Index

Student Satisfaction in Online Learning of Islamic Higher Education in Indonesia during the Second Wave of COVID-19 Pandemic

Muhammad Munadi1*; Fauzi Annur2 ; Yudi Saputra3 

1Universitas Islam Negeri Raden Mas Said, Surakarta, Indonesia.
2Institut Agama Islam Negeri, Salatiga, Indonesia.

Abstract

This study aims to determine student satisfaction with online learning at UIN Raden Mas Said Surakarta Indonesia. In this study, the researchers developed new variables related to student satisfaction in online learning, such as student commitment, student independence, parental support, main source of support for online learning, student readiness, creative and innovative learning and effectiveness and behavioral intentions. The methodology used is a quantitative research method, with measurement and analysis using the smart-PLS application. The population in this study was all students of the UIN RMS Surakarta education faculty. The results of this study show that there were 412 respondents who filled out the questionnaire. After being processed using Smart-PLS there were three variables that were accepted:  first, effectiveness has a significant effect on student satisfaction in online learning, second, student commitment has a significant effect on student satisfaction in online learning and third, student satisfaction has a significant effect on the behavioral intentions of students in online learning.

Keywords: Student satisfaction, Online learning, Commitment, Effectiveness, Intensity.

Contribution of this paper to literature
This study not only provides an overview of the variables that need to be considered by an educator, but also shows these variables should receive more emphasis compared with other variables. This is because effectiveness, commitment and satisfaction have a significant impact on satisfaction itself, as well as on the intensity of student behavior in learning.

1. Introduction

The year 2020-2021  has been a  challenging year for world health. However, it does not end there  as the economic and education sectors  have  also experienced equally serious impacts.  From the beginning of 2020  till today, countries  all over the world have implemented distance learning or online learning systems  to  prevent the  spread of the Covid-19 virus.  Unpreparedness in the early days of the pandemic for online learning had  to be faced  by almost all countries. A study found that higher education institutions in Romania  were not very  prepared for online learning; as a result serious technical problems cropped up,  followed by the lack of technical skills  among teachers  which resulted in their teaching styles not being able to  adapt well to the online environment (Coman et al., 2020).  Another study revealed that facility readiness had a significant effect on student satisfaction (Kumar, 2021). In addition,  some countries  were beginning to show  an increase in stress and anxiety due to the Covid-19 outbreak. Several stressors were identified as contributing factors to  the increase in stress levels, anxiety, depression  and difficulty in concentration  experienced among students (Son, Hegde, Smith, Wang  & Sasangohar, 2020).

The results of research in developed countries such as France and South Korea  show that the majority of French students express a preference for  learning in face to face classrooms compared  with online  classes, while the preferences of Korean students are more balanced. On the average, Korean students express higher satisfaction with online  learning compared  with French students (Jung & Vranceanu, 2020).

In the past, online learning was only used for conducting courses, meaning that it was not completely done routinely,  as in Alison, Canvas Network, Coursera, iCourse, EdX, etc. (UNESCO,  2020), all of which provide online courses with very interesting programs. The duration of time and continuity of learning are  factors that impact the difference  between online learning and online courses   and research results clearly show that online learning in these courses has a very significant impact. The Covid-19 pandemic has been   prevailing for more than a year; the unavoidable  result is distance learning/online learning. Distance learning  however provides opportunities to experiment with alternative teaching methods, tools and assessment styles (Alolaywi, 2021). Even more interesting, it was found that the WhatsApp Group platform became the most effective learning media at the beginning of the Covid-19 pandemic (Wargadinata, Maimunah, Eva  & Rofiq, 2020). In order to follow up on online learning, practitioners in the field of education do not stop creating and innovating so that the new learning  methods can make students understand and be satisfied with their progress and achievements. One of the learning objectives is to make students feel satisfied with the learning process. There are quite a number of studies that reveal the relationship of student satisfaction with various  factors. In general, student satisfaction is influenced by perceived usefulness, perceived pleasure  and effectiveness of multimedia content (Levent, Balcikanli, Calli, Cebeci  & Seymen, 2013).

Information quality and self-efficacy have a significant effect on student satisfaction in online learning (Machado-Da-Silva, Meirelles, Filenga  & Brugnolo Filho, 2014), in  particular self-efficacy (Alzahrani & Seth, 2021). In addition, providing motivation in online learning is  a most important dimension and has a significant impact on student satisfaction (Hariyati, Wagino  & Mudjito, 2021; Hermida, 2020; Kirmizi, 2015) at the undergraduate and postgraduate levels in Bangladesh (Rahman, Uddin  & Dey, 2021).

Communication and flexibility are also a very decisive part of student satisfaction in online learning (Elshami et al., 2021). The level of effort  shown by the instructor, agreement on the appropriateness of the customized assessment method  and the perception of well-delivered online learning prove  to be very important in determining the satisfaction scores (Ho, Cheong  & Weldon, 2021). The results show that the important factors in ensuring online learning satisfaction are the instructor's role in providing online media training and the strength of peer interaction (Nambiar, 2020; Nasir, 2020; Ngo, Budiyono  & Ngadiman, 2021; Thach, Lai, Nguyen  & Nguyen, 2021) . This further confirms that technical readiness and interaction in online learning  determine the level of student satisfaction. The higher the level of satisfaction felt by students, the more positive the impact on student performance (Gopal, Singh  & Aggarwal, 2021) and  student achievement will increase further (Basith, Rosmaiyadi, Triani  & Fitri, 2020). This is a very important foundation where student satisfaction leads to good and maximum academic results.

 In the areas of satisfaction and online learning, basically a lot of research has been done as described above, but in the present study the researchers are trying to develop other possible variables  through various discussions held in Indonesia, that have an impact on online learning (Rohmah, 2020). The  new variables that the researchers present, given the reason that these variables have never been associated with satisfaction and basically reside within students themselves,  are very close to their scope, such as student commitment to learning, independence, parental support,  main source of support for online learning, student readiness, creative-innovative teaching, effectiveness and intensity of behavior.

Some of these  variables are very important to be investigated further, with the aim of uncovering the closest variables so that future learning can be managed properly. The various studies above were  mostly carried out at the beginning of 2020-2021 so  they were still included in the early categories of online learning experiments, while the present  research was carried out in the mid-quarter of 2021 where  online learning  had become a new practice in educational institutions. Satisfaction is also a very important part where the measurement is based on students who   learn through online learning organizations. With this research, the evaluation of online learning in an educational institution can be maximized.

2. Method

The methodology used is a quantitative research method with the calculation and analysis done using the smart PLS application.

The population in this study comprised all students majoring in education at UIN RMS Surakarta Indonesia. The questionnaire with 58 statements was distributed randomly through the google-form application.

Table 1.  Respondent demographics.
Respondent 412
Gender 77. 7% Female
22. 3% Male
Department Islamic Education (PAI), English Language Education (PBI), Madrasah Teacher Education (PGMI), Indonesian Language Education (TBI)
Semester / level Semester 2 :                       44%
Semester 4 :                       21.2%
Semester 6 :                       34.1%
Semester 8 :                        0.7%
City 1.       Sukoharjo 13.    Madiun 25.    Kuantan Singgigi
2.       Klaten 14.    Tuban 26.    Jakarta
3.       Sragen 15.    Jepara 27.    Kebumen
4.       Wonogiri 16.    Blora 28.    Pulo Gebang
5.       Boyolali 17.    Pacitan 29.    Lumajang
6.       Karanganyar 18.    Magelang 30.    Brebes
7.       Ngawi 19.    Wonosobo 31.    Gresik
8.       Magetan 20.    Lamongan 32.    Bangkalan
9.       Pati  21.    Sleman 33.    Rantauprapat Sumut
10.    Cilacap  22.    Gunungkidul 34.    Jember
11.    Grobogan 23.    Padangsimpuan  
12.    Temanggung 24.    Banjarnegara  
Internet Access Wi-Fi :                             16.5 %
Internet Credit :               83.5 %
Platform  Zoom:                                 7%
Gmeet:                               82.6%
Wa Group:                         94.1 %
Youtube :                           14.2%
Instagram:                        3.4%
Google Clasroom :            22.8%
Blogspot:                           3.8%
College Platform :            15.6%

From  Table 1 above, it can be seen that the respondents who filled out the questionnaire were dominated by female students  who made up more than three quarters of the total number.  All the students were from the education faculty, majoring, in subjects such as PAI, PBI, PGMI and TBI.

Their levels  were quite diverse,  ranging from levels or semesters 2, 4, 6 and 8. However, most of them  were at level 2. These students  came from various cities throughout Indonesia, but mostly from Central Java and East Java. In  accessingthe internet, they used internet credit more than Wifi.

Meanwhile, the platforms used in online learning  were  diverse,  but the ones that ranked highest  were Google meet and Whatsapp Group. This indicates that learning has been maximized through face-to-face online classes.

The hypotheses  in this study  are as follows:

H1. Parental support  for online learning at home significantly influences  student  satisfaction.
H2. Effectiveness of online learning at home significantly influences  student  satisfaction.
H3. Students’ independence  in online learning at home significantly influences  student  satisfaction.
H4. Student satisfaction significantly influences  their decisions  on online learning.
H5. Students’ online learning readiness from home  significantly influences  student  satisfaction.
H6. Commitment  to online learning at home significantly influences  student  satisfaction.
H7. Learning from home through Innovative-Creative online teaching methods  significantly influences  student  satisfaction.
H8.  Good support  for online learning at home significantly influences  student  satisfaction.

3. Results

a. Measurement Model Evaluation

In the process of data analysis, to meet the reliability and validity of the data, indicators that have a loading factor of 0.7 must be removed from the models, i.e  calculations and non-parametric testing with all indicators that have a loading factor of 0.7, Cronbach's at≥ 0.7, Composite Reliability at ≥ 0.7 and AVE  at ≥ 0.5 to assess convergent validity (Hair, Risher, Sarstedt  & Ringle, 2019). Co linearity testing is done by looking at the value of the variance inflation factor (VIF);  Burns and Burns (2008) stated that there is co linearity if the VIF value is 10.0.   However, (Hair, Hult, Ringle  & Sarstedt, 2014) recommend  a maximum cut off  at 5.0. The results of the reliability, validity and co linearity tests are presented in Table 2:

Table 2.  Measurement model & VIF.
Variables (code) Indicator
Outer Loading
CA
CR
AVE
VIF
Commitment (Com)
0.815
0.877
0.642
X2 Students maximize their  online learning 
0.787
1.656
X3 Students read the material   given by the  lecturer 
0.839
1.905
X4 Students re-read  and  understand the material that has been delivered 
0.840
1.838
X6 Students actively confirm that they are on a path that is truly seeking knowledge 
0.734
1.553
Independence (Ind)
0.79
0.864
0.614
X9 Students actively seek primary reading sources for ongoing courses without being asked 
0.814
1.867
X10 Students actively seek secondary/additional reading sources 
0.803
1.886
X11 Students actively re-read  the lecture material until they understand 
0.799
1.579
X12 Students try to activate the classroom atmosphere by asking and giving opinions 
0.713
1.306
Parent Support (PS)
0.807
0.865
0.563
X13 Parents fully support their children's study schedules outside  online learning 
0.723
1.551
X14 Parents do not order / give work  while their children are  engaged in online learning 
0.705
1.492
X15 Parents actively remind their children to  engage in online learning 
0.820
1.889
X17 Parents actively ask about all the needs of online learning, especially to support online learning 
0.759
1.732
X18 Parents never blame anything related to student online learning 
0.740
1.523
Main Source of Support (MSS)
0.758
0.892
0.805
X23 Students actively visit campus online libraries, national libraries or other online libraries 
0.899
1.593
X24 Students exchange books/e-books/journals with other students 
0.895
1.593
Learning Readiness 
0.801
0.870
0.627
X25 Students prepare themselves before online learning begins 
0.768
1.547
X26 Students actively read the material before the lecture starts 
0.846
1.921
X27 Students actively seek and prepare references that have been suggested by the lecturer 
0.817
1.820
X29 Students actively discuss material with friends outside the class schedule 
0.731
1.421
Innovative and Creative Teaching (ICT)
0.899
0.922
0.663
X31 Lecturers (in general) determine online media (Zoom, G-Meet, Whatsapp group, etc.)  through deliberation 
0.727
1.890
X32 Lecturers (in general) actively use various platforms for online learning media 
0.840
2.411
X33 Lecturers (in general) provide material in the form of Power-points/Material Modules/Blogs/Journals/E-books (minimum 4) 
0.769
2.095
X34 Lecturers (in general) are active in providing contextual discourse of the material being taught 
0.859
2.800
X35 Lecturers (in general) not only deliver material but also sometimes give quizzes  or motivation to lighten up the classroom atmosphere 
0.851
2.517
X36 Lecturers (in general) actively  conduct various ice-breaking activities.
0.830
2.347
Effectiveness (Eff)
0.875
0.909
0.667
X37 Online learning teaches students to be more independent  at managing time and studies  
0.769
1.814
X38 Online learning makes students more active in expressing their opinions 
0.858
2.858
X39 Online learning makes students learn not to be ashamed when they have an opinion to express 
0.848
2.719
X40 Online learning makes time to study science  unlimited 
0.824
2.214
X41 Online lectures make all college activities and home activities more organized and scheduled maximally 
0.780
1.851
Satisfaction (Sat)
0.820
0.893
0.736
Y1 Online learning makes students more qualified in terms of academics 
0.883
2.412
Y2 Online learning makes students more qualified from  a professional  viewpoint 
0.872
2.301
Y8 I would  tell anyone how good the quality of online learning is
0.817
1.494
Behavioral Intentions (BI)
0.929
0.943
0.701
Y9 Online learning is the right answer for a better education now and in the future for me 
0.788
2.374
Y11 I tell  people  that I get a lot of knowledge   through online learning 
0.823
2.394
Y12 I tell  people  that I enjoy online learning 
0.853
2.914
Y13 I tell  people  that online learning makes me more independent 
0.811
2.576
Y14 I will recommend to the public  online learning as  good and fun 
0.87
3.392
Y15 I will  tell the public that online learning makes  students creative in learning 
0.863
3.366
Y16 I will   tell the public that online learning improves and broaden the horizons of thinking 
0.848
3.031

Note: Unqualified variables have been excluded from model CR & Cronbach α ≤ 0.7, AVE ≤ 0.5,and VIF ≥  5.0.

b. Structural Model Evaluation

After assessing discriminant validity, the model must confirm that all constructs have significant differences. This study used the heterotrait-monotrait correlation ratio (HTMT) as suggested by  (Henseler, Ringle  & Sarstedt, 2015). HTMT is defined as the mean value of the correlations of items across constructs relative to the (geometric) mean of the mean correlations for items measuring the same construct. Discriminant validity problems arise when the HTMT value is high (Sarstedt, Ringle  & Hair, 2021).

Table 3.  Discriminant validity.
PS
BI
Eff
Ind
Sat
LLR
Com
ICT
MSS
PS
0.751
BI
0.313
0.837
Eff
0.438
0.613
0.817
Ind
0.346
0.352
0.463
0.784
Sat
0.316
0.731
0.639
0.367
0.858
LR
0.418
0.444
0.548
0.701
0.440
0.792
Com
0.390
0.349
0.483
0.730
0.432
0.640
0.801
ICT
0.338
0.331
0.507
0.468
0.352
0.590
0.454
0.814
MSS
0.367
0.322
0.362
0.452
0.312
0.548
0.466
0.356
0.897

The recommended value for HTMT is < 0.90 (Sarstedt et al., 2021). Each item in Table 3 has a value of < 0.90, so it can be concluded that the model used meets the requirements  of the discriminant validity test.

c. Predictive Accuracy and Relevancy

 Accuracy and relevance of predictions  were used to see how the independent variable affects the dependent variable. To determine the level of the predictive variable, the values of R2 and Q2 must be measured. To find the value of Q2 on Smart PLS, it is necessary to take additional steps using the Blindfolding calculation (Q2 = 1-SSE/SSO). Variables that have R2 of 0.75, 0.50 and 0.25 have a substantial (high), medium, and weak degree of analysis, while variables that have a Q2 value greater than 0, 0.25, and 0.50 describe small, medium and large predictive power. This will further clarify the prediction accuracy of the variables being tested.   

Figure 1.  Structural model with adjusted R2 values.

Table 4.  Predictive accuracy and relevancy.
Variables (code)
R2
R2 Adjusted
Q2
Effect Size
Predictive Accuracy
Satisfaction (Sat)
0.433
0.423
0.301
Weak
Medium
Behavioral Intention (BI)
0.535
0.534
0.369
Moderate
Medium

Table 5.  Hypothesis testing.
Path
SD
T-Statistics
P-Values
Decision
Parental Support - Satisfaction
0.051
0.029
0.977
Rejected
Effectiveness – Satisfaction
0.056
9.828
0.000**
Accepted
Independence – Satisfaction
0.068
0.875
0.382
Rejected
Satisfaction – Behavioral Intention
0.031
23.797
0.000**
Accepted
Learning Readiness – Satisfaction
0.074
1.050
0.294
Rejected
Commitment – Satisfaction
0.065
2.414
0.016**
Accepted
Innovative and Creative Teaching-Satisfaction
0.057
0.491
0.623
Rejected
Main Source of Support- Satisfaction
0.053
0.642
0.521
Rejected

Note: **p<0.01.

Figure 1 shows the results of structural testing with path coefficients and adjusted R2 values and Table 4 shows a summary of the results of predictive accuracy and relevance. Satisfaction (Sat) and behavioral intention (BI) have adjusted R2 values, respectively, of 0.423 (weak) and 0.534 (moderate) with the level of accuracy at the medium level. Table 5 shows the results of hypotheses testing, effectiveness and commitment proven to have an effect on satisfaction, while satisfaction is proven to have an effect on behavioral intention.

d. Importance-Performance Matrix Analysis (IPMA)

IPMA is used to identify factors that  are of significant importance for the development of a particular target construct, with low performance comparisons (Martilla & James, 1977). IPMA compares the total effect of the structural model on a particular target construct with the mean latent score variable of this construct's predecessor (Ringle & Sarstedt, 2016).  There is a need here to present the most important factors, considering that this study was conducted during the Covid-19 pandemic.  

Figure 2.  IPMA satisfaction (standardized effect).

Figure 3.  IPMA behavioral intention (standardized effect).

Figure 2 & Figure 3  show  the results of the IPMA test on Satisfaction (Sat) and Behavioral Intention (BI). Based on the IPMA test, it can be proven that effectiveness (Eff) is the most influential variable on satisfaction (Sat) compared  with the other variables (Commitment, Independence, Innovative and Creative Teaching, Learning Readiness, Main Source of Support  & Parental Support). Satisfaction (Sat) is the variable that has the most influence on Behavioral Intention (BI)  compared  with other variables in the model.

4. Discussion

 There were 412 respondents who  filled out the questionnaire.  The results  show that there are three variables that have a significant influence, namely student commitment, learning effectiveness  and the relationship between satisfaction and the intensity of student behavior that takes place in online learning.

 Examined  individually and in  depth, commitment is seen as one of the internal variables that exists in every student, which  in the activity  of online learning refers to the responsibility of students in lectures.  Factors that affect commitment are the level of self-awareness, student personality and student performance (Anghelache, 2013). There are three levels of commitment, namely low, medium and high (Glickman, 2002). From the results above, it can be seen that  students face various challenges  in online learning and they  have maximum responsibilities too, so that they are satisfied with the online learning which lasts for one year. The results of this study differ from the  notion  that satisfaction has an impact on commitment (Ranadewa, Gregory, Boralugoda, Silva  & Jayasuriya, 2021) as  in the present study, commitment  as a variable  is shown to have an impact on satisfaction.  The results of the study reveal that lecturer commitment in teaching has a significant effect on student satisfaction (Sopiah & Sangadji, 2020).

In addition, the effectiveness factor in itself is a factor,  though being external.  It is also shown to have the  closest  impact  on students  engaging in online learning effectively and efficiently. This is seen  in areas of time management, regularity, time allowance   to develop    potentials such as    reading,   completing assignments on time  and making online learning a  platform to  improve y during practice.  In short, satisfaction is attained when doubts are cleared. In the pre-covid-19 period, a study  showed that the effectiveness of online learning basically had the same impact as traditional learning or classroom learning  in general (Nguyen, 2015). However,  although the results were the same, online learning could not be fully initiated (Hussain, Saeed, & Syed, 2020).

Meanwhile, a research   identified   that teaching effective and positive strategies  resulted in good and fast learning outcomes (Raba, 2017). Furthermore, the overall effectiveness of online learning is based on everything that is received and done when students use online learning.  The results of a research in secondary schools in Romania confirmed that students react differently to online education, and their reactions are based on their proficiency in using online tools, their ability to technically access online courses  and the way instructors conduct learning activities (Butnaru, Niță, Anichiti  & Brînză, 2021).

 The last factor is student satisfaction, where this leads to the intensity of student behavior in using online learning both now and in the future.  Goals that lead to this intensity also refer  to learning outcomes that make them more qualified academically and professionally. Thus, it is  clear that the respondents or students in particular express their satisfaction  in  using online learning in the present and in the future.

The other variables rejected in this study indicate that the online learning journey provides different dynamics. However, this research has provided a maximum and comprehensive picture  as educational institutions  conduct online learning on an ongoing basis and students can already feel the various challenges that exist  when they participate in online learning.

5. Conclusions and Recommendations

The results of this study indicate that, first, parental support has no significant effect on student satisfaction in online learning;  second, effectiveness has a significant effect on student satisfaction in online learning;  third, independence has no significant effect on student satisfaction in online learning;  fourth, student learning readiness has no significant effect on students in online learning;  fifth, student commitment has a significant effect on online learning;  sixth, creativity and innovation  have no significant effect on student satisfaction in online learning;  seventh, the main source of learning support has no significant effect on student satisfaction in online learning and finally, student satisfaction affects the intensity of student behavior in online learning.

This research provides a complete picture where the online learning process has been carried out  comprehensively and optimally. In addition, it  reveals the new variables proposed in this study. The shortcoming in this study is that it is not directly related to the Covid-19 pandemic conditions that  exist in research settings which tend to have different impacts. Recommendations for further research  are to focus more on exploring and elaborating various other factors that may have a significant impact on student satisfaction. This will reveal the mediators that serve  to strengthen the variables of commitment, effectiveness and satisfaction itself.

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