Microsoft Word - Pramita.doc Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 34 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education Mitra Pramita1, R. Ati Sukmawati2, Harja Santana Purba3, Nuruddin Wiranda4, Jajang Kusnendar5, Mohd Samsu Sajat6 1- 4Faculty of Education and Teacher Training, Universitas Lambung Mangkurat, Indonesia 5Computer Science Education, Universitas Pendidikan Indonesia, Indonesia 6School of Computing, Universiti Utara Malaysia, Malaysia DOI: 10.23917/ijolae.v4i1.9265 Received: November 11st, 2019. Revised: August 20th, 2021. Accepted: September 1st, 2021 Available Online: December 24th, 2021. Published Regularly: January 1st, 2022 Abstract E-learning is an educational system that uses information technology in the learning process. One of the platforms that can be used in developing e-learning is Moodle. This research investigates the implementation of e-learning in the learning process in the Computer Education Department. The purpose of this research is to examine the use of E-learning in supporting the learning process. The respondents of this research are 130 active students of the Computer Education Department from the 2016 to 2018 Batches. The method used is a research questionnaire whose data are analyzed using the SPSS Statistics 25 program. This research shows no significant effect of student expectation on e-learning in supporting the learning process. Meanwhile, ease of e-learning, e-learning performance, and social influence for the benefit of e-learning have a significant effect on supporting the learning process and improve learning independence. Keywords: effectiveness, e-learning, moodle platform Corresponding Author: Mitra Pramita, Faculty of Teacher Training Education, Lambung Mangkurat University, Indonesia Email: mitrapramita92@ulm.ac.id 1. Introduction Nowadays, Information and Communication Technology (ICT) is growing increasingly rapidly. The development of IT makes people innovate to utilize it to ease their daily activities. Therefore, the need for a concept and mechanism for IT-based teaching and learning becomes inevitable. Thus, many aspects of human life are influenced by the development of information technology, one of which is related to the learning process in education. The current learning process is different from the old days where classes were held directly. With the development of IT thus, we can use many applications as supporting media for the development of learning in the classroom. One form of teach- ing media that is widely used today and uti- lizes technology (internet) is e-learning. E-learning is one type of teaching and learning that delivers teaching materials to students using the Internet, Internet, or other computer network media (Hartley, 2001). e- learning is learning arranged to use electron- ic or computer systems that are needed to support the learning process (Allen, 2016); it is delivered through electronic media, such as the Internet, Intranet, extranet, CD-ROMs, interactive TV, audio, video (Engelbrecht, Indonesian Journal on Learning and Advanced Education http://journals.ums.ac.id/index.php/ijolae Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 35 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education 2005); and it can also be delivered by any electronic media other than web-based media (Alavi & Leidner, 2001; Hiltz & Turoff, 2005). Whether an application, object, pro- gram, website, etc., can ultimately provide learning opportunities for individuals (Moore, etc.,2011). E-learning offers many media-based, student-centered, and interac- tive learning opportunities that support active learning (Huffaker and Calvert, 2003; etc., 2004). Nowadays, the world community has widely accepted the concept of e-learning, as evidenced by the increasing implementation of e-learning, especially in educational insti- tutions such as schools, training, and univer- sities (Setyoningsih, 2015; Lee, etc., 2009). As a result, e-learning has become a necessi- ty for academics, such as teachers, students, and educational institutions, to use computer technology in teaching and learning activi- ties. According to Salim, the critical factors for e-learning acceptance are grouped into four categories: instructors, students, information technology, and university support (Salim, 2005). E-learning is an educational system that uses information technology in the learning process. Based on the research (Aparicio, Bacao & Oliveira, 2017), it was revealed that the students who used an online learning system (e-learning) could increase producti- vity and facilitate their tasks. Therefore, the use of online learning has a positive impact on the overall success of the university. In addition, this research (Marfuatun et al, 2013, Sukmawati et al, 2020) reported that the level of implementation of the online cooperative learning method was quite good. However, it still needed adequate support for both computers and e-learning servers. The concern of developing this e-learning system is not only to bring material into a digital form that is uploaded on the webserver but also to prioritize the principle of learning and to think about the effects or responses that come from users, whether the design that has been developed will not make them boring of accessing the e-learning system (Hidayati, 2016). Electronic learning or e-learning has been started in the 1970s (Waller and Wil- son, 2001). Lambung Mangkurat University has implemented e-learning beginning from the 2017/2018 academic year in the Even Semester. This e-learning has been devel- oped and used as a form of learning recog- nized by university leaders. At present, the application of e-learning at Lambung Mangkurat University is implemented in an integrated manner through Sistem Informasi Universitas Lambung Mangkurat Terin- tegrasi (SIMARI) or the Integrated Lambung Mangkurat University Information System. Learning through SIMARI is an effort to support various activities of lecturers, stu- dents, and all interested parties, especially related to learning activities that include the delivery of learning and task assessment ma- terials, leading to the effectiveness and im- provement of the quality of learning process- es and outcomes. This research focuses on the use of E- learning using the Moodle platform develo- ped by Lambung Mangkurat University. The purpose of this research is to determine the effect of using e-learning in supporting the learning process. The formulation of the pro- blems is as follows: (a) How is the student acceptance of the ease of e-learning in sup- porting the learning process; (b) How is the performance of e-learning in supporting the learning process; (c) How is the expectation for the use of e-learning in supporting the learning process; and (d) How is the social influence on the use of e-learning in suppor- ting the learning process. Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 36 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education 2. Method References must accompany the method used, relevant modifications should be explained. Data analysis procedures and techniques should be emphasized in the library review article. The stages of research must be clearly stated. This survey research is conducted by collecting information from the respondents through a questionnaire (Cholid & Achmadi, 2007). The method used is a quantitative analysis research method, namely, using in- depth data analysis in the form of numbers (Istijanto, 2005). The researchers distributed the questionnaire electronically in the form of Google Form. The scale used in this ques- tionnaire was a 4-point Likert scale. The population in this research was the Students of Undergraduate Program in Com- puter Education, Lambung Mangkurat Uni- versity, 2016-2018 Batches. The total was 192 students. Samples are part of the popula- tion (Sekaran & Bougie, 2017). In this rese- arch, the samples taken are based on the Slo- vin formula as follows. Information n = number of samples N = population d = the precision value of 95% or sig.= 0.05 The sampling technique in this research was purposive sampling. Since there were 192 total students in the population, thus the number of samples taken was 130 students. The data analysis technique used was validity test, reliability test, classical assump- tion test, multiple linear regression analysis, and hypothesis testing. The validity and reli- ability tests were used to measure whether the questionnaire made was valid and relia- ble. If the questionnaire was declared valid and reliable, the questionnaire could be used. Data for validity and reliability tests were taken from 32 different students and then processed with SPSS 25. SPSS is widely used by other researchers such as Byoung- Chan Lee (Lee etc, 2009), Din Jong (Jong et al, 2009), Al-Adwan (Al-Adwan et al, 2013), Sebjan (Sebjan, 2015), Masood (Masood et al, 2016), and Nassuora (Nassuora, 2013). The next was a multiple linear regressi- on analysis tests with the sample data of 130 students. Before the multiple linear regressi- on analysis tests, the classical assumption test was done first to get the best results (Ghozali, 2006). The purpose of this classi- cal assumption was to avoid the bias of the independent variable as an estimator of the dependent variable. Afterward, hypothesis testing was conducted. In this research, the operational defini- tions of the research variables and measure- ment scale can be seen in Table 1. Table 1. Research Variables, Definitions, and Measurement Scale Variable Operational Definition Scale Ease of E-learning (X1) (Park,2009) Acceptance of ease is related to one's belief that using a parti- cular system will make the person effortless (free from extra efforts) (Davis, Bagozzi, & Warshaw, 1989). In this case, perception of ease describes the e-learning that can ease stu- dents in doing several things related to the learning process. Therefore, the importance of easy familiarity with e-learning is important for students (Bringman‐Rodenbarger, 2020). Likert E-learning Performance (X2) Performance expectancy is related to the benefits or ease ob- tained in work using a particular system (Venkatesh, Morris, Davis & Davis, 2003). In this case, performance illustrates the Likert Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 37 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education Variable Operational Definition Scale benefits obtained from e-learning in conducting the learning process. 3. Result and Discussion a. Implementation Learning Platform There are six main features on this plat- form which are as follows: 1. Dashboard The Dashboard feature (Figure 1) of the e-learning is students who are close to some of these classes. The dashboard displays all classes taught by lecturers and taken by stu- dents. Figure 1. Dashboard 2. My Courses In My Courses (Figure 2), lecturers can send materials, assignments, quizzes, and announcements that will appear when stu- dents open the class. The material that has been given is in the form of text, audio, or video. This feature represents the material presented in lectures by the lecturer when he is in a conventional class. Figure 2. My Courses 3. Classmates In this feature, lecturers and students or students with other students can interact di- rectly by adding to the existing Forum fea- ture to exchange information (Figure 3). This feature represents discussion and question and answers when the class is conventional. Figure 3. Classmates 4. Participants Contains the names of students who took the course, and the lecturer can see when the students access the class (Figure 4). This feature represents a written attendance list as in conventional classrooms. Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 38 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education Figure 4. Participants 5. Grades Grades (Figure 5) in the e-learning can be used to view and rate the assignments given at each meeting. Usually, Grades can also be used to remind the deadlines of as- signments given by the lecturer in question. This feature represents the activities of the lecturer assessing the conventional class. Figure 5. Grades 6. Assignments This feature can be used to view and re- mind the assignments that must be immedi- ately collected according to the maximum limit set by the lecturer. After the assign- ments are submitted, the lecturer can check and assess the assignments, then distribute them back to students (Figure 6). This fea- ture represents the activities of the lecturer giving assignments to conventional classes. Figure 6. Assignments b. Validity and Reliability Tests The validity test was applied to find out whether the existing indicator measurement showed what should be measured or not. The validity test in this research was carried out by looking at the value of the degree of free- dom. With data from 32 students and a signi- ficance value of 0.05, the r-value obtained was 0.349. Further, the Pearson Product Moment was employed to test the validity to obtain the Pearson correlation value for each indicator. The results obtained indicated that all indicators of each variable had a Pearson correlation value of > 0.349 (r-value); thus, it was declared valid and could be used as the next instrument. A variable can be considered good (shows consistent result) if it has a Cron- bach's Alpha value of more than 0.6. This research showed that overall variables, na- mely ease of e-learning, e-learning perfor- mance, e-learning expectation, social influ- ences on e-learning and learning, had Cron- bach's Alpha values of 0.970, more than 0.6. These results indicated that the reliability was good, meaning that further statistical analysis could be carried out. c. Classical Assumption Test 1. Normality Test The normality test aims to determine the level of normality of the distribution of sam- ples studied. For example, the skewness ratio shows a statistical value of -0.417 with a standard of error of 0.212, meaning that the Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 39 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education skewness value is -1.96. Meanwhile, the kur- tosis ratio shows a statistical value of 0.713 with an error standard of 0.422, which means the kurtosis value is 1.69. To conclude, the data distribution was normal because the value of skewness and kurtosis were between -2 and 2. In other words, further statistical analysis can be done. 2. Multicollinearity Test The multicollinearity test aims to deter- mine whether the regression model has a strong correlation between independent vari- ables. A good regression model should not correlate with the independent variables. In this research, the value of each independent variable has fulfilled the requirement that there is no multicollinearity, namely the tole- rance value is > 0.1, and the VIF value is <10. The multicollinearity test results are presented in Table 2. Table 2. Multicollinearity Test Results Model Collinearity Statistics Tolerance VIF Ease of e-learning 0.275 3.641 E-learning Performance 0.353 2.829 The expectation on the use of e-learning 0.361 2.770 Social Influence on the use of e-learning 0.457 2.189 3. Heteroscedasticity Test The heteroscedasticity test aims to test whether variance inequality occurs in one residual of one observation to another obser- vation in the regression model. A good re- gression model is a model having no symp- toms of heteroscedasticity. The present rese- arch results show that the significant values obtained by all independent variables in the regression model are > 0.05. These results prove that in the regression model, there is no symptom of variance inequality or hete- roscedasticity. These results follow the hete- roscedasticity theory studied by previous researchers (Long and Ervin, 2000; Muller and Stadtmuller, 2007). 4. Multiple Linear Regression Analysis Multiple linear regression analysis in this research is used to find out how student acceptance of the ease of e-learning, e- learning performance, student expectation for the use of e-learning, and social influence on the use of e-learning in the learning pro- cess in the Computer Education Department. Table 3. shows the multiple linear regression test results. Based on the calculation results using SPSS, here is the result of the multiple linear regression equation obtained. These results are from previous researchers' linear regression theory (Long and Ervin, 2000; Muller and Stadtmuller, 2007). Table 3. Multiple Linear Regression Test Results Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF (Constant) 2.750 0.985 2.791 0.006 Ease of e-learning 0.373 0.083 0.424 4.487 0.000 0.275 3.641 E-learning Perfor- 0.815 0.163 0.416 4.993 0.000 0.353 2.829 Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 40 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF mance The expectation on the use of e-learning - 0.176 0.135 -0.107 -1.301 0.196 0.361 2.770 The following is the interpretation of the regression above: At constant value, the results of the ease of e-learning, e-learning performance, and social influence on the use of e-learning variables are positive, meaning that higher the levels of ease, performance, and social influence of e-learning, the more effective is the effect on the learning process. Meanwhile, the results of expectation for the e-learning variable are negative, meaning that the higher the expectation for the use of e-learning, the less is the effect on the lear- ning process. 5. Hypothesis testing The hypothesis testing is done to exam- ine the extent to which the effect of one in- dependent variable individually explains the variation of the dependent variable. The re- sults are displayed in Table 4. Table 4. T-Test Results Model t Sig. Ease to use of e-learning (Selim, 2002) (Elkaseh et al, 2016) (Nayanajith etc, 2019) 4.487 0.000 E-learning Performance (Venkatesh, 2001; Venkatesh and Davis, 2000) 4.993 0.000 The expectation for the use of e-learning (Paechter etc., 2010) (Sørebø, 2009) (Lee, 2010) -1.301 0.196 Social Influence on the use of e-learning (Montgomery, 1996) (Hwang,2014) 2.238 0.027 Whether or not there is an effect of in- dependent variables on dependent variable individually is seen based on this require- ment, namely if Tcal. > T-table or the signi- ficance value < 0.05. In this research, T-table with a significance level of 0.05 and Degree of Freedom with the provision = N-2 or 130 = 130 - 2, the result is 1.979. Thus, this pre- sent research found that the ease of e- learning, e-learning performance, and social influence on the use of e-learning variables significantly affect the learning process vari- able. Meanwhile, the expectation for using the E-learning variable individually does not significantly affect the learning process vari- able. • H1: Student acceptance of the ease of E-learning has a positive effect in su- pporting the learning process. Based on Table 4, the t-table value is greater than t-statistics, which indicates the student acceptance of the ease of e-learning has a significant effect in supporting the learning process. Therefore, the first hypothesis stating"Student acceptance of the ease of e-learning has a positive effect in supporting the learning process," is proven and supported by data. In this research, it can be said that the ease of e-learning can support the learning process because e-learning is very clear and easy to learn and use. Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 41 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education If there are students who miss the class, they can still see announcements and materi- als. In addition, task submissions can be ma- de more flexibly, and students can access the materials before the class through e-learning. • H2: E-learning Performance has a positive effect in supporting the lear- ning process Based on Table 4, the t-table value is smaller than the t-statistics, indicating that e- learning performance significantly supports learning. Thus, the second hypothesis, which states “e-learning Performance has a positive effect in supporting the learning process,” is proven and backed by data. Furthermore, the results are in line with the research conduc- ted by Madigan, Louw, Wilbrink, Schieben & Merat (2017), who stated that the perfor- mance of the ARTS vehicle was proven to affect its users to use the system due to ARTS vehicle can make the users easier to get transportation with the appropriate or desired destination effectively and efficien- tly. In this research, it can be said that e- learning performance can support the lear- ning process because e-learning can facilitate the students to store documents such as ma- terials and assignments sent through e- learning. When the students do not have much time to print the documents required, they can access the materials first through their own e-learning. • H3: Expectation for the use of E- learning has a positive effect in sup- porting the learning process Based on Table 4, the t-table value is greater than the t-statistics, meaning that student expectation for the use of e-learning has no significant effect in supporting the learning process. Thus, the third hypothesis, which states, “Expectation for the use of e- learning has a positive effect in supporting learning,” is not proven, which is supported by data. This is in line with research conduc- ted by (Madigan, Louw, Wilbrink, Schieben & Merat, 2017), who reported that expecta- tion for ARTS vehicles had no significant effect. This is possible because using the ARTS Vehicle system is no different from the use of public transportation in general. In this research, it can be said that stu- dent expectation for the use of e-learning does not affect the learning process. Howe- ver, when lecturers give group assignments through e-learning, facilities from e-learning are less able to accommodate group assign- ments online. In addition, when the lecturers give announcements or assignments, students must open the e-learning site regularly be- cause there are no direct incoming notifica- tions in real-time, making it ineffective and inefficient. • H4: Social Influence on the use of e- learning has a positive effect in sup- porting the learning process As seen in Table 4., the t-table value is smaller than the t-statistics, indicating that the social influence on the use of e-learning has a significant effect in supporting the le- arning process. Thus, the fourth hypothesis, “Social Influence on the use of e-learning has a positive effect in supporting the lear- ning process," is proven and supported by data. This is in line with Mustaqim, Kusyanti & Aryadita's (2018) research related to social influences that affected one's intention to use XYZ e-commerce. Therefore, in this research, it can be de- clared that social influence affects students using e-learning. The social influence comes from lecturers, teaching assistants, friends, and campus environments that support the use of the system. When the lecturers use e- learning, students will use it too because it Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 42 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education can support the learning process, such as the ease of access to get materials, submit assig- nments, check student scores from the cor- rected tasks, and get direct feedback from the lecturer faster and easily. Thus, it can answer the educational challenge of the speed and demand in which lecturers provide feedback. 4. Conclusion There are some conclusions drawn in this research as follows. First, student ac- ceptance of the ease of e-learning has a sig- nificant effect in supporting the learning pro- cess. Second, e-learning has a significant effect in supporting the learning process. Third, student expectation for e-learning has no significant effect in supporting the learn- ing process. Fourth, social influence on the use of e-learning has a significant effect in supporting the learning process. Finally, for the development of e-learning advanced, it is expected that there be smartphone notifica- tions regarding the material and assignments posted by the teacher. With features such as dashboards, my courses, classmates, partici- pants, grades, and assignments, it represents the activities of the class. 5. References Alavi, M., & Leidner, D. (2001). Research commentary: Technology mediated le- arning-a call for greater depth and bre- adth of research. Information Systems Research, 12(1), 1–10. Allen, M. W. (2016). Michael Allen's guide to e-learning: Building interactive, fun, and effective learning programs for any company. John Wiley & Sons. https://books.google.co.id/books?hl=id& lr=&id=7ibWBgAAQBAJ. Aparicio, M., Bacao, F., & Oliveira, T. (2017). Grit in the path to e-learning success. Computers in Human Beha- vior, 66, 388-399. Bringman‐Rodenbarger, L., & Hortsch, M. (2020). How students choose E‐learning resources: The importance of ease, fami- liarity, and convenience. FASEB Bio- Advances, 2(5), 286-295. Cholid, N., & Achmadi, A. (2007). Metodo- logi Penelitian. Jakarta: Bumi Aksara. Christidamayani, A. P., & Kristanto, Y. D. (2020). The Effects of Problem Posing Learning Model on Students' Learning Achievement and Motivation. arXiv preprint arXiv:2002.04447. Indonesian Journal on Learning and Advanced Edu- cation (IJOLAE), 2(2), 100-108 Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theore- tical models. Management Scien- ce, 35(8), 982-1003. http://www.jstor.org/stable/10.2307/263 2151. Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and per- ceived usefulness of social media for e- learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192. Engelbrecht, E. (2005). Adapting to chan- ging expectations: Postgraduate stu- dents’ experience of an e-learning tax program. Computers and Education, 45(2), 217–229. Ghozali, I. (2006). Aplikasi Analisis Multi- variate Dengan Program SPSS, Badan Penerbit Universitas Diponegoro, Sema- rang., 2011. Aplikasi Analisis Multivari- ate dengan Program IBM SPSS, 19. Hartley, D. E. (2001). Selling e-learning. American Society for Training and De- velopment. https://books.google.co.id/books?hl=id& lr=&id=jcnh8Vcw0-IC. Hidayati, N. (2016). Sistem E-learning Un- tuk Meningkatkan Proses Belajar Men- gajar: Studi Kasus Pada SMA Negeri 10 Bandar Lampung. Telematika MKOM, 2(2), 153-170. https://journal.budiluhur.ac.id/index.php /telematika/article/view/171/165. Hiltz, S. R., & Turoff, M. (2005). Education goes digital: The evolution of online le- Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 43 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education arning and the revolution in higher edu- cation. Communication of ACM, 48(10), 59–64 https://doi.org/10.1016/j.chb.2016.10.009. Huffaker, D. A., & Calvert, S. L. (2003). The new science of learning: Active learning, metacognition, and transfer of knowled- ge in e-learning applications. Journal of Educational Computing Research, 29(3), 325-334. Hwang, Y. (2014). Understanding social influence theory and personal goals in e- learning. Information Development, 32(3), 466-477. Istijanto, M. M. (2005). Aplikasi Praktis Ri- set Pemasaran. Jakarta: Gramedia Pus- taka Utama. Jong, D., & Wang, T. S. (2009). Student ac- ceptance of web-based learning system. In Proceedings. The 2009 International Symposium on Web Information Sys- tems and Applications (WISA 2009) (p. 533). Academy Publisher. Kang, M., & Shin, W. S. (2015). An empiri- cal investigation of student acceptance of synchronous e-learning in an online university. Journal of Educational Com- puting Research, 52(4), 475-495. Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320- 1329. Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320- 1329. Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e- learning: An extension of the expecta- tion–confirmation model. Computers & Education, 54(2), 506-516. Long, J. S., & Ervin, L. H. (2000). Using heteroscedasticity consistent standard er- rors in the linear regression model. The American Statistician, 54(3), 217-224. Madigan, R., Louw, T., Wilbrink, M., Schie- ben, A., & Merat, N. (2017). What in- fluences the decision to use automated public transport? Using UTAUT to un- derstand public acceptance of automated road transport systems. Transportation research part F: traffic psychology and behaviour, 50, 55-64. Marfuatun, M., LFX, E. W., & Suwardi, S. (2013). Pengembangan Metode Pembe- lajaran Kooperatif secara Online pada Kuliah Kimia Fisika II. Jurnal Pendi- dikan Matematika dan Sains, 1(2), 125- 133. https://journal.uny.ac.id/index.php/jpms/ article/view/2479/2066. Masood, A., & Lodhi, R. N. (2016). Deter- minants of behavioral intentions to use SPSS among students: Application of Technology Acceptance Model (TAM). FWU Journal of Social Sciences, 10(2), 146. Montgomery, M. R., & Casterline, J. B. (1996). Social learning, social influence, and new models of fertility. Population and Development Review, 22, 151-175. Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). e-learning, online learning, and distance learning environments: Are they the same?. The Internet and Higher Education, 14(2), 129-135. Muller, H. G., & Stadtmuller, U. (1987). Estimation of heteroscedasticity in re- gression analysis. The Annals of Statis- tics, 15(2), 610-625. Mustaqim, R. N., Kusyanti, A., & Aryadita, H. (2018). Analisis fak- tor-faktor yang memengaruhi niat penggunaan e-commerce XYZ men- ggunakan model UTAUT (Unified Theory Acceptance and Use of Technology). Jurnal Pengembangan Teknologi Informasi dan Ilmu Kompu- ter, 2(7), 2584-2593. Nassuora, A. B. (2012). Students acceptance of mobile learning for higher education in Saudi Arabia. American Academic & Scholarly Research Journal, 4(2), 24-30. Nayanajith, G., Damunupola, K. A., & Ven- tayen, R. J. (2019). Impact of Innovation and Perceived Ease of Use on E-learning Adoption. Asian Journal of Business and Technology Studies, 2(1), 19-27. Indonesian Journal on Learning and Advanced Education (IJOLAE)| p-ISSN 2655-920x, e-ISSN 2656-2804 Vol. 4 (1) (2022) 34-44 44 Student Acceptance of E-learning to Improve Learning Independence in the Department of Computer Education Paechter, M., Maier, B., & Macher, D. (2010). Students' expectations of and experiences in e-learning: Their relation to learning achievements and course sa- tisfaction. Computers & Education, 54(1), 222-229. Park, S. Y. (2009). An analysis of the technology acceptance model in unders- tanding university students' behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150-162. Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396-413. Šebjan, U., & Tominc, P. (2015). Impact of support of teacher and compatibility with needs of study on the usefulness of SPSS by students. Computers in Human Behavior, 53, 354-365. Sekaran, U., & Bougie, R. (2017). Metode Penelitian untuk Bisnis: Pendekatan Pengembangan-Keahlian. Jakarta Sela- tan. Penerbit Salemba Empat. Selim, H. M. (2003). An empirical investiga- tion of student acceptance of course websites. Computers & Education, 40(4), 343-360. Setyoningsih, S. (2015). E-learning: Pembe- lajaran Interaktif Berbasis Teknologi In- formasi. Elementary: Islamic Teacher Journal, 3(1). http://journal.stainkudus.ac.id/index.php /elementary/article/viewFile/1443/1319. Sukmawati, R. A., Pramita, M., Purba, H. S., & Utami, B. (2020). The use of blended cooperative learning model in the intro- duction to digital systems learning. In- donesian Journal on Learning and Ad- vanced Education (IJOLAE), 2(2), 75- 81. Sørebø, Ø., Halvari, H., Gulli, V. F., & Kris- tiansen, R. (2009). The role of self- determination theory in explaining tea- chers’ motivation to continue to use e- learning technology. Computers & Edu- cation, 53(4), 1177-1187. Venkatesh, V. (2001). Determinants of per- ceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. In- formation Systems Research, 11(4), 342–365. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal fi- eld studies. Management Science, 46, 186–204. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478. Waller, V. and Wilson, J. (2001). A defini- tion for e-learning. TheODL QC News- letter, pp. 1-2. Wingard, A. K., Hermawan, H. D., & Dewi, V. R. (2020). The Effects of Students’ Perception of the School Environment and Students’ Enjoyment in Reading towards Reading Achievement of 4th Grades Students in Hong Kong. Indone- sian Journal on Learning and Advanced Education (IJOLAE), 2(2), 68-74. Zhang, D., Zhao, J. L., Zhou, L., & Nu- namaker, J. F. Jr., (2004). Can e-learning replace classroom learning? Communi- cations of the ACM, 47(5), 75–79