Students’ Self-Motivation and Online Learning Students’
Satisfaction among UNITAR College Students
Sharfika Raime1*, Mohd. Farid Shamsudin2, Raemah Abdullah Hashim3, Norsafriman Abd. Rahman1
1 UNITAR College, UNITAR International University, Selangor, Malaysia
2 School of Business, University Kuala Lumpur, Kuala Lumpur, Malaysia
3 Business and management, Open University Malaysia, Selangor, Malaysia
*Corresponding Author: sharfika@unitar.my
Accepted: 15 September 2020 | Published: 30 September 2020
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Abstract: Online learning is a relatively ongoing word used to define a type of learning that can be performed through websites. The prominence of online learning development and its implementation is indisputable especially in today’s world since the attacks and outbreaks of COVID-19 virus. This research investigated the determining factor of students’ self- motivation towards online learning students’ satisfaction in one of the private colleges in Malaysia applying the quantitative method. A total of fifty-three (53) students from UNITAR College have partaken in the questionnaire survey utilising questionnaires that were adapted from previous researches. Data were assessed using SPSS version 23 for descriptive analysis and Smart-PLS 3.0 for both measurement and structural model analysis. Results disclosed that students’ self-motivation has a significant relationship with UNITAR College online learning students’ satisfaction (t-value=8.589, p-value=0.000). By understanding the factor that leads to students’ satisfaction, it is hoped that all lecturers, colleges, and universities will ensure that students receive the necessary support to ascertain that their motivation level is consistent from the beginning until they are able to attend face-to-face classes as usual.
Keywords: COVID-19, Expectancy-Confirmation Theory, Online Learning, Self-Motivation, Students’ Satisfaction
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1. Introduction
Higher education is deemed as colossal instruments at a college or university level for one’s social, financial and cash related improvement of a country (Weerasinghe & Fernando, 2017). Therefore, coming to high-quality education is significant to education structure to meet the expectation of people and society (Rasouli, Rahbania, & Attaran, 2016).
Nevertheless, since the attack of COVID-19 virus, learning at all levels including higher education expected to encounter a couple of changes as far as learning procedures. To help manage and curb the spread of the disease, and since the declaration of the Movement Control Order (MCO) by the Malaysian Government, all learning institutions including colleges and universities have opted to online learning from traditional up close or face-to- face class (Raman, 2020). This decision was moreover established on headings from the Ministry of Higher Education Malaysia which hinders face-to-face learning sessions. Despite the new norm, most higher education institutions have taken exceptional measure to guarantee that their students can keep learning through computerised or digital infers (Raman, 2020) and to ensure less interruption to both teaching and learning exercises (Chin, 2020). This endeavour is essential to ensure that students have similar learning experiences as
when they attend the traditional face-to-face classes, similarly, as to ensure learning goals for each subject are met. In particular, higher learning institution must ensure the distant or remote styles are fruitful and successful for students (Chin, 2020).
2. Literature review
2.1 Online Learning in Higher Education
Online learning is depicted as a participating in a learning system that included human and non-human beings cooperating through computer-based instructional systems to accomplish educational objectives and targets while achieving learning results just as learning satisfaction and fulfilment among students (Eom, Joseph Wen, & Ashill, 2006). The ability of the colleges and universities to accomplish learning results and higher satisfaction among the students is vital as these aspects ultimately show the effectiveness and viability of the colleges' and universities' online learning system (Rasouli et al., 2016).
In many developing and growing countries like Malaysia, online learning, and Information Communication Technology (ICT) were claimed to have become one of the significant parts of a national attempt to improve public education. The action is purported to have occurred because of the positive demand and reaction to both virtual classes and courses. Besides, the acknowledgement of the online courses and complete an online degree program at both universities and colleges globally is also reported to be growing (Eom et al., 2006).
2.2 Covid-19 and Online Learning in Higher Education
Since the time the world was attacked by the Covid-19 virus, all the economics and financial industries including the higher education industry has implemented the new normal. The new normal is including the rebuilding of the online learning scene (Linney, 2020) where both universities and colleges are constrained to move from traditional face-to-face schooling to online learning (Arumugam, 2020). The transformation of the online learning is not simply to ensure that learning can continue however to ensure that the nation's economic development can proceed too.
To ensure limited disturbance to teaching and learning exercises, HEIs must ensure the so- called faraway methods are significant for all students by guaranteeing a similar degree of learning viability is accomplished whether the lecturers and students are in the same physical space (Chin, 2020; Linney, 2020). Nonetheless, notwithstanding the endeavours issues identified with online learning will happen to each institution of higher learning and this issue can be addressed via the study of online learning students' satisfaction (Strong, 2012).
Among the variables that impact the degree of online learning students’ satisfaction but purported to be understudies are self-motivation (Al-Rahmi et al., 2018; Raime, Shamsudin, Hashim, & Abd Rahman, 2020). Likewise, satisfaction level investigations involving students who were not initially enlisted as an online student (distance learning program) or blended learning were also less examined (Raime et al., 2020). This has presented an opportunity for this study to be performed where the sample involved were among the students who had to go through the online learning because of the pandemic.
2.3 Students’ Satisfaction
Students’ satisfaction is implied as a transient perspective, originating from an evaluation or judgement of students’ educational experiences (Elliott & Healy, 2001). It is portrayed as the impression of joy in the learning condition (Naaj, Nachouki, & Ankit, 2012) and a positive precursor of students dedication (Weerasinghe & Fernando, 2017). In this manner, students’
satisfaction can be depicted as a part of the relative level of experiences and perceived performance about educational administrations and services during the research time frame (Carey, Cambiano, & Vore, 2002). To put it plainly, students’ satisfaction can be characterised as a transient mentality coming about because of students’ evaluation of their educational encounters, administrations, services, as well as facilities (Weerasinghe &
Fernando, 2017).
As cited by Strong (2012), the feeling of satisfaction among students can only be achieved when there is no existence of a gap between what is predicted and what is obtained from the service provider. Although investigation and studies on students' satisfaction have been reported to be overwhelmed, nonetheless, studies related to students' satisfaction specifically on online learning are still inadequate (Al-Rahmi et al., 2018; Raime et al., 2020; Tan, Chuah, & Ting, 2016). Furthermore, investigation on students' satisfaction towards online learning concerning students who did not enrol for online learning in the first place are also insufficient (Raime et al., 2020). Henceforth, studies concerning students that ought to undergo online learning due to world disasters such as COVID-19 is considered important to facilitate higher education in recognising the key factors for attaining higher students’
satisfaction level. This can also cultivate their devotion and loyalty to continue studying in the same college or university (Strong, 2012). Besides that, literature works have stated that students’ satisfaction in online learning ought to be consistently evaluated to plausibly improve its delivery (Strong, 2012). Also, the satisfaction level is postulated to be complex to define since it depends on students’ judgment and experience. As A Result, there is a requirement for Higher Education Industry to repeatedly investigate and differentiate the components that lead to students' satisfaction (Daud, Ali, & Jantan, 2019).
2.4 Self-Motivation
Students are the main participants of online learning systems. Online learning systems are reported to take on more responsibilities on students as compared to traditional face-to-face learning systems. Due to the higher responsibilities and distinct learning strategy, self- regulated learning is deemed necessary for an online learning system to be effective (Eom et al., 2006).
Self-regulated learning entails changing responsibilities and roles of students from passive learners to active learners since these students must self-manage their learning process (Eom et al., 2006). The foundation of self-regulated learning is self-motivation (Smith, 2001). Self- motivation is delineated as the self-generated energy that provides behaviour directed toward a particular purpose (Zimmerman, 1998). The strength of the student’s self-motivation is affected by self-regulatory aspects and practices. The self-regulatory aspects are the student’s personal learning characteristics containing self-efficacy (Bandura, 1977). Since self-efficacy impacts choice, efforts, and preference (Schunk & Zimmerman, 1997), a questionnaire by Eom et al. (2006) representing self-efficacy is employed to gauge the strength of students’ self-motivation for this research.
One of the glaring distinctions between successful students is their noticeable ability to motivate themselves, even when they do not have the burning desire to fulfil a certain task. In contrast, less successful students are likely to have difficulty in calling up self-motivation skills, for examples goal setting, verbal reinforcement, and self-rewards (Dembo & Eaton, 2000). In place of this, the existent literature indicates that students with strong motivation will be more successful and tend to learn the most in online learning courses than those with a smaller amount of motivation. Moreover, students’ motivation is reported to be a major
element that influences the attrition and completion rates in the online learning course and a lack of motivation is also said to be connected to high dropout rates (Frankola, 2001).
Therefore, this study was conducted to involve students who initially did not register as online students but had to undergo online classes. This study is considered significant as it identifies whether student's self-motivation among students in question has a relationship or a predetermined factor to online learning students' satisfaction.
2.5 The Research Model
The research framework that shows the relationship between the independent variables (self- motivation) and the dependent variable (students’ satisfaction), is a result of comprehensive analysis of prior works. The recommended research framework for this research is demonstrated in Figure 1, underpinned by the expectation-confirmation theory (ECT). The ECT was originally found by Richard L. Oliver in 1977 (“Expectation confirmation theory,”
2020). ECT is a cognitive theory which tries to describe ones’ satisfaction level can only be obtained if what they are getting and experiencing met their expectation (Daud et al., 2019).
There are four (4) main constructs that entailed ECT namely, expectations, perceived performance, disconfirmation of beliefs, and satisfaction (“Expectation confirmation theory,”
2020). However, only two of the ECT’s constructs i.e. expectation and satisfaction are applicable to support the development of this research hypothesis. For this study, the population were among the students who initially did not register for online courses but were forced to undergo online classes due to COVID-19. Assuming self-motivation is expected to be carried by these students until the pandemic ended, their failure in meeting their expectations will directly deteriorate their satisfaction level towards online learning.
Therefore, self-motivation that represents confidence and self-preparation to successfully cope with online learning and the new normal is posited as important to achieve the feelings of satisfaction (Tan et al., 2016). Thus, the hypothesis is as below:
H1: There is a significant relationship between students’ self-motivation and online students’
satisfaction.
Self- Motivation
Online Learning Students’
Satisfaction
Figure 1: Research framework
3. Research Methodology
The method of the data collection for this research is subject to the polls that were adapted from previous investigations. Research instrument to evaluate students 'satisfaction was adapted from Cobb (2009) while research instrument to evaluate students’ self-motivation was adapted from Eom et al. (2006). The decision of utilising Cobb (2009) and Eom et al.
(2006) to assess students’ satisfaction and students’ self-motivation respectively was due to both of these instruments were created exclusively for the online learning in the higher education institution context. Furthermore, earlier studies purported the reliability level of these questionnaires were very high with 0.87 for students’ satisfaction (Cobb, 2009) and 0.75 for students’ self-motivation (Eom et al., 2006). Notwithstanding the high-reliability results from the earlier study, the researchers have conducted their own internal consistency test to ascertain the reliability level is above the suggested level. The distribution of research instrument was self-administered and distributed to all students from UNITAR college. A total of fifty-three (53) questionnaires were disseminated through a google survey form. The
amount of 53 is representing all students from UNITAR college from every intake. The decision of employing google survey form is because it was the simplest and practical method to relate to students during this pandemic period besides its effectiveness in term of cost and time. The researchers have received a great deal of support and cooperation from every student where the amount of time needed to get a response from them was very short.
A total of 53 responses were received after three days. Since the researcher has utilised the google survey form method, all the questions from the feedback have been answered and this has made it easier for the researcher to address the missing data problem. For descriptive analysis, SPSS version 23 was used while Smart PLS 3.0 was used for assessing the structural model and measurement model.
4. Results and Discussion
4.1 Descriptive Analysis
The segment or demographic profile characteristics incorporate gender, ethnicity, age and certificate program. Larger part of the respondents was female (67.9%) and the remaining were male (32.1%). The samples additionally demonstrated that respondents were for the most part between 16 to 20 years old (94.3%), followed by respondents between 21 to 25 years old (5.7%). This study was represented by various races which were not obliged to Malay just, yet 22.6% of respondents are Chinese, 32.1% of respondents are Indian and 1.9%
signified other (implicit) ethnicity. Table 1 underneath summarised the respondents' profile.
Table 1: Profile of Respondents
Research Sample (n=53)
Particular Variables Frequencies Percentage
Gender Male 17 32.1
Female 36 67.9
Age 16 – 20 years 50 94.3
21 – 25 years 3 5.7
Ethnicity Malay 23 43.4
Chinese 12 22.6
Indian 17 32.1
Others 1 1.9
Program Certificate in Business Studies 50 94.3
Certificate in Hotel Operations 3 5.7
4.2 Measurement Model Analysis
The basic stage was the verification of the validity and reliability of the measurement model utilising the Smart PLS 3.0. In this area, the confirmatory factor analysis, internal consistency, convergent validity and discriminant validity of the model were inspected.
4.2.1 Confirmatory Factor Analysis
As this research has employed survey instruments adjusted from previous researchers, exploratory data analysis (EFA) is inferred not necessary (Hair, Hult, Ringe, & Sarstedt, 2014). Nevertheless, to confirm the constructs’ structure, confirmatory factor analysis (CFA) has been executed. The CFA was performed to uncover and remove any indicator with low reliability hence increase the average variance extracted (AVE) and the composite reliability (CR) of the model (Henseler, Ringle, & Sarstedt, 2015). As postulated by Hair et al. (2014), loading value that is within the range of 0.4 to 0.7 is acceptable provided the AVE scores do not fall below the threshold (AVE>0.5). From the CFA results, only one (1) indicator from a total of two (2) constructs was removed. Indicator eliminated was from the student’s satisfaction construct which left this construct with six (6) items i.e. SS1, SS2, SS3, SS5, SS6
and SS7. Table 2 below reported the AVE scores after the removal of the item with low loading value.
Table 2: Average Variance Extracted
Average Variance Extracted (AVE)
Self-Motivation 0.845
Students' Satisfaction 0.586
4.2.2 Internal Consistency
The internal consistency has been evaluated for this research employing the reliability test i.e.
Cronbach’s alpha (α), the composite reliability (pC) and the Djikstra-Henseler’s Rho (pA).
Table 3 below summarised the results for all three reliability tests. Based on the results in below table, it can be understood that all the variables generated an exceptional reliability as all components give results above 0.7 that is above the minimum acceptable value for Cronbach’s Alpha (Sekaran & Bougie, 2013), composite reliability (Sekaran & Bougie, 2013) and Djikstra-Henseler’s Rho (Dijkstra & Henseler, 2015).
Table 3: Composite Reliability
Cronbach's Alpha rho_A Composite
Reliability
Perceived Lecturers' Feedback 0.817 0.820 0.916
Students' Satisfaction 0.854 0.889 0.893
4.2.3 Outer Loading and Cross Loadings
Urbach and Ahlemann (2010) posited, indicator reliability assessment is important to ensure that a set of indicators that were formed really measure what it is supposed to measure. The assertion can be obtained by making sure that the whole indicators were loaded rightly on their corresponding construct both horizontally and vertically. See Table 4 below.
Table 4: Outer Loading and Cross Loadings of the Items
Self-Motivation Students' Satisfaction
SM1 0.926 0.698
SM2 0.913 0.649
SS1 0.737 0.885
SS2 0.456 0.730
SS3 0.662 0.850
SS5 0.549 0.754
SS6 0.479 0.777
SS7 0.381 0.551
4.2.4 Discriminant Validity
Fornell and Larcker criterion is projected by the squared roots of AVEs latent variable which should be greater than the inter constructs between the latent variable and other entire variables (Fornell & Larcker, 1981). Therefore, any item that does not match the condition (item from cross-loading that is below 0.1) should be eradicated (Fornell & Larcker, 1981).
From Table 5 below, it indicated that the squared roots of AVE latent variable were higher than other correlations. The squared root AVE of students’ satisfaction resulted 0.765 which is greater than its other construct’s correlation (self-motivation ==> students’ satisfaction).
Another squared root of AVE also reveals the same result which self-motivation recorded 0.919.
Table 5: Fornell & Larcker
Self-Motivation Students'
Satisfaction
Self-Motivation 0.919
Students' Satisfaction 0.733 0.765
4.3 Structural Model Analysis
SmartPLS 3.0 was applied for the researchers to assess the significance and relevance of the structural model relationship. Results were obtained by running the PLS algorithm to obtain the path coefficients diagram/results as shown in Figure 2. Figure 3 alternatively is to get the T-value and results were obtained via bootstrapping. The satisfactory t-value is expected to be higher than 1.645 at the significance level 0.05 (Hair, Hult, Ringle, & Sarstedt, 2017).
Therefore, from the results shown in Table 6.0, self-motivation was a predictor to online learning students’ satisfaction besides being significantly related to online learning students’
satisfaction by meeting the t-value and p-value criteria (t-value=8.589, p-value=0.000).
Therefore, hypothesis H1 was supported for this study.
Table 6.0: Hypotheses testing Relationship Original
Sample Mean SD T Value P Value Result
Self-Motivation -> Students' Satisfaction
0.733 0.747 0.085 8.589 0.000 Supported
Figure 2: Path Coefficients Results
Figure 3: Path Coefficients T- Value
4.3.1 R2 for exogenous variable in the relationship with students’ satisfaction
To examine the R² value of the dependent variable that explained the variance for the independent variable, another PLS Algorithm was performed (Figure 4). The result of R² of endogenous variable for the relationship with the exogenous variable is shown in Table 7.0.
Based on the table, the movement of students’ satisfaction is being explained or affected by the movement of students’ self-motivation by only 53.8%. According to Hair et al. (2017), it can be concluded that the R2 of the direct relationship between students’ self-motivation and online learning students’ satisfaction is considered as moderate.
Table 7.0: R2 Result
Variables Students' Satisfaction
Students’ Self-Motivation 0.538
Figure 4: R2 Students’ Self-Motivation – Students’ Satisfaction
5. Conclusion
In the nutshell, the analysis results indicated that there is a significant relationship between students’ self-motivation and online learning students’ satisfaction with T-value higher than 1.645 (t=8.589). Besides, students’ self-motivation is also a predictor to online learning students’ satisfaction with P-value below than 0.10 (p=0.000). These two important results have consequently made hypothesis H1 for this study to be supported. However, even though the results for the path coefficient analysis has confirmed that there is a substantial effect between the independent and dependent variable, the coefficient of determination result (R2) showed that students’ self-motivation has moderate consequence towards students’ online learning satisfaction (R2=0.538). In another word, any movement that happened to the predictor will only influence 53.8% movement in students’ satisfaction. Since students’ self- motivation only influence online learning students’ satisfaction with 53.8%, this indirectly means that there is supposed to be another predictor of students’ satisfaction to represent the remaining 46.2%. Therefore, upcoming, or future researchers are recommended to extend this study to discover other aspects that have a relationship with online learning students' satisfaction.
All in all, besides filling in the literature gap, the results of this research can enhance and broaden the body of knowledge in the field of strategic management, people management as well as e-learning, especially in the education industry. Moreover, even though this research was a case study that only engaged students from UNITAR college as the respondents, the
findings can still serve as a reference to obtain an idea of one of the key components in getting a higher level of online learning students’ satisfaction. Furthermore, this research contribution can be deemed significant too because the samples involved were online students who initially did not register as online students but had to undertake online classes because of COVID-19. At the very least, if in the future the world is experiencing the similar challenges or problems that the classes must be implemented online, lecturers and the management of colleges and universities have a basic idea about the important element sought by the understudies in finding the online learning satisfaction.
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