations levels. This improvement may be due to King Saud University’s increased investment in online learning infrastructure. With a focus on enhancing technological capabilities and support systems, the university aimed to provide an effective online learning experience for students. Secondly, students may have assumed that these features are essential and expected them to function well by default. As a result, these factors did not significantly impact their satisfaction with online learning, indicating the necessity for further investigation beyond aspects related to e-learning systems. This was evident in this study, as the only significant factor was self-directed learning, which was unrelated to e-learning systems.

When analyzing our results, it is essential to consider how Saudi Arabia’s educational and cultural norms shape students’ perceptions and experiences with e-learning. In Saudi society, strong family ties, respect for authority, and shared values are highly emphasized. These cultural traits may influence how students engage with online learning. For example, students who prioritize direct interaction with teachers and traditional face-to-face instruction may view the features of e-learning systems as less critical to their overall satisfaction. This cultural preference for personal interaction may help explain why e-learning system features did not significantly impact student satisfaction in our study.

Additionally, the rapid shift to online learning during the pandemic accelerated technology adoption but also highlighted disparities in students’ digital access and preparedness. While universities now have the opportunity to integrate technological advancements and use e-learning as a complement to, rather than a full replacement for, traditional methods post-pandemic, challenges remain. Ensuring equitable access to technology, addressing varying levels of digital literacy, and fostering self-directed learning skills are unresolved issues. These factors likely contributed to the prominence of self-directed learning as a key predictor of student satisfaction, emphasizing the need for educational policies that support students in developing autonomy and effective online learning habits within Saudi Arabia’s cultural context.

Online learning relies heavily on student-to-student interactions, which are critical; however, our study found that these interactions had little impact on student satisfaction. The traditional Saudi educational model, which favors teacher-led instruction over peer collaboration, may account for this, as students tend to prioritize interactions with their teachers over their peers. Additionally, the e-learning system may not have offered sufficient tools for meaningful peer interaction, making online exchanges less engaging than those in face-to-face settings. As students continue to adapt to virtual learning environments lacking the social dynamics of physical classrooms, their perception of peer interaction may have shifted. To improve student–student engagement, educators should develop collaboration tools, design peer-focused tasks, and provide guidance to foster meaningful interactions online. Addressing these elements could enhance cooperative learning and, in turn, improve student satisfaction.

Out of the five hypotheses tested, only one was supported, which may be attributed to the relatively small sample size of 150 participants, potentially contributing to the wide confidence intervals observed. Although Partial Least Squares Structural Equation Modeling (PLS-SEM) is known for its resilience with smaller sample sizes and is commonly used in exploratory research, a larger sample could provide more precise estimates, narrower confidence intervals, and possibly reveal additional significant relationships. While we did not increase the sample size in this study, we acknowledge this limitation and recommend that future research includes a larger participant pool to improve statistical power and reliability. This would result in more robust findings and a more comprehensive evaluation of the hypothesized relationships.

Self-directed learning emerged as the only significant factor influencing student satisfaction with online learning. This implies that when students take greater control over their learning process, managing their time and resources effectively, they are more likely to be satisfied with their leaning process. Building self-directed learning skills not only increases student satisfaction but also empowers them to become more independent learners. By taking control over their learning progression, students develop the ability to establish objectives, actively seek resources, and modify their study practices to align with their own needs.

Our updated questionnaire design significantly enhanced the validity of the research by incorporating both positively and negatively phrased questions, reducing the potential for acquiescence bias. This approach ensures a more accurate representation of student satisfaction with the e-learning system. Future research would benefit from continuing to use this method to improve response reliability and eliminate any biases introduced by unbalanced question phrasing.

This study is based primarily on data from King Saud University, one of the leading institutions in Saudi Arabia. While the results provide valuable insights into post-pandemic student satisfaction with online learning, the unique status and resources available at King Saud University may not fully reflect the broader educational landscape across the country. This limitation suggests that the results could be influenced by the specific advantages or challenges associated with this university’s online learning systems, which may offer more advanced technological infrastructure and support services compared to other institutions in the region.

Future research should aim to include a more diverse selection of universities, covering both large and smaller institutions across different regions of Saudi Arabia. This would improve the generalizability of the results and offer a broader understanding of undergraduate satisfaction with online learning in various educational contexts post-pandemic. Additionally, comparative studies involving multiple universities could highlight how institutional differences affect student satisfaction and the effectiveness of online learning systems. Such studies would also help identify the best practices and areas for improvement, providing valuable guidance for policy-making and strategic enhancements in online education nationwide.

7. Theoretical and Practical Implications

The research made a theoretical contribution by integrating context-specific factors into the updated Information System Success Model utilized in this study. Specifically, it introduced self-directed learning as a determinant for evaluating student satisfaction with online learning, a factor that was statistically significant. These results expand the understanding of what influences student satisfaction in online learning, aiding researchers in refining their methodologies and constructing a more accurate model.

Regarding practical implications, decision-makers and instructors could develop online learning strategies focusing on enhancing self-directed learning skills, for example, by providing online tutorials and self-assessment tools. Online tutorials give students the ability to manage their study time as they can access these tutorials based on their availability. Self-assessment tools enable students to monitor their progress and identify areas needing improvement. By implementing these strategies, universities can adopt a more student-centered approach to online learning. This encourages students to take control of their learning process, ultimately enhancing their satisfaction and improving the quality of online learning outcomes.

8. Limitation and Future Research

The results of this study revealed that self-directed learning positively influences student satisfaction with distance learning. However, the research also revealed that factors associated with the e-learning system, such as system quality, service quality, information quality, and peer interaction quality, did not show statistical significance. While the results concerning self-directed learning might be suitable for generalization to other universities, the conclusions regarding e-learning systems may not be as easily generalized. This limitation arises from the diverse range of e-learning systems utilized by universities worldwide, each with its unique features and capabilities. Consequently, certain crucial functionalities present in one university’s e-learning system may be absent in others, possibly affecting the link between student satisfaction and online learning. Thus, this reduces the generalizability of the results to other universities within Saudi Arabia. Moreover, although the sample size was appropriate for using PLS-SEM, the results might still be affected by the limitations of a smaller sample, such as reduced statistical power, which increases the risk of missing significant relationships. This is evident in the study, as self-directed learning is the only factor that shows significant results.

With regard to future research, a cross-sectional study involving the collection of data from different universities will be conducted. This approach would provide a diverse and representative sample, capturing a range of experiences and perspectives across various institutional settings. Furthermore, collecting data from multiple universities could improve the generalizability of the results, providing a more comprehensive understanding of the factors affecting student satisfaction with online learning. Additionally, comparing the results across different institutions could identify best practices and areas for improvement in online learning, ultimately developing new strategies or modifying existing policies based on informed insight.

In the discussion of the methodology, it was suggested that future surveys incorporate both positively and negatively worded questions to enhance validity and reduce potential response biases. However, it is important to clarify that this study did not implement this redesigned survey. As a result, the results presented here are based on the original survey design. Future research should consider using the redesigned survey to collect new data, which would provide a more solid foundation for assessing the impact of question design on survey responses and, in turn, on modeling student satisfaction.

Future studies should also investigate the effect of balanced item wording across various student demographics, despite this study’s use of reverse-coded questions to mitigate response bias. Additionally, future research could explore other forms of randomization to see if they lead to even more consistent and reliable results.

9. Conclusions

This study aimed to evaluate undergraduates’ satisfaction with online learning following the COVID-19 pandemic, as several universities in Saudi Arabia seek to integrate online learning methods alongside traditional approaches. Student satisfaction served as an indicator of the degree to which online learning quality aligned with university strategies and objectives. The assessment of student satisfaction focused on two factors: self-directed learning skills and e-learning system characteristics, encompassing system quality, service quality, information quality, and peer interaction quality. The results indicated that only self-directed learning significantly influenced student satisfaction with online learning. These findings could assist decision-makers in ensuring that students effectively manage their learning processes during online learning, including time and resource allocation, goal setting, and self-assessment. In essence, students in online learning environments prefer a somewhat student-centered approach to achieving satisfaction with their learning experience. Based on these findings, universities may consider prioritizing the development of self-directed learning skills among students to enhance their satisfaction with online learning, thereby aligning with the universities’ objectives of ensuring students’ success and providing high-quality education.