The Influence of Student Learning, Student Expectation and Quality of Instructor on Student Perceived Satisfaction and Student Academic Performance: Under Online, Hybrid and Physical Classrooms

2.1 Theoretical Background

2.2 Student Learning and Student Perceived Satisfaction

Pongton and Suntrayuth (2019) has stated that organizations with more-motivated employees tend to be more productive than organizations with less-motivated workers. This is applicable for the education sector also where student satisfaction has practical significance (Bobe & Cooper, 2020) because it is linked with the outcomes of education such as student loyalty (Susilawati, Khaira, & Pratama, 2021). For example, student satisfaction is one of the tools used in higher education institutions for purposes such as the evaluation of teaching for benchmarking (Winstone, Ajjawi, Dirkx, & Boud, 2022). Student perceived satisfaction can be defined as the judgment that an educational service provides the desired level of fulfilment (Han & Sa, 2022). The relationship between student learning and satisfaction has been analysed by multiple studies (Ho, Cheong, & Weldon, 2021; Pham et al., 2019).

Azizan et al. (2022) have found that student satisfaction is shaped by different factors including learning practices. Specifically, students who perceive increased levels of learning tend to be more satisfied than those who perceive lower learning levels (Li, 2019). Prior research has shown that higher learning levels can be expected to generate higher satisfaction in traditional, online, and hybrid education modes (Saichaie, 2020). Al-Adwan et al. (2020) show that students’ perceptions of the usefulness, collaborative learning, enhanced communication, enjoyment, and ease of use of social media positively impact its utilization in their learning processes. In addition, the effect of resource sharing on the use of social media for student learning is found to be negligible. Furthermore, the use of social media positively affects students’ perceptions of their academic performance.

Scholars believe that students prefer an environment that assists learning (Zheng, Ward, & Stanulis, 2020) because this type of environment can be expected to increase perceived satisfaction. A climate that is conducive to learning promotes and supports the exchange of ideas, opinions, and information (Anthonysamy, Koo, & Hew, 2020). These factors have been observed to increase the satisfaction levels of students (Tratnik, Urh, & Jereb, 2019). Aligned with these findings, Kaufmann and Vallade (2022) have found evidence showing that building and maintaining a shared learning space is important for increasing satisfaction. Factors such as collaborative learning and learning environment shape student belief (van Leeuwen & Janssen, 2019) such as those related to satisfaction.

The nature of higher education is changing (Loton et al., 2022), and today, different systems of education are available including traditional, online, and hybrid modes (Wong, 2020; Wut et al., 2022). Importantly, variables such as student satisfaction have been observed to change across these educational systems (Moodie, 2021). Many studies have mentioned the pros of online learning, including a student-centred approach (Sandybayev, 2020). Online education is based on working in virtual classrooms which assist student–teacher interactions (Gudmundsdottir & Hatlevik, 2018). Students’ evaluation of these factors is likely to impact their perceived satisfaction (Lei, Siu, & So, 2021). Related studies have found that superiority of a learning system is a driver of student satisfaction (Pham et al., 2019). Sepulveda-escobar, Morrison, and Sepulveda-escobar (2020) have stated that a student’s style of learning plays a key role in the context of distance education. Scholars have also highlighted the benefits of hybrid education systems (Borcoman, Sorea, Nechita, & Georgeta, 2023). In these blended modes, student satisfaction has been found to be impacted by the use of effective student learning tools (Zheng, Ma, & Lin, 2021). Empirical research has showed that the learning climate is amongst the main determinants of student perceived satisfaction in a hybrid education system (Wang et al., 2019).

On the basis of this discussion, we have developed the following hypothesis:

H1a: Student learning has a significant effect on student perceived satisfaction in online education.

H1b: Student learning has a significant effect on student perceived satisfaction in hybrid education.

H1c: Student learning has a significant effect on student perceived satisfaction in traditional education.

2.3 Student Expectations and Student Perceived Satisfaction

Student expectations refer to the extent to which student predictions are met, based on the educational experience (Cicha, Rizun, Rutecka, & Strzelecki, 2021). If these expectations are met, students tend to be satisfied and vice versa (Gopal et al., 2021). Student perceived satisfaction is reached when an educational experience meets or exceeds the individual’s expectations (Eshun & Amofa, 2020). Since the evolution of the market economy, end-user satisfaction is equally important for the good and service sectors (Mohanty, Sekhar, & Shahaida, 2022). Research has been conducted to assess the relationship between expectations and perceived satisfaction amongst students (Joo et al., 2017). These include different education systems as well like traditional (Suwannaphisit et al., 2021), online (Gopal et al., 2021), and hybrid (Prifti, 2022).

Importantly, there are different types of expectations that have been identified in this context. Students expect their teachers to care about their pace of learning and offer a friendly learning environment (Factor & de Guzman, 2017). Relevant instruction, constructive feedback, and strong interpersonal relationships are also amongst these expectations (Subke et al., 2020; Taylan & Ozkan, 2021). Singh and Jasial (2021) has stated that expectations about educational services are formed in three main aspects: (1) learning and career opportunities, (2) reputations and infrastructure of institutions, and (3) availability and empathy of the staff. Mishra (2020) have observed that a potential student takes information about a higher education institution from people who have attended (or are attending) at that institution such as parents, friends, relatives, or other sources. Things such as word-of-mouth communication, personal needs, and experience with the educational service have been observed to influence student expectations (Rehman, Woyo, & Akahome, 2022). Student satisfaction is dependent on how different aspects offered by the institution effect students’ overall education and career goals (Landrum et al., 2021). Al-Adwan et al. (2021) show that various elements, such as the instructor, technical infrastructure, support services, educational frameworks, and the quality of course content, directly and positively impact student satisfaction, perceived utility, and system usage. In addition, it suggests that self-regulated learning has a negative impact on student satisfaction, perceived usefulness, and system usage.

Prifti (2022) have found that satisfaction is impacted by the expectation levels of students. Wang and Lin (2021) have conducted a relevant study and found that expectations are included in the different predictors of satisfaction in the online education model. Researchers have argued that it is important to understand what students’ expectations are because this will help educational organisations in achieving higher service quality and, through this, student perceived satisfaction (Demir & Ali, 2021). Today, educational institutions must take student satisfaction into consideration because there is intense competition amongst these service providers, higher expectations of students, and increasing tuition fees (Hassel & Ridout, 2018; Richards, 2019).

Based on this discussion, we have developed the following hypothesis:

H2a: Student expectations have a significant effect on student perceived satisfaction in online education.

H2b: Student expectations have a significant effect on student perceived satisfaction in hybrid education.

H2c: Student expectations have a significant effect on student perceived satisfaction in traditional education.

2.4 Quality of Instruction and Student Perceived Satisfaction

Quality of instruction represents teachers’ observable behaviours and can be defined using different dimensions such as classroom management and cognitive activation (Praetorius et al., 2018). The models of instruction quality can be defined as efficient management of classroom along with the effective use of allotted time and clarity about rules of the institution (Praetorius et al., 2018). Cognitive activation can be defined as whether students are challenged to think analytically through different teaching strategies (Teig, Scherer, & Nilsen, 2019). In a related study by Burić and Kim (2020), classroom management and cognitive activation are shown to positively influence student learning outcomes.

Quality of instruction is a strong antecedent of student perceived satisfaction (Sriyalatha & Appuhamilage, 2019). A related study has showed that teaching skills and competence have meaningful effects on student satisfaction (Garnjost & Lawter, 2019). Aghaei, Shahbazi, Pirbabaei, and Beyti (2023) and Singh and Jasial (2021) have assessed different dimensions of academic experience and concluded that teacher competence is the key determinant of the students’ perceived satisfaction along with the curriculum. Shirazi (2017) has stated that a teacher’s subject expertise and interaction with students impacts student perceived satisfaction.

Research has shown the role of instruction quality in generating student satisfaction (Chen, Liu, & Zheng, 2022). Multiple studies have shown that higher quality of instruction positively affects student outcomes like satisfaction (Kelcey et al., 2019). This is because student outcomes are viewed as appropriate measures of instructional quality (Blömeke et al., 2022). Importantly, research on student perceived satisfaction is relevant for different education modes such as online, traditional, and hybrid modes. Therefore, it is important to improve the quality of instruction and endeavour for high standards in teaching strategies (Asiyai, 2022). Al-Adwan, Nofal, Akram, Albelbisi, and Al-Okaily (2022) show that the quality of the format and design of learning content is acknowledged as a crucial element for the success of e-learning. Therefore, both instructors and e-learning developers need to ensure that the learning materials they provide are reliable, accurate, and consistently updated.

Based on this discussion, we have developed the following hypothesis:

H3a: Quality of instruction has a significant effect on student perceived satisfaction in online education.

H3b: Quality of instruction has a significant effect on student perceived satisfaction in hybrid education.

H3c: Quality of instruction has a significant effect on student perceived satisfaction in traditional education.

2.5 Student Perceived Satisfaction and Student Academic Performance

Relevant studies have measured student academic performance through variables such as grade point averages and standardised test scores (Brandt et al., 2020; Liu et al., 2018). Since academic performance shows the quality of an educational service, it is an important area for research (Ulum, 2022). Therefore, the significant roles of factors such as quality of academic staff and the learning environment (Cayubit, 2022) in shaping academic performance are underlined in education literature. In multiple studies that have analysed the influence of student perceived satisfaction on elements of academic performance, findings showing that more satisfied students perform better in terms of academics (Bossman & Agyei, 2022). This implies a positive relationship between students’ perceived satisfaction and student academic performance (Li & Carroll, 2020). Importantly, student perceived satisfaction and academic performance are amongst the variables highlighted by both scholars and policy makers in the current competitive academic environment (Kostagiolas et al., 2019).

Student perceived satisfaction related to educational experience is a central variable in different theories and frameworks of education including those of student academic performance (Bean, 1980; Bean & Bradley, 1986; Pascarella & Terenzini, 1976; Spady, 1970). Related research has suggested that student perceived satisfaction can have a strong influence on performance elements such as grades and mastery (Kazanidis et al., 2019). Satisfaction with an academic experience can impact performance through increased motivation and academically important behaviours (Yoo, Marshall, & Marshall, 2022) such as attending classes regularly. Multiple studies have revealed that students work on improving their academic performance when they are extrinsically motivated (Dökme, Açıksöz, & Ünlü, 2022; Feraco, Resnati, Fregonese, Spoto, & Meneghetti, 2023). This means that student satisfaction can increase extrinsic motivation of student and ultimately academic performance (Diaz-Carrion & Franco-Leal, 2022). Therefore, research has shown that factors such as satisfaction can take the role of an essential mechanism for improving student academic performance (Kostagiolas et al., 2019).

Moreover, there are multiple spectrums that act as an enabler for student perceived satisfaction and academic performance. It has been observed that student satisfaction can be dependent on optimism about rewards about in the future, such as good recruitment chances (Kim, Hood, Creed, & Bath, 2023). Therefore, if students expect certain rewards, they will experience more satisfaction in their educational institution and will make efforts for higher academic performance. For many students, studying does not only represent acquiring of knowledge or skills (Gallarza et al., 2017) but also related to personal development and social progress, both of which can have positive effects on student perceived satisfaction and academic performance (Marginson, 2018).

Based on this discussion, we have developed the following hypothesis:

H4a: Student perceived satisfaction has a significant effect on student academic performance in online education.

H4b: Student perceived satisfaction has a significant effect on student academic performance in hybrid education.

H4c: Student perceived satisfaction has a significant effect on student academic performance in traditional education.

3.1 Research Procedure and Samples

The data were collected under three different time frames and modes of instruction namely online, hybrid, and traditional (on the campus) classes. Due to COVID-19 pandemic, unlike many other sectors, the education sector remained operational. However, the mode of education transmission indeed evolved under the baseline conditions. By November 2019, China experienced the outset of COVID-19 cases and its rapid spread in significantly a smaller number of days resulted in complete shutdown of the educational sector. However, The Ministry of Education of the People’s Republic of China, public, and private educational organizations took the immediate action by designing new methods for education transmission by taking into consideration health and safety as a priority. By early February 2020, most of the private educational organizations resumed their online educational services. It was a new normal for all the involved stakeholders such as the ministry of education, public and private educational institutes, and students.

The students at OEC of Jiangsu University make up the study sample. Jiangsu University was selected for research because the university administration gave permission to collect data and clear any confusion that respondents might have while filling the survey. Moreover, Jiangsu University consists of a large population of foreign students which ensure a representative sample. The questionnaire was self-administered, and it was designed in English language because the foreign students were more comfortable with English language. Before, the distribution of questionnaire a pilot test was conducted for 10 students, and based on their recommendation, changes were made to the survey. It has a total of 4,000 registered students for the year 2020–2024, of which 3,500 undergraduates were the targeted population. This research adopted the random sampling technique to collect the data to avoid inconvenience and likewise biasness (reference). Random sampling technique allows room for data collection and makes it convenient to generalize results because study sample were students at OEC (Yadav, Thakur, & Pareek, 2024). These students belong to different ethnicities which make this sample a strong representative of other colleges around the world, as this sample has diverse characteristics. For this study’s purpose, email addresses of students were obtained from university administration after which the study description and intentions were shared with second- and third-year undergraduate students enrolled in multiple majors offered by the university. Once the students provided their consent, a Google form link and WeChat link was shared with them, and to enhance precision, we utilized Eqxiu.com for gathering data because it integrates effectively with WeChat, which is widely used in China. This platform has also been employed for survey analysis in prior research (Hartescu & Morgan, 2019). Students’ participation was voluntary, and anonymity of responses was promised. The questionnaire was not translated because the target population were the foreign students studying in the Overseas College of Jiangsu University. Figure 2 shows the research flow and rounds of data collection and analysis.

Figure 2

Research flow.

To ensure accuracy, this research applied (Hair et al., 2021) Cohen’s theory to evaluate the adequacy of the sample size at each time period. Moreover, a g* power post hoc test was employed to assess the statistical robustness of the sample concerning exogenous factors. The results indicated that the strength surpassed the minimum required threshold of 0.8.

The total of 600 undergraduate students from the second or third year responded when the classes were completely online of which 346 responses were completely filled. Therefore, the sample size during online classes was 346 students, and the response rate was 58%. For the online system, sessions were conducted on Tencent meeting app which is similar to Zoom and the recorded lectures, as well as relevant class materials were uploaded by the faculty on the university portal, which can be easily accessed by the students.

The second round of data collection was conducted when the COVID-19 spread experienced a decelerating trend and universities got permission from higher authorities to resume their operations under certain conditions. By August 2021, the universities got the permission to partially resume their operations if their students, faculty, and administrative staff were fully vaccinated. Also, OEC started off with a hybrid model of education transmission with certain conditions. First, only fully vaccinated individuals were allowed to enter campus. Second, wearing a mask was mandatory. Third, only students from Zhenjiang were allowed to come to campus and the rest were required to take their classes online, and each class was divided as Group A and Group B. Faculty was delivering lectures on the campus with some students sitting in class with 10-m social distance and some students taking the class online simultaneously. Finally, if any individual experienced COVID-19 symptoms, immediate rapid testing was recommended with all the points of contact in the past 1 week and they were duly informed. If the virus got transmitted in class, then the entire class again resumed to the online mode; however, in case of an exam, it would get delayed till a further date. Moreover, there would be no marked reduction in exams later if the student provided proof of illness. It is also important to mention that OEC heavily invested in digital technologies by installing state of the art microphones, speakers, cameras, and high-speed Internet for smooth and effective transmission of lecture delivery. During the hybrid mode, the recorded lectures were not mandatory to be uploaded by the faculty on the university portal, but other relevant class materials were uploaded online which could be easily accessed by the students. Under the hybrid model, we collected data by the end of November 2021. The total of 550 undergraduate students from the second or third year responded when the classes were performed using a hybrid mode out of which 328 responses were filled. Therefore, the sample size during hybrid classes was 328 students and the response rate was 60%.

The third round of data collection was performed when most of the COVID-19 restrictions were relaxed. By January 2023, both the ministry of education and educational institutes decided to resume their education operations at full capacity. However, the preventive measures such as masks and vaccination were still intact. The total of 580 undergraduate students from the second or third year responded when the classes were on campus out of which 442 responses were filled. Therefore, the sample size during on campus classes was 442 students and the response rate was 76%.

3.2 Research Instruments

The questionnaire covered demographic factors and the model variables. A five-point Likert scale was used for all the items in the model. Quality of instruction was measured using seven items (Bangert, 2004) which included statement such as “The teacher conveyed relevant material efficiently” and “The teacher displayed enthusiasm during online teaching.” Student expectations were measured using five items such as “The teacher provided samples that efficiently communicated expectations for assigned group work” (Bangert, 2004; Wilson et al., 1997). For student perceived satisfaction, we selected six items (Bangert, 2004; Wilson et al., 1997; Yin & Wang, 2015). For student academic performance, we selected six items (Wilson et al., 1997). Student learning was measured using five items (Ballouk et al., 2022). Table 1 presents demographic information for the respondents at each research phase. All hypotheses were tested by using structural equation modelling technique. Partial least squares structural equation modelling was conducted (partial least squares structural equation modelling) using SmartPLS v3 software (Sarstedt et al., 2019).

4.1 The Online Model

Table 2 summarizes the findings from the analysis of findings obtained from the data collected on the online mode of teaching. Values for construct reliability and composite reliability of all model constructs are higher than 0.7, which makes them acceptable (Esposito, Napoli, Di Martino, Di Arcidiacono, & Prilleltensky, 2022). To evaluate convergent validity, the values for average variance extracted (AVE) values have been calculated. These values are satisfactory because they are above 0.5 (Henseler, Hubona, & Ray, 2016). Variance inflation factor (VIF) has been computed to assess the presence of multicollinearity, the threshold for multicollinearity is that value for VIF should be less than 5 or 10. is satisfactory if it does not exceed 5 (Gokmen, Dagalp, & Kilickaplan, 2022) the VIF values are less than 5, showing that there is no multicollinearity. Fornell Lacker criterion and Hetero Monotrait (HTMT) ratios have been used to evaluate discriminant validity. For discriminant validity of a latent construct, the square root of AVE should be above correlations with other constructs according to the Fornell Lacker criterion (Hilkenmeier, Bohndick, Bohndick, & Hilkenmeier, 2020) and HTMT values must be less than 0.85 (Henseler et al., 2016). Both requirements have been met by this model. It is important to check for common method bias (CMB) in the selected items because if any one factor explains more than 50% variation then this indicates a presence of CMB, which can yield misleading results. To detect CMB, Harman’s one-factor test has been carried out, all factors combined show a variance of 33.867%. This means that CMB does not exist (Figure 3; Tables 3 and 4).

Figure 3

Measurement model for online teaching.

Bootstrapping techniques have been applied to test all the hypothesis and standardized root-mean-square residual (SRMSR) values are used to evaluate fitness of the model, which is 0.056, as this value is less than the threshold value of 0.08, and this means that model is well designed (Pavlov, Maydeu-olivares, & Shi, 2021). R ² values are 0.152 and 0.201 showing that this model explains 15.2 and 20.1% of variation in student academic performance and student perceived satisfaction, respectively. Student learning has a significantly positive influence on student perceived satisfaction, which supports H1. The values for coefficients and P-values are given in Table 5. Student expectations have a significantly positive effect on student perceived satisfaction supporting H2. Quality of instruction has a significantly positive influence on student perceived satisfaction providing support for H3. Student perceived satisfaction has a significantly positive influence on student academic performance supporting H4 (Figure 4).

Figure 4

Structural model for online teaching.

4.2 The Hybrid Model

Table 6 summarizes the results obtained from measurement of hybrid mode of instruction. Construct reliability and composite reliability values are higher than 0.70 so are acceptable (Esposito et al., 2022). The AVE values are satisfactory as they are not below 0.5 (Henseler et al., 2016). This shows the existence of convergent validity. All constructs have a VIF value of less than 5, which means there is no multicollinearity (Gokmen et al., 2022). For every latent construct, the square root of AVE is above correlations with other constructs (Hilkenmeier et al., 2020), while values for HTMT values show that discriminant validity exists as all figures are less than 0.85 (Henseler et al., 2016). To further check CMB, Harman’s one-factor test has been carried out, and all factors combined show a variance of 31.123%. This means that CMB does not exist (Figure 5; Tables 7 and 8).

Figure 5

Measurement model for hybrid teaching.

The SRMSR value is 0.044, which validates the model’s overall fitness (Henseler et al., 2016). R ² values are 0.062 and 0.093 showing that this model explains 6.2 and 9.3% of variation in student academic performance and student perceived satisfaction, respectively. Student learning has a positive significant influence on student perceived satisfaction which supports H1b. Student expectations have a positively significant impact on student perceived satisfaction supporting H2b. Student perceived satisfaction has a positively significant effect on student academic performance, which supports H4b. Quality of instruction does not have a significant effect on student perceived satisfaction in this model. All the values are present in Table 9 (Figure 6).

Figure 6

Structural model for hybrid teaching.

4.3 The Traditional (on-campus) Model

Construct reliability and composite reliability values are above 0.70 so are acceptable (Esposito et al., 2022). The AVE values show that these constructs have convergent validity as they are not below 0.5 (Henseler et al., 2016). The VIF values for constructs are less than 10, showing that there is no problem of multicollinearity. For every construct, the square root of AVE is above correlations with other constructs (Hilkenmeier et al., 2020), and HTMT values are less than 0.85 (Purwanto & Sudargini, 2021). This means the constructs possess discriminant validity. To further counter check CMB, Harman’s one-factor test has been carried out, and all factors combined show a variance of 28.526%. This means that CMB does not exist (Figure 7; Tables 10–12).

Figure 7

Measurement model for traditional mode of teaching.

This model is well designed because the SRMSR value is 0.7 (Henseler et al., 2016). R ² values are 0.023 and 0.232 showing that this model explains 2.3 and 23.2% of variation in student academic performance and student perceived satisfaction, respectively. Bootstrapping has been used for testing the hypotheses. Student learning has a positively significant effect on student perceived satisfaction, which supports H1c. Student expectations have a positively significant influence on student perceived satisfaction, which supports H2c. Quality of instruction has a positively significant effect on student perceived satisfaction, supporting H3c. Student perceived satisfaction does not have a significant effect on student academic performance in this model. All the values are presented in Table 13 (Figure 8).

Figure 8

Structural model for traditional mode of teaching.

6.1 Theoretical Implications

The findings of the study carry significant implications for existing theories of education research. Achievement goal theory stresses the importance of goal setting and student learning, which eventually leads to improved academic performance. Results of this study provide support to this theory by showing the positive association between student learning and academic performance. Moreover, findings also support the theory of expectation disconfirmation by showing that student expectations play a significant role in determining their satisfaction for learning, which can be seen through improved academic performance. This research has advanced the literature by looking three different modes of teaching using multiple indicators of perceived satisfaction of students. Moreover, unlike previous studies, this research has used the special circumstances provided by COVID-19 to examine same group of students under various scenarios.

6.2 Practical Implications

7.1 Conclusion

The goal of this study was to analyse relationships between student learning, student expectations, quality of instruction, student perceived satisfaction, and student academic performance. This assessment was conducted in the context of higher education in China. Data were obtained from undergraduate students at a university in the online, hybrid, and traditional (on-campus) education modes. SmartPLS 3 was used to conduct structural equation modelling. Some important results have been obtained. First, student learning has a meaningful impact on student perceived satisfaction in all contexts. Second, student expectations also have a positive impact on student perceived satisfaction in the three education modes. Third, the quality of instruction has a positive effect on student perceived satisfaction in online and traditional (on-campus) education. Fourth, student perceived satisfaction has a positive effect on student academic performance in online and hybrid education.

This article has made multiple contributions. The current work can help to better understand quality of instruction in online education in developing countries (Dursun et al., 2018; Martinez-Arguelles et al., 2013). Studies about student perceived satisfaction have generated mixed results, so this research has increased clarity (Landrum et al., 2021). This study has contributed to research in the higher education sector where a paucity of related studies has been observed (Abeysekera & Dawson, 2015; Estes et al., 2015).

7.2 Limitations

This study has focused on just one university of one country due to which there might be problem in generalizing the findings in other settings. In addition, this research has not tested any mediating factors such as personality traits, which might influence relationships between variables. Moreover, this research used simple structural equation modelling to test relationships and compare mediums of instruction and did not opt for sophisticated econometric techniques such as difference and difference approach to compare all teaching modes. This study could have used other econometric approaches.

7.3 Directions for Future Research

Scholars should assess this model’s variables and their relationships in a different sample (other institutions and countries). This approach is likely to generate useful and interesting findings. For example, the model can be analysed in the context of school education. Furthermore, comparative analysis can be conducted to obtain useful results such as private and public sector institutions for improved understanding of this topic. Moreover, COVID-19 was a force that affected every sector and not just higher education. Therefore, studies should be conducted in other sectors also. For example, employee’s performance can be analysed for work from home and traditional and hybrid modes of jobs. Fourth, the model can be expanded by adding other related variables such as the role of personality factors (such as the Big Five traits) and can be analysed for the relationship between student perceived satisfaction and performance (as mediators). Furthermore, future researchers can use sophisticated techniques such as difference and difference approach to compare different mediums of instruction.