Australasian Journal of Educational Technology, 2022, 38(6). 121 The adoption of blended learning using Coursera MOOCs: A case study in a Vietnamese higher education institution Nguyen Thi Thao Ho Universiti Teknologi PETRONAS, Malaysia; FPT University, Vietnam Hiep-Hung Pham Phu Xuan University, Vietnam; Thanh Do University, Vietnam Subarna Sivapalan Universiti Teknologi PETRONAS, Malaysia Viet-Hung Dinh University of Labour and Social Affairs, Vietnam This research is unique to a Vietnamese higher education institution that adopted blended learning using Coursera MOOCs. Employing the service quality model, the objective was to investigate factors affecting the continuance intention and recommendation to others towards blended learning using Coursera MOOCs. This study was conducted an online survey with 637 students across four campuses of a Vietnamese higher education institution. The results of the structural equation modeling showed that the learning content and online responsiveness increased satisfaction with Coursera MOOCs whereas online reliability did not affect satisfaction with Coursera MOOCs. There were also positive relationships between empathy, tangibles, classroom responsiveness, and classroom activities. Assurance and classroom reliability had no significant impacts on classroom activities. Satisfaction and classroom activities positively influenced the continuance intention towards blended learning using Coursera MOOCs. Lastly, satisfaction, classroom activities, and continuance intention significantly affected the recommendation to others towards blended learning using Coursera MOOCs. Implications for practice or policy: • A case study process for evaluating the quality of the blended learning using Coursera MOOCs is detailed. • Practical recommendations are made for curriculum development, teaching and learning, assessment, and professional development as universities implement the blended learning using MOOCs. Keywords: blended learning, Coursera MOOCs, offline mentoring, service quality model, higher education institutions Introduction Blended learning is seen as the way forward for higher education. Teachers need to meet a variety of students' learning needs, scaffold learning processes, and promote active, reflective, and collaborative learning by employing a blended learning mode (Hoang, 2015). Therefore, higher education systems across the world, including Vietnamese higher education institutions, have developed and implemented blended learning models that aim to enhance learning activities in face-to-face classrooms and personalise the online learning for students (Tang & Tien, 2020). Vietnamese higher education institutions have employed a model of blended learning using Coursera MOOCs since September 2019. In this model, students must pass the required courses on the Coursera platform to obtain certificates, and be eligible to sit the offline final exams offered by the higher education institution. Importantly, the model of blended learning using Coursera MOOCs incorporates organisation and management of the self-learning skills of students, which can assist in the development of students’ lifelong learning skills. There is little research on the implementation of a model of MOOCs blended with offline mentoring. Further, few empirical studies have assessed the quality of blended learning. Therefore, this study was conducted to investigate how the quality of Coursera MOOCs and offline mentoring affect the students' Australasian Journal of Educational Technology, 2022, 38(6). 122 continuance intention and willingness to recommend to others to study at a higher education institution. This study collected data from four higher education institution campuses across Vietnam: Hanoi, Ho Chi Minh City, Da Nang, and Can Tho. Literature review Blended learning methods allow students to gain technical expertise through online videos deployed on learning management systems, and then use those materials in the classroom (Wibawa & Kardipah, 2018). Students benefit from a strong cyber infrastructure that can accommodate the use of technologies such as videoconferencing, live streaming, and MOOCs (Malaysia Ministry of Education, 2015). The quality of blended learning has not been widely investigated. There is little research on the models of MOOCs used for blended learning generally, and the model of blended learning using Coursera MOOCs particularly. Very few universities use the learning materials from the well-known MOOC provider, Coursera, and blend them with offline mentoring in traditional classrooms. MOOCs for blended learning There have been several studies on the advantages of blended learning using MOOCs. Bruff et al., (2013) mentioned that a model was piloted in which a Coursera MOOC, Machine Learning, was integrated within a graduate machine learning course at Vanderbilt University in USA. The results of Bruff et al. (2013) noted the students provided positive feedback with this model as they might develop their self-paced learning skills.. Loviscach (2013) noted that well-known universities offered high-quality courseware to students. Noticeably, blended MOOCs allow instructors more time for classroom activities including discussions and problem solving (Estévez-Ayres et al., 2015). Bralić and Divjak (2018) stated that blended learning using MOOCs could be useful in providing high-quality learning experiences that are both affordable and suitable for culturally diverse students. Last but not least, effective offline teaching combined with MOOCs can assist in raising the educational standards and quality of education in India (Virani et al., 2020). The concept of service quality in blended learning in higher education There are different definitions of service quality in the previous studies. Juran (1988) gave an early definition of service quality as achieving customer expectations, whereas Zeithaml (1988) described it as superiority or excellence in service delivery. Service quality refer to a customer's comparison of service expectations with perceptions of what is actually delivered by the service provider (Parasuraman et al., 1988). From a service quality viewpoint of higher education, students are the direct clients, so the primary responsibility is to meet students’ needs. There are few studies on the service quality of blended learning. Notably, Moore (2012) identified five pillars of quality: (1) learning effectiveness, (2) access, (3) cost-effectiveness and institutional commitment, (4) faculty satisfaction, and (5) student satisfaction, to enable stakeholders to ensure instructional quality in blended learning. Blended learning in widely used in Vietnamese higher education institutions, however there is a need to develop a set of quality criteria for online assessments, examinations, and student involvement (Pham & Ho, 2020). MOOC platforms Siemens and Downes (2008) created the first MOOC in 2008 called “Connectivism and Connective Knowledge” based on their ideas. Later, Daphne Koller and Andrew Ng founded Coursera as an independent for-profit technology in early 2012. In the same year, Udacity and Udemy were established. In February 2012, MIT and Harvard merged their MITx platforms into EdX. FutureLearn is another digital education platform, created in December 2012 and operated by the Open University of the United Kingdom (Chen, 2020). Australasian Journal of Educational Technology, 2022, 38(6). 123 Blended learning in the Vietnamese higher education institution: The model of blended learning using Coursera MOOCs In this study, the board of management of the Vietnamese higher education institution has evaluated the benefits of Coursera MOOCs for students as being to: (1) acquire the most up-to-date content from renowned universities’ instructors; (2) enhance English, self-learning, and digital skills of students; and (3) achieve worthy specialisation certificates issued by Coursera (Anh et al., 2019). Simultaneously, the board of management has also assessed the benefits of Coursera MOOCs for the studied Vietnamese higher education institution as the: (1) implementation of digital transformation; (2) enhancement lecturers' receptiveness to novel content and instructional approaches; (3) optimisation of learning materials; and (4) pioneering to gain competitive advantage over other higher education institutions (Anh, et al., 2019). A model of blending learning using Coursera MOOCs has been officially employed at the higher education institution in this study since September 2019. Students from information technology, business administration, graphic design, linguistics, multimedia communication, and hospitality management programs must take a Coursera MOOC each semester. Students work through the Coursera MOOCs at their own pace over 13 to 15 weeks. Coursera instructors work not only as facilitators but also as developers. Coursera MOOCs are given on mass for learners across the world, therefore, individual support for the students of the Vietnamese higher education institution in this study many not be synchronous. Further, MOOCs lack direct student-lecturer interaction and student-student interaction compared with the face-to- face classrooms (Quora, 2017). Consequently, students have still had five offline mentoring sessions with their lecturers at this Vietnamese higher education. These Vietnamese higher educations’ lecturers are mentors who guide, advise, and support their students on the matters related to Coursera MOOCs. It is necessary that students receive Coursera certificates to be qualified for the final exams. Above all, this Vietnamese higher education institution has to conduct offline final exams at the end of the semester to ensure the quality of student’s learning on both Coursera MOOCs and offline mentoring sessions. Proposed model and hypothesis development As teaching and learning activities in higher education undergo substantial changes, it is critical to understand the impact of e-learning service quality. Evaluating e-learning service quality is necessary. Previous studies by Pham et al. (2019) and Uppal et al. (2018) employed the SERVQUAL model to assess the quality of the e-learning service. The SERVQUAL model of Parasuraman et al. (1988) identified five dimensions of service quality: tangibles, reliability, responsiveness, assurance, and empathy. Physical facilities, equipment, and personnel are referred to as tangibles. Reliability means that the provider can reliably and accurately provide the promised service. The willingness of the supplier to assist and provide prompt service is referred to as responsiveness. Assurance is a term that refers to an employee's knowledge, courtesy, and capacity to inspire trust and confidence. Empathy is a term that refers to the caring and personalised attention that a service firm delivers to its clients (Parasuraman et al.,1988). Therefore, we select the SERVQUAL model to investigate service quality factors affecting students’ continuance intention and recommendation to others towards blended learning using Coursera MOOCs. To extend service factors, we added the dimension of learning content, which refers to the prompt provision of correct learning materials for students (Uppal et al., 2018). Coursera MOOCs quality and satisfaction In previous research investigating the quality of e-learning and MOOCs, Lin (2007) noted that system quality, information quality, and service quality, made e-learning systems successful in Taiwan. The growing conversation around MOOCs reflect concerns about platform service quality and what MOOCs should offer in addition to an underlying online learning platform to improve service quality (Safri & Hanafiah, 2020). MOOC quality can be considered and measured in two dimensions, service quality and information quality. Service quality in this context also involves in the quality of the MOOC’s delivery by instructors, the quality of support from information technology staff of the MOOC provider, and the quality of classroom activities from mentors of from the higher education institution (Albelbisi et al., 2021; Machumu & Musabila, 2018). Australasian Journal of Educational Technology, 2022, 38(6). 124 Satisfaction is the result of comparing what has been obtained to what was expected, and consists of the purchase decision, and the needs and wants associated with the purchase (Otaibi & Yasmeen, 2014). Noticeably, the Delone and McLean (2003) found both information quality and service quality are associated with user satisfaction. Each service quality factor contributes differently to the overall quality of e-learning services, which in turn affects e-learning student satisfaction (Pham et al., 2019). Therefore, the following hypotheses are formulated for this study: H1. Learning content positively affects the student satisfaction in Coursera MOOCs. H2. Online reliability positively affects the student satisfaction in Coursera MOOCs. H3. Online responsiveness positively affects the student satisfaction in Coursera MOOCs. Offline mentoring quality and classroom activities In this study, mentoring is defined as a relationship between the mentor and the mentee in which the mentor supports the mentee to promote knowledge and skill development based on their own experience (Panopoulos & Sarri, 2013). The quality of offline mentoring is measured by the dimension of service quality. Service quality in offline programs might be considered as the support offered from the mentor (Albelbisi et al., 2021). It is noted that some MOOCs do not offer mentors, coaches, and guides for students and this leads to lack of opportunities for engagement and connection between students and MOOC instructors (Safri & Hanafiah, 2020). Ultimately, students still need offline mentoring to deal with MOOC issues. Classroom activities are activities offered to students to strengthen their learning through mentor interaction and peer interaction (Chow & Shi, 2014). Importantly, mentoring, like teaching, works best when it is tailored to the requirements of the mentee. Hobson (2012) noted that there might be a relationship between classroom activities and offline mentor quality. Our further research hypotheses are: H4. Empathy positively affects the classroom activities in offline mentoring. H5. Assurance positively affects the classroom activities in offline mentoring. H6. Classroom reliability positively affects the classroom activities in offline mentoring. H7. Tangibles positively affects the classroom activities in offline mentoring. H8. Classroom responsiveness positively affects the classroom activities in offline mentoring. Satisfaction and loyalty Fida et al. (2020) point out that there is a significant association between three variables: service quality, customer satisfaction, and customer loyalty. Perceived service quality is frequently regarded as a predictor of customer satisfaction (Yee et al., 2011). Customer satisfaction is a critical factor in determining customer loyalty, particularly in the service industry (Belás & Gabčová, 2016). Loyal customers are more likely to buy more products, pay less attention to pricing, and recommend the brand to relatives and friends (Bai et al., 2020). Dağhan and Akkoyunlu (2016) found that satisfaction had the strongest impact on the continuance intention in online learning environments (Dağhan & Akkoyunlu, 2016). We propose the hypothesis: H9. Satisfaction with Coursera MOOCs positively affects continuance intention towards blended learning using Coursera MOOCs. Several studies on traditional services and online services point out that customer satisfaction positively affects customer loyalty (Dehghan et al., 2014; Kilburn et al., 2016; Suchánek & Králová, 2019). In this context, customer loyalty refers to recommending the brand to relatives and friends (Bai et al., 2020). Therefore, we also hypothesise: H10. Satisfaction with Coursera MOOCs positively affects recommendation of blended learning using Coursera MOOCs to others. Australasian Journal of Educational Technology, 2022, 38(6). 125 Classroom activities and loyalty Li et al, (2021) noted that student-instructor interaction had a positive impact on students’ continuance intentions. Therefore, we also hypothesise: H11. The classroom activities, in offline mentoring positively affects continuance intention towards blended learning using Coursera MOOCs. He et al.'s (2017) found that teachers’ interaction styles might have influenced the student loyalty and their recommendation to others. Therefore, we hypothesise: H12. The classroom activities, in offline mentoring positively affects recommendation of blended learning using Coursera MOOCs to others. Continuance intention and recommendation to others Izogo (2016) noted that repurchase intention positively affected willingness to recommend. Lastly, we hypothesise: H13. Continuance intention towards blended learning using Coursera MOOCs positively affects recommendation of blended learning using Coursera MOOCs to others. In summary, the proposed research model is illustrated in Figure 1. Figure 1. Research model showing the hypotheses relationships Methods Informed consent was obtained from all survey participants. The research team ensured that all questions in this survey were answered by selecting the most appropriate answer. Participation was voluntary, and anonymity and confidentiality was ensured. Australasian Journal of Educational Technology, 2022, 38(6). 126 Research site The higher education institution in this research was the first private university established by an information and communications technology enterprise in Vietnam. This research investigated six programs run by the institute including information technology, business administration, graphic design, linguistics, multimedia communication, and hospitality management. These programs implemented blended learning using Coursera MOOCs from 2019. The study involved four campuses: Hanoi, Ho Chi Minh City, Da Nang, and Can Tho. There were approximately 21,500 students enrolled across the four campuses. Since September 2019, every student in the six programs the subject of this study has been required to take one Coursera MOOC per semester. Survey instrument design To measure the quality of blended learning using Coursera MOOCs, the 72 questions in the survey were divided amongst five sections: (1) Coursera MOOCs quality, (2) offline mentoring quality, (3) students’ perception towards Coursera MOOCs, (4) students’ perception towards offline mentoring, and (5) students’ acceptance towards blended learning using Coursera MOOCs (Table 3). The quality of the Coursera MOOCs was measured using three variables: learning content, reliability, and responsiveness. Items of learning content were adopted and adapted from Yin (2016). Items related to reliability were taken from the studies of Pham et al. (2019) and Udo et al. (2011), and items related to responsiveness are taken from the studies of Pham et al. (2019) and Yin (2016). The quality of offline mentoring was measured using five variables: empathy, assurance, reliability, tangibles, and responsiveness. The items in these variables were taken from the studies of Pham et al. (2019), Yin (2016) and Udo et al. (2011). To measure students’ perception towards Coursera MOOCs, we used the variable of satisfaction. Items related to satisfaction were adopted and adapted from Song (2010). To measure students’ perception towards offline mentoring, we used the variable of classroom activities. Items related to classroom activities were adopted and adapted from Yin (2016). Lastly, to measure students’ acceptance towards blended learning, we used two variables, continuance intention and recommendation to others. Items related to these variables were taken from Song (2010). The students scored their responses to the questions on a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5). Sampling Because of the large population of undergraduate students in the studied Vietnamese higher education institution, cluster sampling was used (Elfil & Negida, 2017). The researchers conducted the survey across the four campuses (Hanoi, Ho Chi Minh City, Can Tho, and Da Nang) of the Vietnamese higher education institution, using Google forms distributed via the participants’ student email addresses. The survey was first pre-tested on 54 students of the higher education institution. Using SPSS version 26 to calculate Cronbach’s alpha, we found the Cronbach’s alpha values were acceptable ranging from 0.7 to 0.8 (Saidi & Siew, 2019). The survey was then distributed to undergraduate students enrolled in the six programs at the four campuses listed previously. The research was conducted from 2 to 22 June 2021, and 637 usable responses were obtained. Common method bias Referring to the study of Rodríguez-Ardura and Meseguer-Artola (2020), this paper employed the following processes to deal with common method bias. First, data was gathered from more than two different sources. In this case, we collected data from four campuses of the Vietnamese higher education institution. Second, the survey was anonymous, and every effort was made to ensure all questions were clear and appropriate. Lastly, with the inclusion both positive and negative worded items on the same scale, survey respondents were prevented from giving identical responses to all questions. Results Descriptive characteristics of the respondents All the respondents were first year, junior, sophomore, and senior students and had experienced at least one Coursera MOOC blended with offline mentoring. The demographic and basic information of the Australasian Journal of Educational Technology, 2022, 38(6). 127 respondents is depicted in Table 1. Male and female responders accounted for 60.1% and 38.5%, respectively. Only 1.4% of the respondents do not specify their gender. Nearly half of the sample were third year students (42.4%), while the remainder was made up of first year students (25.9%), second year students (17.1%), and final year students (14.6%). Regarding the programs of the respondents, 35.1% were information technology students and 35.0% were business administration students. The remainder were English linguistics, multimedia communication, hospitality management, and graphic design students. Table 1 Demographic and basic information of respondents Characteristic Frequency (n = 637) % Gender: Female 245 38.5 Male 383 60.1 Not prefer to say 9 1.4 School year: First year 165 25.9 Second year 109 17.1 Third year 270 42.4 Final year 93 14.6 Number of Coursera MOOCs 1 207 32.5 2 153 24.0 3 106 16.7 More than 3 171 26.8 Program: Business administration 223 35.0 English linguistics 121 19.0 Graphic design 29 4.6 Hospitality management 14 2.2 Information technology 224 35.1 Multimedia communication 26 4.1 Campus: Can Tho 95 14.9 Da Nang 47 7.4 Hanoi 304 47.7 Ho Chi Minh 191 30.0 Results of confirmatory factor analysis Hair et al. (2012) note that the confirmatory factor analysis approach is used to validate the factor structure of a set of observed variables. All indexes were within acceptable criteria ranges, signifying a good model fit (Table 2). The chi-square value was 2.478, with the range of 2.0 to 5.0 considered as acceptable (Hair et al., 2010). The most frequently used rules for performing the confirmatory factor analysis on a measurement model are that the normed fit index is greater than 0.9, incremental fit index is greater than 0.9, relative fit index is greater than 0.8, Tucker-Lewis index is greater than 0.9, root mean square error of approximation is less than 0.08, and comparative fit index is greater than 0.9 (Hair et al., 2010). Thus, these results demonstrate the goodness of fit of the proposed model. Australasian Journal of Educational Technology, 2022, 38(6). 128 Table 2 Results of multiple fit indices of the confirmatory factor analysis Index Result Acceptable level Chi-square/degree of freedom 2.478 < 5 Normed fit index 0.904 > 0.9 Incremental fit index 0.941 > 0.9 Relative fit index 0.895 >0.8 Tucker-Lewis index 0.935 > 0.9 Root mean square error of approximation 0.048 < 0.08 Comparative fix index 0.940 > 0.9 Awang (2015) stated that a factor loading of 0.7 or greater for each item indicates a high degree of convergent validity. Table 3 shows all factor loading values were greater than the ideal threshold of 0.7, thus, demonstrating high validity of the survey results (Awang, 2015). Table 3 Results of factor loading for confirmatory factor analysis Items Factor loading Learning content LC6: Coursera MOOC materials are plentiful. 0.814 LC5: Coursera MOOC materials are up to date. 0.807 LC4: Coursera MOOC materials are easy to use. 0.833 LC2: Coursera MOOC materials are understandable. 0.766 LC1: Coursera MOOC materials are easy to access. 0.801 Online reliability OREL6: My Coursera MOOC instructor is knowledgeable. 0.838 OREL5: My Coursera MOOC instructor is trustworthy. 0.849 OREL4: My Coursera MOOC instructor is well-prepared. 0.847 OREL3: My Coursera MOOC instructor provides good online lectures. 0.831 OREL1: My Coursera MOOC instructor is dependable. 0.73 Online responsiveness ORES5: Coursera staff deal with my request within the promised deadline. 0.836 ORES4: Coursera staff are willing to help me. 0.870 ORES3: Coursera staff understand my specific needs. 0.849 ORES2: Coursera staff are active in support service. 0.864 ORES1: Coursera staff provide me with prompt support service. 0.864 Empathy EMP6: My lecturer provides me with an environment that encourages interactive participation. 0.844 EMP5: My lecturer motivates me to do my best. 0.884 EMP4: My lecturer encourages me to do my best. 0.863 EMP3: My lecturer has my best long-term interest in mind. 0.759 EMP1: My lecturer is genuinely concerned about me. 0.727 Assurance Australasian Journal of Educational Technology, 2022, 38(6). 129 ASS3: My lecturer is an expert in his/her field. 0.884 ASS1: My lecturer is knowledgeable in his/her field. 0.888 ASS2: My lecturer is professional in his/her field. 0.919 Tangibles TAN6: The content of final exams aligns with course learning outcomes. 0.773 TAN4: Final exams are effective in measuring students’ performance. 0.780 TAN2: The difficulty level of final exams is reasonable. 0.777 TAN1: The duration of final exams is reasonable. 0.774 Classroom reliability CREL5: My lecturer consistently provides me with good mentoring. 0.897 CREL4: My lecturer reliably corrects information when needed. 0.883 Classroom responsiveness CRES6: My lecturer responds to me in a professional manner. 0.838 CRES4: My lecturer effectively answers my questions. 0.877 CRES3: My lecturer responds to my learning needs in a timely manner. 0.860 CRES2: My lecturer is ready to answer my questions. 0.912 CRES1: My lecturer always welcomes my questions and comments. 0.882 Classroom activities CA1: The transformation from teacher-centered classroom to student- centered classroom is good for me. 0.764 CA2: Collaborative learning enhances my engagement in the classroom. 0.840 CA3: Active learning is useful for me in the classroom. 0.842 CA4: I enjoy working in groups in the classroom. 0.758 CA5: I have opportunities to have direct interaction with my lecturer in the classroom. 0.755 Satisfaction SAT3: My experience with Coursera MOOCs was better than I had expected. 0.848 SAT4: Most of my expectations with the Coursera MOOCs were confirmed. 0.826 SAT5: My tuition fee paid for Coursera MOOCs was worth it. 0.784 SAT6: I am generally satisfied with Coursera MOOCs. 0.882 Continuance intention CI2: I am likely to engage in blended courses in the next semester. 0.844 CI4: I plan to spend more time on blended courses in the next semester. 0.875 CI5: I plan to actively engage in blended courses in the next semester. 0.878 CI6: I plan to frequently engage in blended courses in the next semester. 0.887 Recommendation to others RTO1: I will recommend blended courses to friends in my program of study. 0.877 RTO2: I will recommend blended courses to friends in other programs of study. 0.885 RTO3: I will recommend blended courses to my family members. 0.887 RTO4: I will post positive things about blended courses on my Facebook. 0.799 RTO6: I will recommend blended courses to anyone. 0.853 All variables in Table 4 have composite reliability > 0.7 and average variance extracted > 0.5. Maximum shared variance is likewise lower than average variance extracted for all variables. Notably, the square root of average variance extracted (in the cross line) exceeds all inter-construct correlations combined. Thus, the measurement model has convergent and discriminant validity (Hair et al., 2010). Australasian Journal of Educational Technology, 2022, 38(6). 130 Table 4 Convergent and discriminant validity C R A V E M S V L C O R E L O R E S E M P A S S C R E L T A N C R E S R T O C I C A S A T LC 0.902 0.647 0.646 0.804 OREL 0.911 0.673 0.646 0.804 *** 0.820 ORES 0.932 0.734 0.594 0.771 *** 0.735 *** 0.857 EMP 0.909 0.669 0.611 0.626 *** 0.733 *** 0.639 *** 0.818 ASS 0.925 0.805 0.748 0.633 *** 0.761 *** 0.573 *** 0.772 *** 0.897 CREL 0.884 0.792 0.748 0.639 *** 0.748 *** 0.586 *** 0.781 *** 0.865 *** 0.890 TAN 0.858 0.602 0.556 0.746 *** 0.713 *** 0.689 *** 0.699 *** 0.650 *** 0.710 *** 0.776 CRES 0.942 0.764 0.695 0.598 *** 0.725 *** 0.563 *** 0.755 *** 0.823 *** 0.834 *** 0.629 *** 0.874 RTO 0.934 0.741 0.727 0.601 *** 0.580 *** 0.599 *** 0.582 *** 0.455 *** 0.479 *** 0.706 *** 0.449 *** 0.861 CI 0.926 0.759 0.727 0.599 *** 0.552 *** 0.578 *** 0.578 *** 0.445 *** 0.452 *** 0.692 *** 0.442 *** 0.852 *** 0.871 CA 0.894 0.629 0.433 0.634 *** 0.641 *** 0.569 *** 0.654 *** 0.592 *** 0.605 *** 0.658 *** 0.605 *** 0.582 *** 0.575 *** 0.793 SAT 0.902 0.698 0.512 0.715 *** 0.638 *** 0.631 *** 0.542 *** 0.443 *** 0.451 *** 0.690 *** 0.412 *** 0.702 *** 0.711 *** 0.574 *** 0.836 Note. CR: composite reliability, AVE: average variance extracted, MSV: maximum shared variance, LC: learning content, OREL: online reliability, ORES: online responsiveness, EMP: empathy, ASS: assurance, TAN: tangibles, CREL: classroom reliability, CRES: classroom responsiveness, CA: classroom activities, SAT: satisfaction, CI: continuance intention, RTO: recommendation to others ***Correlation is significant at the 0.001 level Structural model The indices measure the model fit for the structural model (Table 5). The values of these indices all fell within the acceptable range, as mentioned in the study of Hair et al. (2010). Chi-square is 3168.770 whereas degree of freedom is 1236. As a result, the chi-square/degree of freedom was 2.564, with a range of 2.0 to 5.0 considered as acceptable (Hair et al., 2010). The most frequently used rules for the structural model are that the normed fit index is greater than 0.9, Tucker-Lewis index is greater than 0.9, root mean square error of approximation is less than 0.08, and comparative fit index is greater than 0.9 (Hair et al., 2010). Thus, these results demonstrated the goodness of fit of the proposed model. Australasian Journal of Educational Technology, 2022, 38(6). 131 Table 5 Results of multiple fit indices of the structural model Index Result Acceptable level Chi-square 3168.77 0 - Degree of freedom 1236 - Chi-square/degrees of freedom 2.564 < 5 Normed fit index 0.899 > 0.9 Tucker-Lewis index 0.931 > 0.9 Root mean square error of approximation 0.050 < 0.08 Comparative fit index 0.936 > 0.9 Hypothesis testing Impact of Coursera MOOCs quality on satisfaction Table 6 shows results of the structural equation modelling. The influence of learning content on satisfaction was both positive and statistically significant (β=0.533). As a result, hypothesis 1 was supported. Online responsiveness had a substantial effect on satisfaction (β=0.181, p=0.001), corroborating hypothesis 3. On the basis of these findings, it was observed that the effect of learning content on satisfaction was 2.94 times greater than the effect of online responsiveness on satisfaction (0.533 compared to 0.181). This demonstrated the respondents’ perception of the significance of MOOC learning content in boosting their satisfaction with MOOCs. Finally, p=0.232 indicated that the association between online reliability and satisfaction was negligible. As a result, hypothesis 2 was not supported. Impact of offline mentor quality on classroom activities The results of data analysis indicated that empathy had a substantial effect on classroom activities for the respondents (β = 0.269), therefore, hypothesis 4 was accepted. Similarly, the impact of tangibles on classroom activities was also significant (β = 0.422), supporting hypothesis 7. Additionally, classroom responsiveness had a statistically significant effect on classroom activities (β= 0.176, p= 0.014). As a result, hypothesis 8 was supported. Lastly, insignificant relationships between the assurance and classroom activities, and between the classroom reliability and classroom activities were observed (p = 0.515 and p = 0.33). Consequently, both hypothesis 5 and hypothesis 6 were rejected. Impact of satisfaction with Coursera MOOCs and classroom activities in offline mentoring on continuance intention towards blended learning using Coursera MOOCs Hypotheses 9 and 11 postulated the relationship between satisfaction and continuance intention, and the relationship between classroom activities and continuance intention respectively. It was found that the effects of class activities in offline mentoring on continuance intention towards blended learning using Coursera MOOCs and satisfaction with Coursera MOOCs on continuance intention towards blended learning using Coursera MOOCs in this study were very strongly significant (β ranging from 0.281 to 0.572). Thus, both hypothesis 9 and hypothesis 11 were accepted. Interestingly, the effect of satisfaction with Coursera MOOCs on continuance intention towards blended learning Coursera MOOCs was 3.56 times as much as the effect of class activities in offline mentoring on continuance intention towards blended learning Coursera MOOCs (β =0.572 compared to β= 0.281). This was perhaps as a result of the requirement for students to pass Coursera MOOCs to qualified for taking their final exams. In the meantime, the offline mentoring hours are only optional in the case of students who have any concerns and need support from their higher education institution lecturers. Impact of satisfaction with Coursera MOOCs, classroom activities in offline mentoring, and continuance intention towards blended learning using Coursera MOOCs on recommendation of blended learning using Coursera MOOCs to others The results of data analysis showed that satisfaction with Coursera MOOCs had a positive and significant impact on recommendation of blended learning using Coursera MOOCs to others (β=0.176). Therefore, hypothesis 10 was supported. Similarly, the impact of classroom activities in offline mentoring on recommendation of blended learning using Coursera MOOCs to others was also significant (β=0.112). Therefore, hypothesis 12 was also supported. Continuance intention towards blended learning using Australasian Journal of Educational Technology, 2022, 38(6). 132 Coursera MOOCs significantly influenced recommendation of blended learning using Coursera MOOCs to others (β = 0.665), therefore hypothesis 13 was supported. Table 6 Results of structural equation model Dependent variable β p Hypothesis Satisfaction Learning content 0.533 *** H1: supported Online reliability 0.075 0.232 H2: not Supported Online responsiveness 0.181 0.001 H3: supported R2 55.5% Classroom activities Empathy 0.269 *** H4: supported Assurance 0.055 0.515 H5: not supported Classroom reliability -0.097 0.33 H6: not supported Tangibles 0.422 *** H7: supported Classroom responsiveness 0.176 0.014 H8: supported R2 55.0% Continuance intention Satisfaction 0.572 *** H9: supported Classroom activities 0.281 *** H11: supported R2 55.1% Recommendation to others Satisfaction 0.176 *** H10: supported Classroom activities 0.112 *** H12: supported Continuance intention 0.665 *** H13: supported R2 74.6% Note. *** significance level 0.001 LC: learning content, OREL: online reliability, ORES: online responsiveness, EMP: empathy, ASS: assurance, TAN: tangibles, CREL: classroom reliability, CRES: classroom responsiveness, CA: classroom activities, SAT: satisfaction, CI: continuance intention, RTO: recommendation to others Discussion The results showed that learning content had the greatest impact on student’s satisfaction with Coursera MOOCs compared to reliability and responsiveness for the respondents to this survey. This result agrees with previous conclusions by Uppal et al. (2018), Gray and DiLoreto (2016), and Udo et al. (2011). Online content is available 24/7 and can be reviewed by students to reinforce learning and review the knowledge during the course (Gray & DiLoreto, 2016). MOOCs can offer this convenience in terms of scheduling and location, as well as high-quality educational materials and content supplied by a variety of renowned universities (Virani et al., 2020). The higher education institution in this study selected Coursera as a MOOC provider because it was found to be beneficial to students in the following ways: (1) students can acquire the most up-to-date learning content; and (2) students have opportunities to learn via videos by renowned university instructors. Therefore, this research confirms that the learning content of Coursera MOOCs is considered as an important factor for students’ satisfaction at the higher education institution. Anh et al.'s (2019) study noted that the learning content of MOOCs is also beneficial to lecturers as they can utilise new content and new instructional methods. This, in conjunction with the results of this study, suggest that the curriculum development office of higher education institution should select Coursera MOOCs that are up-to-date and contain practical content that meets the industries’ requirements and students’ professional needs. More importantly, this research showed the selection of appropriate Coursera MOOCs could increase students’ interested in their own learning. Similarly, Bralić and Divjak (2018) recommended that universities should provide students with a variety of engaging MOOCs to select from, enabling students to achieve their learning needs.. Online responsiveness also had a positive relationship with students’ satisfaction with the Coursera MOOCs. This finding aligns with the findings of Uppal et al. (2018), and Udo et al. (2011). In both of their studies with MOOCs, students faced issues such as plagiarism in assignments, obtaining Coursera Australasian Journal of Educational Technology, 2022, 38(6). 133 certificates, and technical issues. Therefore, there is a need for prompt and effective support from Coursera staff. This can be resolved quickly and professionally by making it part of the terms of agreement between the higher education institution and Coursera. Online reliability did not significantly affect students’ satisfaction with the Coursera MOOCs. This result is consistent with the results of Uppal et al. (2018), and Udo et al. (2011). MOOCs help develop students’ autonomy in learning (Bruff et al., 2013). Based on constructivist learning theory, learners create their own meaning in order to learn (Garmston & Wellman, 1994). Thus, it is critical for them to take ownership of their own learning, and to acquire both autonomy and knowledge (Alzahrani, 2015). Consequently, in this context students at the higher education institution did not depend on Coursera instructors. They were active in exploring their own knowledge and looking for support from their online peers (Uppal et al., 2018). Tangibles, empathy, and classroom responsiveness all had significant impact on classroom activities, with Tangibles illustrating the greatest impact on classroom activities compared to the impacts of both empathy and classroom responsiveness. This revealed that the tangibles variable, the final exam in this study, was perceived as important by the respondents, stimulating them to attend the offline mentoring and interact with higher education institution lecturers in classroom activities. This revealed that grades are very important to responding students at the higher education institution. The final exam is a critical factor in their educational process. The result suggest that the higher education institution should put more effort into improving the quality of the final exams in the following ways: (1) develop questions mapped with the course outcomes and content, (2) check the test bank carefully before generating the final exam questions, and (3) monitor the final exam diligently. Similarly, the caring (empathy) and the willingness to assist (classroom responsiveness) of higher education institution lecturers were considered as motivators for student to engage in offline mentoring and interact with higher education institution lecturers and their peers. It is suggested that the heads of departments should offer Coursera MOOC orientation for full time and part time lecturers, especially for new lecturers. Lecturers need to be aware of ways to care for the students and understand what kinds of classroom activities should be conducted in offline mentoring to respond to students’ needs. Most importantly, the higher education institution should organise professional training, pedagogical and psychological workshops to assist lecturers to provide better offline mentoring sessions with students. There were insignificant relationships between the assurance and classroom activities in offline mentoring, and between the classroom reliability and classroom activities in offline mentoring. This finding agreed with the current practice at the higher education institution. The higher education institution lecturers work not only as mentors but also as facilitators in supporting students with matters related to their Coursera MOOCs. Remarkably, offline mentoring hours are not compulsory for the students. Thus, they only come to class when they seek the support from the higher education institution lecturers. In this situation, the higher education institution lecturers face a big challenge as some students do not find it necessary to attend the offline mentoring sessions, as well as do not highly regard the role of the higher education institution lecturers in these offline sessions. This challenge might result in negative effects on classroom activities in offline mentoring. Such classroom activities might negatively influence continuance intention towards blended learning using Coursera MOOCs and negatively influence recommendation of blended learning using Coursera MOOCs to others. Satisfaction with Coursera MOOCs significantly affected continuance intention towards blended learning using Coursera MOOCs and recommendation of blended learning using Coursera MOOCs to others. This result agreed with the studies by Cole et al. (2014) and Lu et al. (2019). It confirmed that satisfaction was a critical factor reflecting the effectiveness of MOOCs implementation at the higher education institution for the respondents. There might be implications for the higher education institution from the positive relationship between satisfaction, continuance intention, and recommendation to others. The HEI needs to conduct more and ongoing studies to explore factors that influence students’ satisfaction with Coursera MOOCs, besides the learning content and responsiveness mentioned above. Interestingly, Erdem-Aydin (2015) highlighted that learners are willing to attend MOOCs, especially if they are focused on areas they are interested in. This can give hints for which curriculum development office and departments of the higher education institution to link to students’ learning needs and select Coursera MOOCs accordingly. Classroom activities in offline mentoring positively influenced continuance intention towards blended learning using Coursera MOOCs. This result disagreed with the finding by Chow and Shi (2014), who Australasian Journal of Educational Technology, 2022, 38(6). 134 noted that the impacts of tutor interaction and peer interaction on e-learning continuance intention were not significant. Similarly, classroom activities significantly influenced recommendation of blended learning using Coursera MOOCs to others. In short, these results suggest that it is not only Coursera MOOCs but also classroom activities in offline mentoring that contribute to the student loyalty with blended learning in this situation. The findings might encourage higher education institutions to continuously improve the quality of offline mentoring in general and the quality of classroom activities in particular. It is strongly suggested that higher education institutions should consider increasing the offline mentoring hours, as well as creating diversified classroom activities tailored to students’ needs. In addition, the higher education institution lecturers could not only directly mentor in face-to-face classrooms but also use social media tools to mentor students beyond class hours. The results showed that continuance intention towards blended learning using Coursera MOOCs had the greatest impact on recommendation of blended learning using Coursera MOOCs to others . This finding totally fits with the study by Pham et al. (2019). In fact, as students intended to continue experiencing with blended learning using Coursera MOOCs, they were willing to recommend blended learning using Coursera MOOCs to others including their classmates and friends. This implies that student continuance intention towards blended learning using MOOCs Coursera and willingness to recommend blended learning using MOOCs Coursera to others might be measured as successful indicators in evaluating the quality of blended learning using MOOCs Coursera implementation. In summary, from a theoretical perspective, the study revealed that the not only the service quality of Coursera MOOCs, but also the service quality of offline mentoring, significantly affected the continuance intention towards blended learning using MOOCs Coursera and recommendation of blended learning using Coursera MOOCs to others at the higher education institution by the respondents. This paper proposes a need for further studies to examine the mechanisms leading to students’ continuance intention and willingness to recommend blended learning to others, using a blended learning using MOOCs model . From a practical perspective, such research would provide a case study of a blended learning model between the internationally well-known MOOC provider and locally supported offline mentoring. This might encourage higher education institutions to develop the blended learning using Coursera MOOCs model and the MOOCs policies of Vietnam, for higher education institutions in developing countries. Conclusion There are still some gaps in the evaluation of the quality of blended learning. There is little research on the blended learning using MOOCs models generally and the blended learning using Coursera MOOCs model particularly. Very few universities outsource the learning materials from the Coursera and blend it with offline mentoring in traditional classrooms. The findings of this study showed that the Coursera MOOCs learning content and online responsiveness of Coursera staff had a great effect on student satisfaction. These findings agreed with the findings of Uppal et al. (2018) and Udo et al. (2011). Online reliability had an insignificant effect on student satisfaction with Coursera MOOCs. This result also supports the previous studies of Uppal et al. (2018) and Udo et al. (2011). In offline mentoring, there were also positive correlations between empathy, tangibles, classroom responsiveness, and classroom activities. Additionally, we observed that assurance and classroom reliability had insignificant impacts on classroom activities in offline mentoring. Notably, satisfaction with Coursera MOOCs and class activities in offline mentoring had significant effects on the continuance intention towards blended learning using Coursera MOOCs. These findings agree and disagree with the studies by Lu et al. (2019), and Chow and Shi (2014), respectively. Continuance intention towards blended learning using Coursera MOOCs had a significant relationship with recommendation of blended learning using Coursera MOOCs to others. This conclusion confirms the existing literature (Pham et al., 2019). This study contains the two following limitations. First, it focused only on undergraduate programs at a single Vietnamese university. Second, it investigated a specific case with the provider Coursera. Consequently, we propose that further research might study other programs such as vocation and postgraduate programs, and examine cases of other popular MOOC providers. To highlight the significance of the blended learning implementation, we will continue investigating how the model of blended learning using Coursera MOOCs affects future student performances at the higher education institution. Australasian Journal of Educational Technology, 2022, 38(6). 135 References Abrahim, S., Mir, B. A., Suhara, H., Mohamed, F. A., & Sato, M. (2019). Structural equation modeling and confirmatory factor analysis of social media use and education. International Journal of Educational Technology in Higher Education, 16(1), 1-25. https://doi.org/10.1186/s41239-019-0157- y Albelbisi, N. A., Al-Adwan, A. S., & Habibi, A. (2021). Impact of quality antecedents on satisfaction toward MOOC. Turkish Online Journal of Distance Education, 22(2), 164–175. https://eric.ed.gov/?id=EJ1290827 Alzahrani, J. (2015). Investigating role of interactivity in effectiveness of e-learning. Brunel University London. http://bura.brunel.ac.uk/handle/2438/11801 Anh, T. V., Nguyen, H. T. T., & Linh, N. T. M. (2019). Digital transformation: A digital learning case study. Proceedings of the 2019 The World Symposium on Software Engineering, Wuhan, China, 119– 124. https://doi.org/10.1145/3362125.3362135 Awang, Z. (2015). SEM made simple: A gentle approach to learning structural equation modeling, MPWS Rich Publication. https://eprints.unisza.edu.my/3180/ Bai, Y., Jia, S., Wang, S., & Tan, B. (2020). Customer loyalty improves the effectiveness of recommender systems based on complex network. Information, 11(3), 1-6. https://doi.org/10.3390/info11030171 Belás, J., & Gabčová, L. (2016). The relationship among customer satisfaction, loyalty and financial performance of commercial banks. E+ M Ekonomie a Management. https://doi.org/10.15240/TUL/001/2016-1-010 Bralić, A., & Divjak, B. (2018). Integrating MOOCs in traditionally taught courses: Achieving learning outcomes with blended learning. International Journal of Educational Technology in Higher Education, 15(1), 1-16. https://doi.org/10.1186/s41239-017-0085-7 Bruff, D. O., Fisher, D. H., McEwen, K. E., & Smith, B. E. (2013). Wrapping a MOOC: Student perceptions of an experiment in blended learning. Journal of Online Learning and Teaching, 9(2), 187-199. https://jolt.merlot.org/vol9no2/bruff_0613.pdf Chen, C. (2020). Online learning is growing faster than ever—We compared 4 of the top platforms to help you decide which one makes sense for you. https://www.businessinsider.com/online-learning- platform-comparison-udemy-skillshare-lynda-coursera Chow, W. S., & Shi, S. (2014). Investigating students’ satisfaction and continuance intention toward e- learning: An extension of the expectation – Confirmation model. Procedia - Social and Behavioral Sciences, 141, 1145–1149. https://doi.org/10.1016/j.sbspro.2014.05.193 Cole, M. T., Shelley, D. J., & Swartz, L. B. (2014). Online instruction, e-learning, and student satisfaction: A three year study. International Review of Research in Open and Distance Learning, 15(6), 111–131. https://eric.ed.gov/?id=EJ1048236 Dağhan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198–211. https://doi.org/10.1016/j.chb.2016.02.066 Dehghan, A., Dugger, J., Dobrzykowski, D., & Balazs, A. (2014). The antecedents of student loyalty in online programs. International Journal of Educational Management, 28(1), 15–35. https://doi.org/10.1108/IJEM-01-2013-0007 Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748 Elfil, M., & Negida, A. (2017). Sampling methods in clinical research: An educational review. Emergency, 5(1), 1-3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325924/ Erdem-Aydin, İ. (2015). Preferences and willingness for participating MOOCs in Turkish. Turkish Online Journal of Educational Technology, 14(3), 88-96. https://eric.ed.gov/?id=EJ1067718 Estévez-Ayres, I., Alario-Hoyos, C., Pérez-Sanagustín, M., Pardo, A., Crespo-García, R. M., Leony, D., Parada G, H. A., & Delgado-Kloos, C. (2015). A methodology for improving active learning engineering courses with a large number of students and teachers through feedback gathering and iterative refinement. International Journal of Technology and Design Education, 25(3), 387–408. https://eric.ed.gov/?id=EJ1067292 Fida, B. A., Ahmed, U., Al-Balushi, Y., & Singh, D. (2020). Impact of service quality on customer loyalty and customer satisfaction in Islamic banks in the Sultanate of Oman. SAGE Open, 10(2), 1-10. https://doi.org/10.1177/2158244020919517 https://doi.org/10.1186/s41239-019-0157-y https://doi.org/10.1186/s41239-019-0157-y https://eric.ed.gov/?id=EJ1290827 http://bura.brunel.ac.uk/handle/2438/11801 https://doi.org/10.1145/3362125.3362135 https://eprints.unisza.edu.my/3180/ https://doi.org/10.3390/info11030171 https://doi.org/10.15240/TUL/001/2016-1-010 https://doi.org/10.1186/s41239-017-0085-7 https://jolt.merlot.org/vol9no2/bruff_0613.pdf https://www.businessinsider.com/online-learning-platform-comparison-udemy-skillshare-lynda-coursera https://www.businessinsider.com/online-learning-platform-comparison-udemy-skillshare-lynda-coursera https://doi.org/10.1016/j.sbspro.2014.05.193 https://eric.ed.gov/?id=EJ1048236 https://doi.org/10.1016/j.chb.2016.02.066 https://doi.org/10.1108/IJEM-01-2013-0007 https://doi.org/10.1080/07421222.2003.11045748 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325924/ https://eric.ed.gov/?id=EJ1067718 https://eric.ed.gov/?id=EJ1067292 https://doi.org/10.1177/2158244020919517 Australasian Journal of Educational Technology, 2022, 38(6). 136 Garmston, R., & Wellman, B. (1994). Insights from constructivist learning-theory. Educational Leadership, 51(7), 84-85. https://www.proquest.com/docview/224847505 Gray, J. A., & DiLoreto, M. (2016). The effects of student engagement, student satisfaction, and perceived learning in online learning environments. International Journal of Educational Leadership Preparation, 11(1), 1-20. https://eric.ed.gov/?id=EJ1103654 Hair, J. F., Black, B., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: Global edition (7th ed.). Pearson. Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning, 45(5), 320–340. https://doi.org/10.1016/j.lrp.2012.09.008 He, X., Huang, S.-Z., Li, T., & Chen, K. (2017). A study of interactive style on students loyalty in science technology education: Moderating of management level. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 4689–4700. https://doi.org/10.12973/eurasia.2017.00959a Hoang, N. T. (2015). EFL teachers’ perceptions and experiences of blended learning in a Vietnamese university (Doctoral dissertation). Queensland University of Technology. https://eprints.qut.edu.au/83945/ Hobson, A. J. (2012). Fostering face-to-face mentoring and coaching. In S. J. Fletcher, & C. A. Mullen (Eds.), The SAGE handbook of mentoring and coaching in education (pp.59-73). SAGE Publications Ltd. https://doi.org/10.4135/9781446247549 Izogo, E. E. (2016). Structural equation test of relationship quality: Repurchase intention – willingness to recommend framework in retail banking. International Journal of Emerging Markets, 11(3), 374–394. https://doi.org/10.1108/IJOEM-07-2015-0130 Juran, J. M. (1988). Juran on planning for quality. Collier Macmillan. Kilburn, B., Kilburn, A., & Davis, D. (2016). Building collegiate e-loyalty: The role of perceived value in the quality loyalty linkage in online higher education. Contemporary Issues in Education Research, 9(3), 95–102. https://eric.ed.gov/?id=EJ1106895 Li, Y., Nishimura, N., Yagami, H., & Park, H.-S. (2021). An empirical study on online learners’ continuance intentions in China. Sustainability, 13(2), 1–18. https://ideas.repec.org/a/gam/jsusta/v13y2021i2p889-d481813.html Lin, H.-F. (2007). Measuring online learning systems success: Applying the updated DeLone and McLean model. Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 10(6), 817–820. https://doi.org/10.1089/cpb.2007.9948 Loviscach, J. (2013). MOOCs and blended learning. MOOCs-massive open online courses: Offene Bildung Oder Geschäftsmodell. Waxmann Verlag GmbH. Lu, Y., Wang, B., & Lu, Y. (2019). Understanding key drivers of MOOC satisfaction and continuance intention to use. Journal of Electronic Commerce Research, 20(2), 105–117. https://www.proquest.com/docview/2226387668/abstract/9C882FE7BC9341DDPQ/1 Machumu, H., Ghasia, M., & Musabila, A. K. (2018). Blended learning activities in blended learning environments: Experiences from Mzumbe University, Tanzania. Proceedings of EdMedia+ Innovate Learning 2018, Amsterdam. https://biblio.ugent.be/publication/8626711/file/8626717.pdf Malaysia Ministry of Education (2015). Malaysia Education Blueprint 2015 2025 (Higher Education). Putrajaya, Malaysia. http://mohe.gov.my/muat-turun/awam/penerbitan/pppm-2015-2025-pt/5- malaysia-education-blueprint-2015-2025-higher-education Moore, J. C. (2012). A synthesis of Sloan-C effective practices. Journal of Asynchronous Learning Networks, 16(1), 91–115. https://www.learntechlib.org/p/132475/ Otaibi, N. M. A., & Yasmeen, K. (2014). An overview of customer loyalty, perceived service quality and customer satisfaction: Brief on Saudi grocery stores. Journal of Entrepreneurship and Business Innovation, 1(1), 79–122. https://ideas.repec.org/a/mth/jebi88/v1y2014i1p79- 122.htmlhttps://ideas.repec.org/a/mth/jebi88/v1y2014i1p79-122.html Panopoulos, A. P., & Sarri, K. (2013). E-mentoring: The adoption process and innovation challenge. International Journal of Information Management, 33(1), 217-226. https://doi.org/10.1016/j.ijinfomgt.2012.10.003 Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40. https://www.researchgate.net/publication/225083802_SERVQUAL_A_multiple- _Item_Scale_for_measuring_consumer_perceptions_of_service_quality https://www.proquest.com/docview/224847505 https://eric.ed.gov/?id=EJ1103654 https://doi.org/10.1016/j.lrp.2012.09.008 https://doi.org/10.12973/eurasia.2017.00959a https://eprints.qut.edu.au/83945/ https://doi.org/10.4135/9781446247549 https://doi.org/10.1108/IJOEM-07-2015-0130 https://eric.ed.gov/?id=EJ1106895 https://ideas.repec.org/a/gam/jsusta/v13y2021i2p889-d481813.html https://doi.org/10.1089/cpb.2007.9948 https://www.proquest.com/docview/2226387668/abstract/9C882FE7BC9341DDPQ/1 https://biblio.ugent.be/publication/8626711/file/8626717.pdf http://mohe.gov.my/muat-turun/awam/penerbitan/pppm-2015-2025-pt/5-malaysia-education-blueprint-2015-2025-higher-education http://mohe.gov.my/muat-turun/awam/penerbitan/pppm-2015-2025-pt/5-malaysia-education-blueprint-2015-2025-higher-education https://www.learntechlib.org/p/132475/ https://ideas.repec.org/a/mth/jebi88/v1y2014i1p79-122.html https://ideas.repec.org/a/mth/jebi88/v1y2014i1p79-122.html https://doi.org/10.1016/j.ijinfomgt.2012.10.003 https://www.researchgate.net/publication/225083802_SERVQUAL_A_multiple-_Item_Scale_for_measuring_consumer_perceptions_of_service_quality https://www.researchgate.net/publication/225083802_SERVQUAL_A_multiple-_Item_Scale_for_measuring_consumer_perceptions_of_service_quality Australasian Journal of Educational Technology, 2022, 38(6). 137 Pham, H. H., & Ho, T. T. H. (2020). Toward a ‘new normal’with e-learning in Vietnamese higher education during the post COVID-19 pandemic. Higher Education Research & Development, 39(7), 1327-1331. https://doi.org/10.1080/07294360.2020.1823945https://doi.org/10.1080/07294360.2020.1823945 Pham, L., Limbu, Y. B., Bui, T. K., Nguyen, H. T., & Pham, H. T. (2019). Does e-learning service quality influence e-learning student satisfaction and loyalty? Evidence from Vietnam. International Journal of Educational Technology in Higher Education, 16(1), 1-26. https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-019-0136-3 Quora (2017). The Future Of Massively Open Online Courses (MOOCs). Forbes. https://www.forbes.com/sites/quora/2017/03/23/the-future-of-massively-open-online-courses-moocs/ Rodríguez-Ardura, I., & Meseguer-Artola, A. (2020). Editorial: How to prevent, detect and control common method variance in electronic commerce research. Journal of Theoretical and Applied Electronic Commerce Research, 15(2), 1–5. https://doi.org/10.4067/S0718-18762020000200101 Safri, S. N. W., & Hanafiah, M. H. (2020). Massive open online course (MOOC) service quality assessment: Issues and instruments. Journal of Tourism, Hospitality & Culinary Arts, 12(1), 1–9. https://www.jthca.org/ Saidi, S. S., & Siew, N. M. (2019). Investigating the validity and reliability of survey attitude towards statistics instrument among rural secondary school students. International Journal of Educational Methodology, 5(4), 651–661. https://ijem.com//investigating-the-validity-and-reliability-of-survey- attitude-towards-statistics-instrument-among-rural-secondary-school-students.html Siemens, G., & Downes, S. (2008). Place to go: Connectivism and connective knowledge. Innovate: Journal of Online Education, 5(1), 1-8. https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1037&context=innovate Song, S. M. (2010). E-learning: Investigating students’ acceptance of online learning in hospitality programs. Iowa State University. https://dr.lib.iastate.edu/entities/publication/77e1be4f-1281-4baf- 9c5d-60529a1ff0ac Suchánek, P., & Králová, M. (2019). Customer satisfaction, loyalty, knowledge and competitiveness in the food industry. Economic research-Ekonomska istraživanja, 32(1), 1237-1255. https://doi.org/10.1080/1331677X.2019.1627893 Tang, S. M., & Tien, H. N. (2020). Digital transformation trend in Vietnam higher education: Blended learning model. International Journal of Social Science and Economics Invention, 6(07), 304-309. https://doi.org/10.23958/ijssei/vol06-i07/218 Udo, G. J., Bagchi, K. K., & Kirs, P. J. (2011). Using SERVQUAL to assess the quality of e-learning experience. Computers in Human Behavior, 27(3), 1272–1283. https://doi.org/10.1016/j.chb.2011.01.009 Uppal, M. A., Ali, S., & Gulliver, S. R. (2018). Factors determining e-learning service quality. British Journal of Educational Technology, 49(3), 412–426. https://doi.org/10.1111/bjet.12552 Virani, S. R., Saini, J. R., & Sharma, S. (2020). Adoption of massive open online courses (MOOCs) for blended learning: The Indian educators’ perspective. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2020.1817760 Wibawa, B., & Kardipah, S. (2018). The flipped-blended model for STEM education to improve students’ performances. International Journal of Engineering & Technology, 7(2.29), 1006–1009. https://doi.org/10.14419/ijet.v7i2.29.14298 Yee, R. W. Y., Yeung, A. C. L., & Cheng, T. C. E. (2011). The service-profit chain: An empirical analysis in high-contact service industries. https://doi.org/10.1016/j.ijpe.2011.01.001 Yin, Y. (2016). Chinese learners’ perceptions of MOOCs: A case study (Doctoral dissertation). Heinrich- Heine-Universität Düsseldorf. https://d-nb.info/1113748001/34 Zeithaml, V. A. (1988), Consumer perceptions of price, quality and value: A means-end model and synthesis of evidence, Journal of Marketing, 52(3), 2-22. https://doi.org/10.2307/1251446 Corresponding author: Hiep-Hung Pham, hiep@edlabasia.org Copyright: Articles published in the Australasian Journal of Educational Technology (AJET) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC- ND 4.0). Authors retain copyright in their work and grant AJET right of first publication under CC BY- NC-ND 4.0. https://doi.org/10.1080/07294360.2020.1823945 https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-019-0136-3 https://www.forbes.com/sites/quora/2017/03/23/the-future-of-massively-open-online-courses-moocs/ https://doi.org/10.4067/S0718-18762020000200101 https://www.jthca.org/ https://ijem.com/investigating-the-validity-and-reliability-of-survey-attitude-towards-statistics-instrument-among-rural-secondary-school-students.html https://ijem.com/investigating-the-validity-and-reliability-of-survey-attitude-towards-statistics-instrument-among-rural-secondary-school-students.html https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1037&context=innovate https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1037&context=innovate https://dr.lib.iastate.edu/entities/publication/77e1be4f-1281-4baf-9c5d-60529a1ff0ac https://dr.lib.iastate.edu/entities/publication/77e1be4f-1281-4baf-9c5d-60529a1ff0ac https://doi.org/10.1080/1331677X.2019.1627893 https://doi.org/10.23958/ijssei/vol06-i07/218 https://doi.org/10.1016/j.chb.2011.01.009 https://doi.org/10.1111/bjet.12552 https://doi.org/10.1080/10494820.2020.1817760 https://doi.org/10.14419/ijet.v7i2.29.14298 https://d-nb.info/1113748001/34 https://doi.org/10.2307/1251446 https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/ Australasian Journal of Educational Technology, 2022, 38(6). 138 Please cite as: Ho, N. T. T., Pham. H-H., Sivapalan. S, & Dinh,V-H. (2022). The adoption of blended learning using Coursera MOOCs: A case study in a Vietnamese higher education institution. Australasian Journal of Educational Technology, 38(6), 121-138. https://doi.org/10.14742/ajet.7671 https://doi.org/10.14742/ajet.7671