Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19

The aim of the study is to identify the factors affecting students’ satisfaction and performance regarding online classes during the pandemic period of COVID–19 and to establish the relationship between these variables. The study is quantitative in nature, and the data were collected from 544 respondents through online survey who were studying the business management (B.B.A or M.B.A) or hotel management courses in Indian universities. Structural equation modeling was used to analyze the proposed hypotheses. The results show that four independent factors used in the study viz. quality of instructor, course design, prompt feedback, and expectation of students positively impact students’ satisfaction and further student’s satisfaction positively impact students’ performance. For educational management, these four factors are essential to have a high level of satisfaction and performance for online courses. This study is being conducted during the epidemic period of COVID- 19 to check the effect of online teaching on students’ performance.

Coronavirus is a group of viruses that is the main root of diseases like cough, cold, sneezing, fever, and some respiratory symptoms (WHO, 2019). Coronavirus is a contagious disease, which is spreading very fast amongst the human beings. COVID-19 is a new sprain which was originated in Wuhan, China, in December 2019. Coronavirus circulates in animals, but some of these viruses can transmit between animals and humans (Perlman & Mclntosh, 2020). As of March 282,020, according to the MoHFW, a total of 909 confirmed COVID-19 cases (862 Indians and 47 foreign nationals) had been reported in India (Centers for Disease Control and Prevention, 2020). Officially, no vaccine or medicine is evaluated to cure the spread of COVID-19 (Yu et al., 2020). The influence of the COVID-19 pandemic on the education system leads to schools and colleges’ widespread closures worldwide. On March 24, India declared a country-wide lockdown of schools and colleges (NDTV, 2020) for preventing the transmission of the coronavirus amongst the students (Bayham & Fenichel, 2020). School closures in response to the COVID-19 pandemic have shed light on several issues affecting access to education. COVID-19 is soaring due to which the huge number of children, adults, and youths cannot attend schools and colleges (UNESCO, 2020). Lah and Botelho (2012) contended that the effect of school closing on students’ performance is hazy.

Similarly, school closing may also affect students because of disruption of teacher and students’ networks, leading to poor performance. Bridge (2020) reported that schools and colleges are moving towards educational technologies for student learning to avoid a strain during the pandemic season. Hence, the present study’s objective is to develop and test a conceptual model of student’s satisfaction pertaining to online teaching during COVID-19, where both students and teachers have no other option than to use the online platform uninterrupted learning and teaching.

UNESCO recommends distance learning programs and open educational applications during school closure caused by COVID-19 so that schools and teachers use to teach their pupils and bound the interruption of education. Therefore, many institutes go for the online classes (Shehzadi et al., 2020).

As a versatile platform for learning and teaching processes, the E-learning framework has been increasingly used (Salloum & Shaalan, 2018). E-learning is defined as a new paradigm of online learning based on information technology (Moore et al., 2011). In contrast to traditional learning academics, educators, and other practitioners are eager to know how e-learning can produce better outcomes and academic achievements. Only by analyzing student satisfaction and their performance can the answer be sought.

Many comparative studies have been carried out to prove the point to explore whether face-to-face or traditional teaching methods are more productive or whether online or hybrid learning is better (Lockman & Schirmer, 2020; Pei & Wu, 2019; González-Gómez et al., 2016; González-Gómez et al., 2016). Results of the studies show that the students perform much better in online learning than in traditional learning. Henriksen et al. (2020) highlighted the problems faced by educators while shifting from offline to online mode of teaching. In the past, several research studies had been carried out on online learning to explore student satisfaction, acceptance of e-learning, distance learning success factors, and learning efficiency (Sher, 2009; Lee, 2014; Yen et al., 2018). However, scant amount of literature is available on the factors that affect the students’ satisfaction and performance in online classes during the pandemic of Covid-19 (Rajabalee & Santally, 2020). In the present study, the authors proposed that course design, quality of the instructor, prompt feedback, and students’ expectations are the four prominent determinants of learning outcome and satisfaction of the students during online classes (Lee, 2014).

The Course Design refers to curriculum knowledge, program organization, instructional goals, and course structure (Wright, 2003). If well planned, course design increasing the satisfaction of pupils with the system (Almaiah & Alyoussef, 2019). Mtebe and Raisamo (2014) proposed that effective course design will help in improving the performance through learners knowledge and skills (Khan & Yildiz, 2020; Mohammed et al., 2020). However, if the course is not designed effectively then it might lead to low usage of e-learning platforms by the teachers and students (Almaiah & Almulhem, 2018). On the other hand, if the course is designed effectively then it will lead to higher acceptance of e-learning system by the students and their performance also increases (Mtebe & Raisamo, 2014). Hence, to prepare these courses for online learning, many instructors who are teaching blended courses for the first time are likely to require a complete overhaul of their courses (Bersin, 2004; Ho et al., 2006).

The second-factor, Instructor Quality, plays an essential role in affecting the students’ satisfaction in online classes. Instructor quality refers to a professional who understands the students’ educational needs, has unique teaching skills, and understands how to meet the students’ learning needs (Luekens et al., 2004). Marsh (1987) developed five instruments for measuring the instructor’s quality, in which the main method was Students’ Evaluation of Educational Quality (SEEQ), which delineated the instructor’s quality. SEEQ is considered one of the methods most commonly used and embraced unanimously (Grammatikopoulos et al., 2014). SEEQ was a very useful method of feedback by students to measure the instructor’s quality (Marsh, 1987).

The third factor that improves the student’s satisfaction level is prompt feedback (Kinicki et al., 2004). Feedback is defined as information given by lecturers and tutors about the performance of students. Within this context, feedback is a “consequence of performance” (Hattie & Timperley, 2007, p. 81). In education, “prompt feedback can be described as knowing what you know and what you do not related to learning” (Simsek et al., 2017, p.334). Christensen (2014) studied linking feedback to performance and introduced the positivity ratio concept, which is a mechanism that plays an important role in finding out the performance through feedback. It has been found that prompt feedback helps in developing a strong linkage between faculty and students which ultimately leads to better learning outcomes (Simsek et al., 2017; Chang, 2011).

The fourth factor is students’ expectation. Appleton-Knapp and Krentler (2006) measured the impact of student’s expectations on their performance. They pin pointed that the student expectation is important. When the expectations of the students are achieved then it lead to the higher satisfaction level of the student (Bates & Kaye, 2014). These findings were backed by previous research model “Student Satisfaction Index Model” (Zhang et al., 2008). However, when the expectations are students is not fulfilled then it might lead to lower leaning and satisfaction with the course. Student satisfaction is defined as students’ ability to compare the desired benefit with the observed effect of a particular product or service (Budur et al., 2019). Students’ whose grade expectation is high will show high satisfaction instead of those facing lower grade expectations.

The scrutiny of the literature show that although different researchers have examined the factors affecting student satisfaction but none of the study has examined the effect of course design, quality of the instructor, prompt feedback, and students’ expectations on students’ satisfaction with online classes during the pandemic period of Covid-19. Therefore, this study tries to explore the factors that affect students’ satisfaction and performance regarding online classes during the pandemic period of COVID–19. As the pandemic compelled educational institutions to move online with which they were not acquainted, including teachers and learners. The students were not mentally prepared for such a shift. Therefore, this research will be examined to understand what factors affect students and how students perceived these changes which are reflected through their satisfaction level.

This paper is structured as follows: The second section provides a description of theoretical framework and the linkage among different research variables and accordingly different research hypotheses were framed. The third section deals with the research methodology of the paper as per APA guideline. The outcomes and corresponding results of the empirical analysis are then discussed. Lastly, the paper concludes with a discussion and proposes implications for future studies.

Achievement goal theory (AGT) is commonly used to understand the student’s performance, and it is proposed by four scholars Carole Ames, Carol Dweck, Martin Maehr, and John Nicholls in the late 1970s (Elliot, 2005). Elliott & Dweck (1988, p11) define that “an achievement goal involves a program of cognitive processes that have cognitive, affective and behavioral consequence”. This theory suggests that students’ motivation and achievement-related behaviors can be easily understood by the purpose and the reasons they adopted while they are engaged in the learning activities (Dweck & Leggett, 1988; Ames, 1992; Urdan, 1997). Some of the studies believe that there are four approaches to achieve a goal, i.e., mastery-approach, mastery avoidance, performance approach, and performance-avoidance (Pintrich, 1999; Elliot & McGregor, 2001; Schwinger & Stiensmeier-Pelster, 2011, Hansen & Ringdal, 2018; Mouratidis et al., 2018). The environment also affects the performance of students (Ames & Archer, 1988). Traditionally, classroom teaching is an effective method to achieve the goal (Ames & Archer, 1988; Ames, 1992; Clayton et al., 2010) however in the modern era, the internet-based teaching is also one of the effective tools to deliver lectures, and web-based applications are becoming modern classrooms (Azlan et al., 2020). Hence, following section discuss about the relationship between different independent variables and dependent variables (Fig. 1).

Proposed Model

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Quality of the instructor and satisfaction of the students

Quality of instructor with high fanaticism on student’s learning has a positive impact on their satisfaction. Quality of instructor is one of the most critical measures for student satisfaction, leading to the education process’s outcome (Munteanu et al., 2010; Arambewela & Hall, 2009; Ramsden, 1991). Suppose the teacher delivers the course effectively and influence the students to do better in their studies. In that case, this process leads to student satisfaction and enhances the learning process (Ladyshewsky, 2013). Furthermore, understanding the need of learner by the instructor also ensures student satisfaction (Kauffman, 2015). Hence the hypothesis that the quality of instructor significantly affects the satisfaction of the students was included in this study.

• H1: The quality of the instructor positively affects the satisfaction of the students.

Course design and satisfaction of students

The course’s technological design is highly persuading the students’ learning and satisfaction through their course expectations (Liaw, 2008; Lin et al., 2008). Active course design indicates the students’ effective outcomes compared to the traditional design (Black & Kassaye, 2014). Learning style is essential for effective course design (Wooldridge, 1995). While creating an online course design, it is essential to keep in mind that we generate an experience for students with different learning styles. Similarly, (Jenkins, 2015) highlighted that the course design attributes could be developed and employed to enhance student success. Hence the hypothesis that the course design significantly affects students’ satisfaction was included in this study.

• H2: Course design positively affects the satisfaction of students.

Prompt feedback and satisfaction of students

The emphasis in this study is to understand the influence of prompt feedback on satisfaction. Feedback gives the information about the students’ effective performance (Chang, 2011; Grebennikov & Shah, 2013; Simsek et al., 2017). Prompt feedback enhances student learning experience (Brownlee et al., 2009) and boosts satisfaction (O’donovan, 2017). Prompt feedback is the self-evaluation tool for the students (Rogers, 1992) by which they can improve their performance. Eraut (2006) highlighted the impact of feedback on future practice and student learning development. Good feedback practice is beneficial for student learning and teachers to improve students’ learning experience (Yorke, 2003). Hence the hypothesis that prompt feedback significantly affects satisfaction was included in this study.

• H3: Prompt feedback of the students positively affects the satisfaction.

Expectations and satisfaction of students

Expectation is a crucial factor that directly influences the satisfaction of the student. Expectation Disconfirmation Theory (EDT) (Oliver, 1980) was utilized to determine the level of satisfaction based on their expectations (Schwarz & Zhu, 2015). Student’s expectation is the best way to improve their satisfaction (Brown et al., 2014). It is possible to recognize student expectations to progress satisfaction level (ICSB, 2015). Finally, the positive approach used in many online learning classes has been shown to place a high expectation on learners (Gold, 2011) and has led to successful outcomes. Hence the hypothesis that expectations of the student significantly affect the satisfaction was included in this study.

• H4: Expectations of the students positively affects the satisfaction.

Satisfaction and performance of the students

Zeithaml (1988) describes that satisfaction is the outcome result of the performance of any educational institute. According to Kotler and Clarke (1986), satisfaction is the desired outcome of any aim that amuses any individual’s admiration. Quality interactions between instructor and students lead to student satisfaction (Malik et al., 2010; Martínez-Argüelles et al., 2016). Teaching quality and course material enhances the student satisfaction by successful outcomes (Sanderson, 1995). Satisfaction relates to the student performance in terms of motivation, learning, assurance, and retention (Biner et al., 1996). Mensink and King (2020) described that performance is the conclusion of student-teacher efforts, and it shows the interest of students in the studies. The critical element in education is students’ academic performance (Rono, 2013). Therefore, it is considered as center pole, and the entire education system rotates around the student’s performance. Narad and Abdullah (2016) concluded that the students’ academic performance determines academic institutions’ success and failure.

Singh et al. (2016) asserted that the student academic performance directly influences the country’s socio-economic development. Farooq et al. (2011) highlights the students’ academic performance is the primary concern of all faculties. Additionally, the main foundation of knowledge gaining and improvement of skills is student’s academic performance. According to Narad and Abdullah (2016), regular evaluation or examinations is essential over a specific period of time in assessing students’ academic performance for better outcomes. Hence the hypothesis that satisfaction significantly affects the performance of the students was included in this study.

• H5: Students’ satisfaction positively affects the performance of the students.

Satisfaction as mediator

Sibanda et al. (2015) applied the goal theory to examine the factors persuading students’ academic performance that enlightens students’ significance connected to their satisfaction and academic achievement. According to this theory, students perform well if they know about factors that impact on their performance. Regarding the above variables, institutional factors that influence student satisfaction through performance include course design and quality of the instructor (DeBourgh, 2003; Lado et al., 2003), prompt feedback, and expectation (Fredericksen et al., 2000). Hence the hypothesis that quality of the instructor, course design, prompts feedback, and student expectations significantly affect the students’ performance through satisfaction was included in this study.

• H6: Quality of the instructor, course design, prompt feedback, and student’ expectations affect the students’ performance through satisfaction.
• H6a: Students’ satisfaction mediates the relationship between quality of the instructor and student’s performance.
• H6b: Students’ satisfaction mediates the relationship between course design and student’s performance.
• H6c: Students’ satisfaction mediates the relationship between prompt feedback and student’s performance.
• H6d: Students’ satisfaction mediates the relationship between student’ expectations and student’s performance.

Participants

In this cross-sectional study, the data were collected from 544 respondents who were studying the management (B.B.A or M.B.A) and hotel management courses. The purposive sampling technique was used to collect the data. Descriptive statistics shows that 48.35% of the respondents were either MBA or BBA and rests of the respondents were hotel management students. The percentages of male students were (71%) and female students were (29%). The percentage of male students is almost double in comparison to females. The ages of the students varied from 18 to 35. The dominant group was those aged from 18 to 22, and which was the under graduation student group and their ratio was (94%), and another set of students were from the post-graduation course, which was (6%) only.

Materials

The research instrument consists of two sections. The first section is related to demographical variables such as discipline, gender, age group, and education level (under-graduate or post-graduate). The second section measures the six factors viz. instructor’s quality, course design, prompt feedback, student expectations, satisfaction, and performance. These attributes were taken from previous studies (Yin & Wang, 2015; Bangert, 2004; Chickering & Gamson, 1987; Wilson et al., 1997). The “instructor quality” was measured through the scale developed by Bangert (2004). The scale consists of seven items. The “course design” and “prompt feedback” items were adapted from the research work of Bangert (2004). The “course design” scale consists of six items. The “prompt feedback” scale consists of five items. The “students’ expectation” scale consists of five items. Four items were adapted from Bangert, 2004 and one item was taken from Wilson et al. (1997). Students’ satisfaction was measure with six items taken from Bangert (2004); Wilson et al. (1997); Yin and Wang (2015). The “students’ performance” was measured through the scale developed by Wilson et al. (1997). The scale consists of six items. These variables were accessed on a five-point likert scale, ranging from 1(strongly disagree) to 5(strongly agree). Only the students from India have taken part in the survey. A total of thirty-four questions were asked in the study to check the effect of the first four variables on students’ satisfaction and performance. For full details of the questionnaire, kindly refer Appendix Tables 6.

Design

The study used a descriptive research design. The factors “instructor quality, course design, prompt feedback and students’ expectation” were independent variables. The students’ satisfaction was mediator and students’ performance was the dependent variable in the current study.

Procedure

In this cross-sectional research the respondents were selected through judgment sampling. They were informed about the objective of the study and information gathering process. They were assured about the confidentiality of the data and no incentive was given to then for participating in this study. The information utilizes for this study was gathered through an online survey. The questionnaire was built through Google forms, and then it was circulated through the mails. Students’ were also asked to write the name of their college, and fifteen colleges across India have taken part to fill the data. The data were collected in the pandemic period of COVID-19 during the total lockdown in India. This was the best time to collect the data related to the current research topic because all the colleges across India were involved in online classes. Therefore, students have enough time to understand the instrument and respondent to the questionnaire in an effective manner. A total of 615 questionnaires were circulated, out of which the students returned 574. Thirty responses were not included due to the unengaged responses. Finally, 544 questionnaires were utilized in the present investigation. Male and female students both have taken part to fill the survey, different age groups, and various courses, i.e., under graduation and post-graduation students of management and hotel management students were the part of the sample.

Exploratory factor analysis (EFA)

To analyze the data, SPSS and AMOS software were used. First, to extract the distinct factors, an exploratory factor analysis (EFA) was performed using VARIMAX rotation on a sample of 544. Results of the exploratory analysis rendered six distinct factors. Factor one was named as the quality of instructor, and some of the items were “The instructor communicated effectively”, “The instructor was enthusiastic about online teaching” and “The instructor was concerned about student learning” etc. Factor two was labeled as course design, and the items were “The course was well organized”, “The course was designed to allow assignments to be completed across different learning environments.” and “The instructor facilitated the course effectively” etc. Factor three was labeled as prompt feedback of students, and some of the items were “The instructor responded promptly to my questions about the use of Webinar”, “The instructor responded promptly to my questions about general course requirements” etc. The fourth factor was Student’s Expectations, and the items were “The instructor provided models that clearly communicated expectations for weekly group assignments”, “The instructor used good examples to explain statistical concepts” etc. The fifth factor was students’ satisfaction, and the items were “The online classes were valuable”, “Overall, I am satisfied with the quality of this course” etc. The sixth factor was performance of the student, and the items were “The online classes has sharpened my analytic skills”, “Online classes really tries to get the best out of all its students” etc. These six factors explained 67.784% of the total variance. To validate the factors extracted through EFA, the researcher performed confirmatory factor analysis (CFA) through AMOS. Finally, structural equation modeling (SEM) was used to test the hypothesized relationships.

Measurement model

The results of Table 1 summarize the findings of EFA and CFA. Results of the table showed that EFA renders six distinct factors, and CFA validated these factors. Table 2 shows that the proposed measurement model achieved good convergent validity (Aggarwal et al., 2018a, b). Results of the confirmatory factor analysis showed that the values of standardized factor loadings were statistically significant at the 0.05 level. Further, the results of the measurement model also showed acceptable model fit indices such that CMIN = 710.709; df = 480; CMIN/df = 1.481 p < .000; Incremental Fit Index (IFI) = 0.979; Tucker-Lewis Index (TLI) = 0.976; Goodness of Fit index (GFI) = 0.928; Adjusted Goodness of Fit Index (AGFI) = 0.916; Comparative Fit Index (CFI) = 0.978; Root Mean Square Residual (RMR) = 0.042; Root Mean Squared Error of Approximation (RMSEA) = 0.030 is satisfactory.

The Average Variance Explained (AVE) according to the acceptable index should be higher than the value of squared correlations between the latent variables and all other variables. The discriminant validity is confirmed (Table 2) as the value of AVE’s square root is greater than the inter-construct correlations coefficient (Hair et al., 2006). Additionally, the discriminant validity existed when there was a low correlation between each variable measurement indicator with all other variables except with the one with which it must be theoretically associated (Aggarwal et al., 2018a, b; Aggarwal et al., 2020). The results of Table 2 show that the measurement model achieved good discriminate validity.

Structural model

To test the proposed hypothesis, the researcher used the structural equation modeling technique. This is a multivariate statistical analysis technique, and it includes the amalgamation of factor analysis and multiple regression analysis. It is used to analyze the structural relationship between measured variables and latent constructs.

Table 3 represents the structural model’s model fitness indices where all variables put together when CMIN/DF is 2.479, and all the model fit values are within the particular range. That means the model has attained a good model fit. Furthermore, other fit indices as GFI = .982 and AGFI = 0.956 be all so supportive (Schumacker & Lomax, 1996; Marsh & Grayson, 1995; Kline, 2005).

Hence, the model fitted the data successfully. All co-variances among the variables and regression weights were statistically significant (p < 0.001).

Table 4 represents the relationship between exogenous, mediator and endogenous variables viz—quality of instructor, prompt feedback, course design, students’ expectation, students’ satisfaction and students’ performance. The first four factors have a positive relationship with satisfaction, which further leads to students’ performance positively. Results show that the instructor’s quality has a positive relationship with the satisfaction of students for online classes (SE = 0.706, t-value = 24.196; p < 0.05). Hence, H1 was supported. The second factor is course design, which has a positive relationship with students’ satisfaction of students (SE = 0.064, t-value = 2.395; p < 0.05). Hence, H2 was supported. The third factor is Prompt feedback, and results show that feedback has a positive relationship with the satisfaction of the students (SE = 0.067, t-value = 2.520; p < 0.05). Hence, H3 was supported. The fourth factor is students’ expectations. The results show a positive relationship between students’ expectation and students’ satisfaction with online classes (SE = 0.149, t-value = 5.127; p < 0.05). Hence, H4 was supported. The results of SEM show that out of quality of instructor, prompt feedback, course design, and students’ expectation, the most influencing factor that affect the students’ satisfaction was instructor’s quality (SE = 0.706) followed by students’ expectation (SE =5.127), prompt feedback (SE = 2.520). The factor that least affects the students’ satisfaction was course design (2.395). The results of Table 4 finally depicts that students’ satisfaction has positive effect on students’ performance ((SE = 0.186, t-value = 2.800; p < 0.05). Hence H5 was supported.

Table 5 shows that students’ satisfaction partially mediates the positive relationship between the instructor’s quality and student performance. Hence, H6(a) was supported. Further, the mediation analysis results showed that satisfaction again partially mediates the positive relationship between course design and student’s performance. Hence, H6(b) was supported However, the mediation analysis results showed that satisfaction fully mediates the positive relationship between prompt feedback and student performance. Hence, H6(c) was supported. Finally, the results of the Table 5 showed that satisfaction partially mediates the positive relationship between expectations of the students and student’s performance. Hence, H6(d) was supported.