Online learning, a most significant aspect of education informatization development, has become the focus of attention in the field of education in the recent decade for its advantages of not being constrained by time, geographical location and other factors. In particular, COVID-19 swept the whole globe in 2020, posing unprecedented challenges politically, administratively, economically as well as pedagogically to the countries worldwide (Huang et al., 2020; Tan et al., 2021; Tlili et al., 2021). To effectively control and prevent the spreading of COVID-19, countries around the globe have been using online platforms to carry out online teaching and learning (Miller, 2020; Choi and Chung, 2021; Sobaih et al., 2021; Wlodarczyk et al., 2021). Policies like “suspending classes, ongoing learning” in China have been advanced and implemented, and online learning has been incorporated into every subject across almost all the school sectors globally, which has already made online learning an inevitable and irreversible trend in global education development (Cen et al., 2020). With this trend, an increasing diversity of online learning platforms have been adopted to facilitate online learning (Su and Chen, 2020). A large number of studies show that online learning can provide many positive learning experiences to online learners (Arbaugh, 2014; Eom et al., 2016; Li et al., 2016b,2017) and that learners are more satisfied with online learning than traditional face-to-face learning (Morton et al., 2016; Dooley et al., 2018; Green et al., 2018; Riddle and Gier, 2019). However, some studies show that online learning is not as satisfying as face-to-face learning and that online learners have poorer engagement with online learning (Pickering and Swinnerton, 2019). The possible factors accounting for poorer engagement and lower satisfaction are poor course design and poor pedagogy in online learning (Woodworth et al., 2015). The different arguments among scholars necessitates a more comprehensive, systematic and in-depth study of online learning satisfaction (OLS), which is of vital significance to enhance the service quality of online learning courses and perfect the online teaching quality evaluation system.

The term of online learning has been used in the field of education since the 1990s. It was first proposed to refer to placing some course materials on the computer networks to form a virtual learning community to achieve a face-to-face learning (Hiltz, 1999). Online learning is also known by some alternative terms like e-learning, blended learning, virtual learning, remote education, online education, web-based education, web-based instruction and online courses (Singh and Thurman, 2019), and there are some subtle differences in terminology not obvious for non-professionals to notice (Lee, 2017; Singh and Thurman, 2019). This article defines online learning to be anything from uploading learning materials onto some online learning platform to teaching and learning live through a diversity of software applications which facilitate “the bridging of the space between the teacher and the student through the use of web-based technologies” (Singh and Thurman, 2019, p.293).

Based on reviews of the literature on students’ satisfaction with online learning, this paper summarizes factors influencing OLS, and presents the prospect of future research on OLS.

The concept of satisfaction has been long studied in the field of psychology (Myerson, 1943) and gradually expanded to other fields. Cardozo (1965) introduced customer satisfaction into the marketing field for the first time, which attracted great attention. Satisfaction was defined as the degree of pleasure felt by individuals, derived from their perceptions of product functions and their expectations for products (Kotler, 1997). Symonds (1955) explored what the field of education could learn from the field of psychology and learner satisfaction was mentioned in the discussion (Symonds, 1955). Since then, learning satisfaction has drawn great attention of researchers with different backgrounds, and different definitions of learning satisfaction have been provided. Learning satisfaction was defined as a feeling or attitude of learners that their desires and needs can be fulfilled in learning activities or processes (Houle, 1961; Long, 1989; Sanchez-Franco, 2009; Topala and Tomozii, 2014), a subjective psychological state formed after the comparison between the learner’s learning expectation and the actual perceived learning effect (Fernandes et al., 2013; Jiang et al., 2017a), and the learners’ evaluation of their satisfaction with teaching mode, course content, learning environment and other elements, as well as their learning state and learning effect (Xu, 2018). Based on the above definitions, this review paper holds the opinion that learning satisfaction is the feeling or attitude of learners toward learning activities, which directly reflects the degree to which learners’ expectations are fulfilled during the learning processes.

In order to monitor and improve the teaching and learning experiences, an increasingly large number of institutions and researchers have been employing student evaluation instruments to measure learners’ satisfaction levels (Arbaugh, 2014; Rienties, 2014; Asoodar et al., 2016; Bahati et al., 2019; Rajabalee and Santally, 2021). For example, the American Council on Education applied Cooperative Institutional Research Program in 1966 to measure the satisfaction of freshmen. Nowadays, a majority of institutions in the United States and the United Kingdom periodically collect learning satisfaction and academic performance data systematically (Baldwin and Blattner, 2003; Kember and Ginns, 2012; Rienties, 2014). And a number of scales targeted at learning satisfaction have been designed, among which the representative ones are the Students’ Evaluations of Educational Quality Questionnaire (Marsh, 1982), the Course Experience Questionnaire (Ramsden, 1991), the National Student Survey (Ashby et al., 2011; Callender et al., 2014), the Qualtrics Survey (Van Wart et al., 2020), and Performance Evaluation Matrix based on a fuzzy linguistic scale (Yu et al., 2018). These scales lay a foundation for the study on (online) learning satisfaction.

Online learning satisfaction refers to evaluation opinions and feeling experiences of learners toward the quality of online learning service provided by online learning providers, which is a cumulative psychological response to online learning contents and learning environment, formed after a rational and emotional comparison between the actual perceived online learning effect and expectations of the perception (Yao et al., 2016). Currently, OLS has become a focus of research drawing much attention (Bair and Bair, 2011; Ramayah and Lee, 2012; Ladyshewsky, 2013; Richardson et al., 2017; Alqurashi, 2019; Wang et al., 2019; Lin et al., 2020; Xiao and Li, 2021), especially after the spreading of COVID-19, among which the factors influencing OLS are the most hotly discussed (Hew et al., 2020; Zhang et al., 2020; Jiang et al., 2021; Zeng and Wang, 2021; Schroedler et al., 2022). Some of the factors of significance include the role of online educator (An et al., 2009; Costley and Lange, 2016), online interaction between teacher and learner (Baker, 2010; Kuo et al., 2014), perceived usefulness of online learning course (Liaw, 2008; Liu et al., 2015), online learning content (Kranzow, 2013), the role of platform technology (Dinh and Nguyen, 2020), learner’s motivation and efficacy (Artino, 2007; Alqurashi, 2019), online learning environment (Piccoli et al., 2001; Alqurashi, 2019) as well as assessment and evaluation systems (Dinh and Nguyen, 2020).

In this mini review paper, previous studies on OLS were searched as follows. Firstly, studies published in international journals were searched in electronic databases of Web of Science, Elsevier, and Wiley Online Library. Take Web of Science for example. The author used the following searching parameters to conduct the search for previous studies: TI = (satisfaction) AND TI = (online learning OR online course OR online education OR remote education OR e-learning OR distance learning OR virtual learning OR distance education OR remote education OR blended learning OR web-based learning OR web-based education OR web-based instruction) AND SILOID = (WOS) AND PY = (2012–2022). Only studies exploring factors influencing OLS were extracted manually. Secondly, studies published in Chinese journals were searched in China National Knowledge Infrastructure (CNKI) database. The author used the Chinese counterparts of the searching parameters mentioned above and extracted the studies on OLS published in Chinese Social Sciences Citation Index (CSSCI) source journals.

As a result, 109 papers were found in Web of Science, 82 in Elsevier, 26 in Wiley Online Library, and 16 in CNKI. Among the 233 papers, 8 studies were published in 2012, 27 in 2013, 22 in 2014, 10 in 2015, 17 in 2016, 24 in 2017, 12 in 2018, 19 in 2019, 31 in 2020, 51 in 2021, and 12 in 2022. Based on a careful reading and analysis of the papers, this mini review summarizes the factors influencing OLS under four headings, namely, online learner factors (explored by 95 papers), online instructor factors (explored by 58 papers), online platform factors (explored by 74 papers), and online instructional design factors (explored by 85 papers).

Previous studies show that factors related to teachers and teaching are the most significant factors influencing OLS while factors related to learners are the least significant. However, previous empirical research on OLS collected data overwhelmingly through a combination of relatively smaller scale questionnaires and interviews, which may influence the validity of the research findings. To have a full and deeper understanding of OLS, an increasing number of large(r) scale studies are suggested to be conducted in future, with reference to some pioneering studies using large scale data (Li et al., 2016b,2017; Langan and Harris, 2019; Sutherland et al., 2019). It is also suggested that future studies might be integrated with techniques applied in cognitive neuropsychology, such as magnetoencephalography, electroencephalography and eye tracking technology, to obtain more scientific research conclusions.

In addition, most studies were carried out in a horizontal paradigm, that is, students’ OLS in a certain period was taken as the research object, and a scarcity of longitudinal studies were conducted on dynamic tracking investigation of OLS (Li et al., 2017). Since OLS is a dynamic value that changes over time and reflects the value of a continuous process (Li et al., 2017), longitudinal research from the developmental and ecological perspectives is suggested to be conducted on OLS to have a better and deeper knowledge of dynamicity of OLS and its relationship with a diversity of factors influencing OLS.

A large bulk of studies on OLS were focused on college students and college courses, with a few studies exploring OLS of primary school students and middle school students (Zhang et al., 2020). However, the booming development of online learning and the concept of lifelong learning attract people from all walks of life to participate in online courses, which makes online learning environment increasingly complicated. Different learners have different motivations for online learning, different expectation of support from online instructors, different perception of usefulness and convenience of online courses, and different levels of OLS. Therefore, the research subjects of future research should be expanded to focus on online learners from all social strata.

Research on OLS involves almost all aspects of online learning environment, but the previous studies were mostly focused on one or several factors that affect OLS (Baber, 2020), and ignored several factors like culture and context (Rubin and Fernandes, 2013). And since OLS is the result of the interaction of multiple factors, future research should be to explore the interrelationship of various factors, including culture and context, and the mechanism behind their interrelationship (She et al., 2021). Besides, previous research only focused on one type of online learning context, such as computers (Bhargava et al., 2021) and mobile phones (Xiao, 2021). Nevertheless, the fast development of technology has given creation to a diversity of digital media contexts (Kim et al., 2019). It is worth conducting research on OLS in different digital media contexts in future.

Finally, for the reason that the satisfaction degree is affected by a variety of factors, the results of OLS survey will inevitably show that some learners are satisfied while some are not, but there were few empirical studies concerned with intervention of OLS. Therefore, based on prior online satisfaction survey, future research should be conducted to find out the key factors leading to the variations in OLS of different learners, to develop online learning platform with adaptive technology and provide different learners with personalized and accurate online learning environments, and explore how learning design changes can enhance OLS of students (Li et al., 2016b).

After elucidating some terms related to OLS, this mini review paper reviews and analyzes the research status of factors influencing OLS from the perspectives of learners, instructors, platforms and instructional design. It is suggested that large(r) scale studies be conducted to explore the diversity of factors influencing OLS of learners from all social strata and interrelationship of various factors, with techniques applied in cognitive neuropsychology. It is also suggested that longitudinal research be conducted on OLS to reveal the dynamicity of OLS, providing enlightenments on how to enhance online learning efficacy through intervention of OLS.

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The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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