Abstract
Purpose
The study is aimed at analyzing the perceptions of students and teachers regarding the effectiveness of massive open online courses (MOOCs) on learning efficiency of students and also evaluating MOOCs as an ideal tool for designing a blended model for education.
Design/methodology/approach
The analysis was carried out by using the data gathered from the students as well as teachers of University of Mysore, Karnataka, India. Two separate sets of questionnaires were developed for both the categories of respondents. Also, the respondents were required to have prior experience in MOOCs. Further, the collected data was analyzed using statistical package for social sciences (SPSS).
Findings
The study showed that MOOCs have a more positive influence on learning efficiency, as opined by both teachers and students. Negative views such as cheating during the assessment, lack of individual attention to students and low teacher-student ratio were also observed.
Practical implications
Many educational institutions view that the MOOCs do not influence learning efficiency and also do not support in achieving their vision. However, this study provides evidence that MOOCs are positively influencing the learning efficiency and also can be employed in a blended model of education so as to promote collaborative learning.
Originality/value
Technology is playing a pivotal role in all fields of life and the education sector is not an exception. It can be rightly said that the technology-based education models such as MOOCs are the need of the hour. This study may help higher education institutions to adopt MOOCs as part of their blended model of education, and, if already adopted, the outcome of the present study will help them to improve the effectiveness of the MOOCs they are offering.
Keywords
Citation
N., A., Kulal, A., M.S., D. and Dinesh, S. (2023), "Effectiveness of MOOCs on learning efficiency of students: a perception study", Journal of Research in Innovative Teaching & Learning, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JRIT-12-2022-0091
Publisher
:Emerald Publishing Limited
Copyright © 2023, Abhishek N., Abhinandan Kulal, Divyashree M.S. and Sahana Dinesh
License
Published in Journal of Research in Innovative Teaching & Learning. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
With changing life patterns and rapid advancements in technology, educational institutions and learners are trying to follow newer ways in their teaching and learning process. There are many platforms available that accommodate the teachers and learners to carry out their educational activities online. However, they are distinct from the traditional methods followed. Thus, there arises the need of having a blended model for education which must create a collaborative learning environment and enable the stakeholders in building shared understandings. The blended model of education is a method which offers a flexible mode of teaching and learning (Ayoub et al., 2020). One such way is massive open online courses (MOOCs). MOOCs are a new and innovative method of offering a blended model of education with several key features such as online lectures, video sessions, slide shows, discussion forums and a host of several combinations. This new model of education is available for anyone interested in learning without any restrictions (Wong, 2016). According to the Coursera platform, 170 leading universities offer more than 7,000 courses through MOOCs [1].
We live in a globalized and technology-based environment which demands the skilled and competent workforce. The knowledge, skills and competencies acquired by learners through education must suit the current market requirements (Tailor et al., 2020). MOOCs have a greater role in fulfilling this demand. MOOCs enable the learners to access a wide range of courses relating to an even wider array of topics such as career development, supplementary learning, corporate learning and training and so on [2]. One of the major benefits of acquiring education through MOOCs is that it offers collaborative sharing of knowledge by world-class teachers. Further, it enables a teacher to address millions of students and exchange thoughts at the global level by surpassing all the geographical boundaries. These days the students, teachers and other professionals alike are preferring MOOCs for their personal and career development. Further, it consumes relatively less resources and allows the learners to have access to global standard courses (Viswanathan, 2012; Anand Shankar Raja and Kallarakal, 2021). This in turn enhances the learning quality and efficiency of students at large (Bekele, 2010; Stokes et al., 2015; Sun et al., 2016; Ahmed et al., 2021). Now MOOCs have been identified as a tool for improving the traditional education models and also enhancing the learning efficiency of students through technology-driven methods of teaching (Bordoloi et al., 2021). Here, learning efficiency means the patterns of learning which uses minimal resources such as time and efforts while maintaining higher learning outcomes (Ghazali et al., 2020).
There is a debate over whether MOOC-based education system is more effective and influential in improving the learning efficiency of students or not. Therefore, in order to analyze the effectiveness of MOOCs on learning efficiency of students, the present study intends to analyze the perceptions of teachers and students of MOOCs offered by University of Mysore and to know the effectiveness of MOOCs on the learning efficiency of students. The subsequent part of the paper is organized as literature review, research questions, objectives, methodology, discussion and conclusion.
2. Literature survey and research gap
2.1 Technology based education and blended model of education
Morris and Hayes (1997) opined that the purpose of education can be achieved only when there is an existence of a collaborative learning environment. Such an environment enables the learners to build shared understandings. Further, it should be characterized by interaction, group activities, discussion forums and other activities which aids in improving communication and interpersonal skills among both teachers and learners. In addition to this, studies (Petraglia, 1998 and Bekele, 2010) noted that a collaborative learning environment creates a realistic teaching and learning environment and also enhances the learning abilities of students. Inducement of technology in education certainly creates a way for building collaborative learning environments (Glaser, 1990; McAlpine, 2000; Johnson and Johnson, 1992).
Another study (Wieser and Seeler, 2018) noted that the adoption of technology in the education system provides an opportunity to serve the students in an advanced way. The authors also pointed out that there is a need for initiatives to be taken by higher education institutions to induce various technology-based teaching-learning tools. Further, they also opined that due to the development of online learning models the virtual learning spaces are transforming into social learning spaces.
As noted by several authors (Liang and Chen, 2012; Kim Bonk, 2006; McKiernen and Wilson, 2014; Sursock, 2015), online education is becoming a standard instruction model in near future. Interestingly, they also opined that the online model of education certainly differentiates the mode of instruction with that of traditional methods of instruction.
In studies by Wu et al. (2010) and Wieser and Seeler (2018), it was found that due to distance and social isolation feelings among learners in online model of education, the success of online mode of education is unconfirmed. They also noted that there is an increasing trend in number of dropouts in online courses as compared to traditional class room courses offered by higher educational institutions.
Fearon et al. (2012), in their study suggested that the distance and social isolation feelings among learners can be removed by initiating a blended model of education which consists of both traditional and virtual models of education. For instance, past research (Garrison and Kanuka, 2004; Owston et al., 2008) found that blended learning methods are best suitable for redesigning education models in a technology-based learning environment. They also pointed out that the blended models possess the features and merits of both traditional and virtual teaching approaches.
The blended model of teaching may comprise face-to-face classes, flipped classrooms and online face-to-face classes. The use of these combinations in teaching may help the learners to gain a shared understanding realistically and to achieve success in their higher education life (Ayob et al., 2023).
Several authors (Harding et al., 2006; Wang et al., 2009; Yen and Lee, 2011) have noted a higher student involvement under a blended teaching environment which in turn increases the students' quality in terms of their commitment and perseverance. In other studies (Stacey and Gerbric, 2008; Williams et al., 2008; Gosper et al., 2010) it was noted that the success of blended learning can be measured in terms of quality of learning outcomes, learner experience, level of teacher satisfaction and time created for a teacher to involve in research. However, Poon (2014) highlighted that the rapidly changing technology poses a significant challenge in adopting the blended model of teaching. Therefore, higher education institutions should be well prepared for timely updating the education system on par with technological advancements.
2.2 Technology-based education and learning effectiveness
According to studies by many authors (Looney et al., 2008; Mashaw, 2012; Mitchell et al., 2017) opined that learning effectiveness means the degree to which the actual learning outcomes have been achieved. Further, it is a function of influential pedagogical practices and it is actually related to success and can be assessed through the students' appraisal of pedagogical and instructional design and their impact on students' attitudes, beliefs, emotions, expectations and their behavior at large.
Past studies (Chowdhury, 2020; Sharma et al., 2022) have noted that technology-mediated education enhances overall learning effectiveness and satisfaction among learners. They also emphasized that the ease of use of technology-mediated teaching tools rapidly impacts on the adoption of online education by learners. Further, the predeveloped educational videos moderately impact the learning effectiveness of students. For instance, in the studies by (Lizzio et al., 2002; Poon, 2014) it was highlighted that the predeveloped educational videos and contents certainly enhances the learning experience and student engagement. They also opined that predeveloped contents create and enhance a positive attitude among learners in their learning domain. However, in the studies by (Lizzio et al., 2002; Poon, 2014) it was highlighted that the major problems faced by students in a technology-mediated education system is related to limited internet downloading capacity and broadband width. In support of this, many authors (Chowdhury, 2020; Adams et al., 2021), have also emphasized that the readiness of students to pursue their education under a blended model is an essential aspect. Therefore, they suggested that the education institutions should provide training and demonstration to students on technology-mediated tools before they are actually implemented in education system.
2.3 MOOC and learning efficiency
Many authors in their study (Rasch and Schnotz, 2009; Steffens and Reiss, 2010) explained that the learning efficiency is key determinant of any education instruction design and which explains the capability of educational resources to help the learners in retaining, recalling and understanding a particular concept in a more effective manner.
According to a study by Onah et al. (2021), effective implementation of a blended model of education requires new methods which can be used simultaneously with face-to-face teachings like MOOCs. Further, MOOCs are a modern education innovation which can be used for distance learning. They also noted that it is a part of the blended teaching model in which learning takes place in both face-to-face and online settings. For instance, several authors in their study (Bates, 2012; Taneja and Goel, 2014; Singh and Sharma, 2021; Anand Shankar Raja and Kallarakal, 2021; Hossain et al., 2022) have highlighted that there is a high-level social collaborative learning and shared understandings in MOOC based education system.
Wang et al. (2022a, b) emphasized that MOOCs are significantly influencing the learning behavior of students due to its perceived usefulness and ease of use during the learning process.
In another study by Wang et al. (2022a, b), it was noted that even in the case of MOOCs, there is an increasing rate of dropouts due to various factors such as psychological, social, personal, course-related, time factors and the hidden costs. They also emphasized the need for motivation and interaction with students to minimize the dropout rates in MOOCs. Further, to enhance the degree of interaction and motivation among learners, appropriate course designs are very significant.
In another study (Cheng, 2022), it was found that the knowledge quality of students is influenced by system quality, interface design quality, degree of learner-teacher interaction and degree of collaboration in MOOCs. Further, they also noted that high quality MOOCs significantly influence on perceived usefulness, overall satisfaction and learning experience of students with continuance intention with MOOCs.
By analyzing the earlier literature on various aspects outlined above it was discovered that the majority of studies have been conducted on online teaching methods and blended models of teaching and fewer studies have focused on conceptual and foundational aspects of MOOCs. And no studies have made an attempt to examine the impact of MOOCs on the learning efficiency of students by considering the perception of both teachers and students. Therefore, the present study is intended to empirically analyze the effectiveness of MOOCs on the learning efficiency of students by considering the perception of both teachers and students who are involved in the MOOCs offered by the University of Mysore, India.
Based on the outcome of the literature survey, the following research questions are framed:
What is the perception of teachers on the effectiveness of MOOCs on learning efficiency?
What is the perception of students on the effectiveness of MOOC on learning efficiency?
Is MOOCs an ideal tool for designing the blended model of education?
What are the challenges involved in participating in MOOCs?
In order to answer the research questions developed, the study aimed at analyzing the perception of students and teachers regarding the effectiveness of MOOCs on the learning efficiency of students and also to evaluate MOOCs as an ideal tool for designing the blended model of education based on the perception of the target population.
3. Research methodology
The study followed quantitative descriptive research design to answer the research question. This method describes the characteristics of variables and also explains the relationship between variables (Bloomfield and Fisher, 2019). Here, researcher collected opinions of the respondents through structured questionnaire in close ended form of questions.
3.1 Participants
The present study examined the perception of stakeholders (student-teachers) of MOOCs conducted by the University of Mysore in 2021 (see Appendix 1 for list of MOOCs offered by University of Mysore). University of Mysore is one among the oldest universities in India and is the first state university which initiated MOOCs in its educational programs to students who were studying at graduation and above levels. The present study is descriptive quantitative research to gather the data concerning the objective of the study. The respondents are teachers who delivered MOOCs and also the students who are postgraduates and undergraduates who studied MOOCs offered by the University. It was observed that there are around 32,000 students who have taken MOOCs offered by the University during 2021 and these courses are taught by 52 teachers [3]. For the present study, respondents among these teachers and students were randomly selected. These respondents were from a varied academic domain such as commerce, management, science and humanities. They are also varied in their demographic profile such as age, gender and place of origin.
3.2 Sampling procedure
To determine the sample size, a two-step procedure was followed. In the first step, a preliminary study was conducted to know the population proportion concerning students who are learning through MOOCs in a class. A pilot study was conducted among students of University of Mysore. 280 students were asked whether they took up MOOCs during their learning or not. Out of 280 students, 52 students reported to have enrolled for MOOCs and this comes to 18.57% and is rounded off to 19%. Therefore, the population proportion for the present study is 19%.
In the second step, Krejcie and Morgan (1970) formula was employed to determine the sample size. The final sample size of students as per the formula is 211.60 and rounded off to 212. And on the other hand, all the teachers who taught under MOOCs of University are considered as teacher participants for the study. The final sample size of both students and teachers is 264.
3.3 Collection of data
Two survey questionnaires were designed, one each for the students and teachers. The questionnaires gathered the opinions from the students and teachers regarding the effectiveness of MOOCs on learning efficiency of students and the suitability of using MOOCs as the blended model of education.
The survey questionnaire was designed with a five-point Likert scale. Where five indicated ‘strongly agreed’ and one indicated ‘strongly disagreed’. The questionnaire collected demographic details of both students and teachers. Questions on the impact of MOOCs on learning efficiency were framed for the student respondents. Questions on MOOC as a tool for blended education and ease of learning and teaching were framed for teacher respondents. Questionnaires were distributed through Google forms. Two reminders with a gap of fifteen days were sent to fill the questionnaires by the participants.
Email-id of students and teachers who have been involved in MOOCs were collected from administrative office of the University. A total of 500 Google forms were sent to students out of which 368 responses were received. Out of the received responses, 357 were completed questionnaires. From 357 responses 212 responses were randomly selected for further analysis.
On the other hand, questionnaires were sent to all 52 teachers who have taught MOOCs. 49 filled questionnaires were received from the teachers out of which 43 were completed questionnaires and which are considered for the further analysis.
Data was collected and accounted for scientifically and then analyzed with the help of the statistical package for social sciences 2020 (SPSS 2020) version. Exploratory factor analysis and confirmatory factor analysis used to evaluate the validity and reliability of newly developed instruments. Descriptive statistics like percentage analysis and mean analysis and t-test were used to describe the perception of respondents. The outcome of data analysis is presented as demographic profile and opinion of respondents. Apart from the primary sources of information, the secondary sources of information were also employed in the present study to examine the conceptual aspects and to review the earlier literature and support the outcome of the present study.
3.4 Exploratory factor analysis
Exploratory factor analysis was conducted to identify the dimensions of perception on impact of MOOCs on learning efficiency of the students. Kaiser-Meyer-Olkin (KMO) was conducted to check sample adequacy and the results showed a value of 0.92, which is a good indicator of sampling adequacy (Pallant, 2011). Later, Bartlett's test of sphericity was conducted to check whether the correlation matrix fits an identity matrix and results showed a chi-square value which was significant (X2=1,523.68, p < 0.001), meaning correlation matrix is significantly different from identity matrix and is best suited for factor analysis. The exploratory factor analysis test was conducted with principal component analysis under the varimax method. The decision was based on Eigen value 1 and suppress factor loading less than 0.5. Table 1 shows the dimensions extracted, statements and factor loadings for each statement. A total of four dimensions were extracted, namely, (1) teachers' positive perception (2) teachers' negative perception (3) students' positive perception (4) students' negative perception with 63% of total variance explained. Statements with factor loading less than 0.5 are removed (highlighted in italic) from further analysis as they are not highly correlated with the dimension.
3.5 Confirmatory factor analysis
Measurement model was developed based on exploratory factor analysis and 29 items were considered for the structured model. Later, measurement model assessment, construct validity and reliability was checked using Cronbach's alpha, composite reliability and average variance extracted (AVE) value. Table 2 showed Cronbach's alpha values which ranged between 0.717 and 0.869 (I < 0.6). This indicates that the scale items are highly reliable. The AVE values are greater than 0.5, which indicates the existence of convergent validity among the items of each dimension. The Fornell-Larcker test was conducted to check discriminant validity among independent variables and Table 3 showed that square of AVE (Italic) is greater than values under that, therefore discriminant is not an issue. Table 4 shows the result of collinearity and values are less than 3, therefore multicollinearity does not exist. Finally, the goodness of fit test conducted for the measurement model and Table 5 showed normative fit indices (NFI) is 0.947 (greater than the threshold level of 0.9) and standardized root mean squared residual is 0.016 (lesser than 0.06), which showed the measurement model is a good fit.
4. Results and discussions
4.1 Demographic profile of the respondents
Table 6 depicts the demographic profile of the target respondents, which shows that males comprise the majority of the respondents in both the groups, that is, teachers (53.5%) and students (58.5%). The majority of teachers is falling under the age group of 30–49 years (72.1%) and is young. This shows that younger teachers are more involved in conducting MOOCs. Many teachers (48.8%) are having less than 3 years of experience in MOOCs. Among the students, the majority is from the commerce stream, i.e. 157 (74.1%). For studying through a blended model of education computer literacy is essential. Therefore, students were asked about their computer literacy level and it was found that 74.1% of the students have rated their level of computer literacy to be medium.
4.2 Students' perception of the influence of MOOC on learning efficiency
Students' feedback on teaching methods matters a lot in any education system. In the future, MOOCs are expected to become popular in the blended education environment. However, it is highly impossible to implement and adopt MOOCs without the acceptance of students. Therefore, by keeping this aspect in mind, students were asked about the influence of MOOCs on their learning efficiency in both positive and negative contexts.
4.3 The positive influence of MOOCs on learning efficiency (students' perspective)
To analyze the influence of MOOCs on learning efficiency, responses from students were gathered on both positive and negative aspects of MOOCs. Students are the ones who experience the impact of MOOCs in their learning life. Table 7 depicts the summary of descriptive and inferential statistics results on the responses of students towards the positive influence of MOOCs on learning efficiency. They strongly agreed that MOOCs provide an opportunity to learn from world-class teachers (M = 4.09, standard deviation = 0.654). This is because MOOCs are conducted online by expert teachers of many world-class institutions. This finding is in par with the findings of Bekele (2010), Stokes et al. (2015), Sun et al. (2016), Wong (2016), Kundu and Bej (2020).
Learners strongly believed that MOOCs create a high interest in learning (M = 4.00, SD = 0.725). This is due to the use of animated videos, pictures and examples during lectures (Liyanagunawardena et al., 2013). Students agreed (M = 3.90, SD = 0.743) that standardized certification and badges granted after the successful completion of MOOCs attracted them to enroll for the course. This is because learners usually enroll on any course with some outcome-based goals such as getting certification, gaining technical skills and so on. This was also emphasized in the study by Sinclair and Kalvala (2015). They also agreed (M = 3.88, SD = 0.767) that live video broadcasts in MOOCs replace the traditional model of teaching as they felt there is evidence of teacher presence during the lecture. This is also supported by the studies by Petraglia (1998) and Bekele (2010) where they emphasized that a collaborative learning environment creates a realistic teaching and learning environment.
Learners strongly agreed that (M = 4.01, SD = 0.743) sufficient quizzes, multiple choice questions tests, discussion forums and assignments encourage and improve their learning involvement. This is consistent with the findings of Shao and Chen (2021). They also agreed that the discussion forums, boards and chat rooms backed by social networking sites build learning contacts and associations among them (M = 3.92, SD = 0.718). For example, several authors (Morris and Hayes, 1997; Petraglia, 1998; Bekele, 2010) explored that learning contacts and associations build a collaborative learning environment and enhance brainstorming and knowledge sharing. This point was also stressed by Wieser and Seeler (2018) as they noted that virtual learning spaces are gradually transforming into social learning spaces. Similarly the study by Ayob et al. (2023) also supported this finding. Students also agreed that continuous feedback in MOOCs ensures the liveliness of learning (M = 3.93, SD = 0.728).
Further, they strongly agreed that visuals and examples used in video lectures and live sessions ensure their fast learning and retain the information for a longer time in their mind (M = 4.02, SD = 0.737). The overall mean and standard deviation are 3.97 and 0.48, respectively. This indicates MOOCs have a positive influence on the learning efficiency of students and the results of the t-test also showed statistical significance. The studies by Chowdhury (2020) and Sharma et al. (2022) also complemented this finding by suggesting that technology-mediated education enhances the overall learning effectiveness of the learners.
4.4 Negative influence of MOOCs on learning efficiency (students perspective)
Table 8 depicts the summary of descriptive and inferential statistics results on the responses of students towards the negative influence of MOOCs on learning efficiency. Students agreed (M = 3.78, SD = 0.776) that acquiring customized knowledge through MOOCs is difficult. This is because MOOCs are usually conducted on standardized subjects. If any students want to learn any specific knowledge in a customized manner, for them MOOCs are not highly suitable (Wang and Baker, 2015).
Learners agreed (M = 3.58, SD = 0.893) that MOOCs designed in standardized language discourage enrollment as they require them to be designed in regional language. Successful dissemination of knowledge from MOOCs is possible only when it is designed in bilingual or multilingual. Because delivery of knowledge in a standardized language creates stress on the students who are not well versed with that language and thereby it negatively influences their learning efficiency (Dreisiebner, 2018; Brisk et al., 2014). Students believed that there is no strict accountability during assessment in MOOCs (M = 3.68, SD = 0.871). This is because of the low teacher-student ratio. In MOOCs, one teacher handles the course for thousands of students at a time. This creates more burden on teachers in completion of the course, individual assessment of students and certification. This in turn impacts the accountability of students to teachers during the course (Shao and Chen, 2021). They also agreed (M = 3.71, SD = 0.924) that the MOOCs with prerecorded lectures cannot clear doubts in a lively manner.
Students agreed that (M = 3.74, SD = 0.901) MOOCs having long duration lectures negatively influence their learning attention. This is because students' attention is of short duration which ranges from fifteen to twenty-five minutes. Therefore, the video lectures presented in MOOCs should have optimum duration. If not, it will lose students' attention and negatively impact their learning efficiency (Mu et al., 2019).
Learners also agreed that there are few skill-oriented MOOCs (M = 3.61, 0.914). They also agreed (M = 3.60, SD = 0.976) that deadlines in quizzes, tests and assignments along with video lectures create stress during learning.
Further, students agreed that (M = 3.78, SD = 0.970) there is more scope for cheating during computer-graded quizzes and tests. This is because in many MOOCs the assessments, quizzes and tests are not proctored. This makes students attend them without any due care of cheating. This negatively influences the learning efficiency of students. This is consistent with the findings of (Costello et al., 2018; Balfour, 2013; Admiraal et al., 2015).
The overall mean and standard deviation are 3.69 and 0.66 respectively. This indicates that MOOCs have a negative influence on the learning efficiency of students. The results of the t-test showed statistical significance. However, it is noticed that the positive influence of MOOCs on learning efficiency was found to be greater than the negative influences (3.97 > 3.69).
4.5 Teachers' perception of the influence of MOOCs on learning efficiency
Students and Teachers are like two faces of the same coin. Without considering the teachers' opinions, the outcome of the present study shall not be complete. Therefore, the present study also collected the opinions of teachers on the influence of MOOCs on the learning efficiency of students. The descriptive statistics of their opinions regarding the positive and negative influence of MOOCs are presented in Tables 9 and Table 10.
The teachers strongly agreed (M = 4.49, SD = 0.551) that the MOOCs provide the opportunity to reach millions of learners at a time. This is because MOOCs are online-based and can offer education to students who are situated in different parts of the world at a time through the World Wide Web. This is consistent with the findings of (Wong, 2016; Bekele, 2010; Stokes et al., 2015; Sun et al., 2016).
Educators also have strongly agreed (M = 4.21, SD = 0.709) that MOOCs enable them to deliver information through video lectures, audio files, etc. in an attractive manner. This is because MOOCs allow educators to deliver their lectures by taking the help of animation pictures, instance videos etc. which is consistent with the findings of (Tailor et al., 2020).
Teachers also agreed that MOOCs with live video broadcast creates a traditional learning environment (M = 3.93, SD = 0.828). This is because live video broadcasting ensures the teacher's presence and interactions between teacher-students and vice versa. Educators strongly believed that (M = 4.23, SD = 0.841) sufficient quizzes, MCQ tests, discussion forums and assignments encourage and improve learner involvement. This is because assessment in MOOC is continuous.
Many MOOCs grade students' learning level by considering their continuous involvement in quizzes, MCQs and discussion forums n addition to their performance in the terminal exam. This is consistent with the findings of Ghazali et al. (2020). Teachers strongly agreed (M = 4.07, SD = 0.936) that the discussion forums, boards, and chat rooms backed by social networking sites build good teacher-student relationships. This is in conformity with the studies by Glaser (1990), Johnson and Johnson (1992), McAlpine (2000) and Wieser and Seeler (2018), who all noted that technology induced education creates a collaborative teaching environment which enhances the teacher-student relationship. They also agreed that (M = 3.91, SD = 0.971) the MOOCs enable them to understand the diversified learner's expectations. Educators strongly agreed (M = 4.09, SD = 0.921) that MOOCs make the teachers continuously updated as they face millions of students from a diversified environment. This is because teachers interact with students from different geographical areas and this creates the need for teachers to advance their knowledge and improve themselves to produce academically sound courses.
This is consistent with the findings of Wong (2016). They also strongly believed (M = 4.19, SD = 0.794) that the use of visuals and examples in video lectures and live sessions enables them to share knowledge more easily. The overall mean and standard deviation are 4.14 and 0.50, respectively. This indicates that the teachers' perceive MOOCs to have a positive influence on the learning efficiency of students. The result of the t-test shows statistical significance.
4.6 Negative influence of MOOCs on learning efficiency (teachers perspective)
The opinion towards negative aspects of MOOCs on learning efficiency is also collected from the teachers. Table 10 shows the results of descriptive statistics on the teachers' perception of the negative influence of MOOCs on learning efficiency. Teachers agreed that (M = 0.388, SD = 0.823) there is a low teacher-student ratio in MOOCs and that makes the burden on them to take individual attention to learners. This is because in MOOCs students may be from many parts of the world and their number may also be large. This creates a burden on taking individual attention.
The learning efficiency of students is also based on individual attention by teachers. If individual attention is not possible then the mode of instruction is invalid. This finding is consistent with the findings of (Bekele, 2010; Stokes et al., 2015; Sun et al., 2016). Educators agreed that (M = 3.63, SD = 1.047) designing of MOOC requires more technical skills than traditional skills and many teachers lack these skills.
If a teacher lacks technical skills such as the inclusion of audio-visuals, attractive animated pictures, video-editing and others, MOOCs will be ineffective and it can negatively influence learning efficiency. Poon (2014) also suggested that rapidly changing technology and its adoption is a challenge in a blended model of teaching. This requires a teacher and the education institutions to continuously update with newer technologies.
Teachers also agreed that (M = 3.84, SD = 0.949) designing MOOCs individually is difficult and needs to be funded by authorities. This is because there is a need for mobilizing various resources (video recorders, technical support team, content developers and resource persons) to conduct MOOCs effectively. If any of the resources required is short, then the course will not be effective and it will negatively influence the learning efficiency of students. This is consistent with the findings of (Bakogianni et al., 2020; Tailor et al., 2020; Ghazali et al., 2020). Teachers agreed that (M = 3.93, SD = 0.828) the interdisciplinary effort is essential to develop a MOOC but arrangement of the same is difficult. This is because MOOCs are getting more importance these days as they are holistic. Therefore, the course designed under MOOC should be created by experts from various disciplines. If this is the reality students are more attracted to the course and they will be more involved in learning. If not their involvement in learning will be less and which negatively influences their learning efficiency. This is consistent with the findings of (Bakogianni et al., 2020; Tailor et al., 2020).
Teachers agreed that (M = 3.95, SD = 0.899) development of computer-graded questions to assess the day-to-day improvement of students requires more time and effort. This is because MOOC designers are always under pressure during the course. Therefore, managing all course-related activities by a single person is highly difficult. Due to this, there may be a chance of missing day-to-day assessment questions by students and this discourages their involvement. They also agreed that (M = 3.86, SD = 0.966) there is more scope for cheating during watching videos and attending quizzes and tests. This is because there are fewer proctored controlled classes, assessment tests and quizzes in MOOCs. If this is the reality students do not consider the course seriously and which negatively influences their learning efficiency.
Educators also agreed (M = 3.72, SD = 1.076) that in MOOCs as there are more learners, peer assessment becomes a burden. This negatively influences on learning efficiency of students due to the low-quality assessment of students in MOOCs. This is consistent with the findings of (Camilleri and Tannhauser, 2013). Teachers are neutral (M = 2.74, SD = 1.415) in their opinion that prerecorded videos have less impact on learning efficiency than live sessions in MOOCs. The overall mean and standard deviation are 3.690 and 0.555 respectively. This indicates that MOOCs also have a negative influence on the learning efficiency of students as per teachers' opinions and the results of the t-statistic are statistically significant.
Independent sample t test conducted to identify whether there is a significant difference in the mean value of positive perception and negative perception. The test result tabulated in Table 11, which showed a significant difference in the mean value of positive perception and negative perception of MOOCs among students, further it showed that positive perception (M = 3.97) is higher than negative perception (M = 3.69) among students. Table 8 also revealed a significant difference in the perception about MOOCs among teachers. It exhibits that positive perception (M = 4.14) is higher than negative perception (M = 3.69). By analyzing the responses of both students and teachers on MOOCs' positive and negative aspects on learning efficiency it was found that it is more positively influencing learning efficiency.
5. Conclusion and recommendation
MOOCs are an emerging way of offering both formal as well as informal education to all those having the thirst to learn. It has made a positive impact on teaching-learning efficiency. The application of technology-based teaching tools in MOOCs improves the quality of learning. Both students and teachers have positive views towards the influence of MOOCs on learning efficiency. However, with the current pace of technological advancements, there is always room for improvement as far as MOOC-based learning is concerned.
It is evident that MOOC-based education has more significant benefits such as it gives an opportunity to the learners to learn from the learned academia across the globe and also provides education to all without any restrictions of any kind (Wong, 2016). However, for the effective implementation of such tech-based education tools in a country like India, there is a need to consider a host of factors. These include aspects relating to providing infrastructure facilities such as video recording studios, allowing private institutes to conduct MOOCs (currently, only the higher education institutions which meet certain quality benchmarks are allowed to design and develop MOOCs) and developing more MOOCs projects other than a few existing ones like NPTEL, mooKIT, IITBX and SWAYAM.
In addition to this, to make MOOCs more effective certain technological advancements are to be focused on. Such aspects are relating to the implementation of proctored controlled classes, proctored controlled assessments and expert system-based quizzes to increase the sanctity of the assessment of students. Further, to reach many students from different geographical parts, multilingual courses should be offered. As a teacher's capacity is limited by the language barriers, artificial intelligence and other allied technologies can be relied on to translate the course to various regional languages.
In order to overcome the limitations of MOOCs relating to absence of live interactions, live classes should be conducted more frequently along with the prerecorded video lectures. This makes the experience of both teachers and students similar to that experienced in traditional classes.
The study also provided the proof that the MOOCs are having a more positive influence on learning efficiency. The study also points out that MOOCs can be used in a blended model of education, but not independent of the traditional classroom teaching. This is because MOOCs are not an alternative to traditional education. However, the people who are willing to be lifelong learners by passion can adopt MOOCs to achieve their dreams.
6. Limitations, contributions and implications
This study made a sincere effort in uncovering the student-teacher perception regarding the effectiveness of MOOCs in enhancing the learning effectiveness. Both the respondent groups approved the positive impact of MOOCs on the learning efficiency of students. However, the study collected the opinion from the students and teachers of only one State University. MOOCs are being offered by many other State and Central Universities and institutions of national prominence which are both public and private. Also, the courses offered by international entities such as edX, Coursera and others are also accessed by the students in India. This study, however, excludes such courses. A further study can therefore be undertaken to evaluate and review the effectiveness of other online courses offered by various other entities and institutions across the country. Nevertheless, the present study emphasizes the influence of MOOCs on the learning efficiency of the student community and thus urges the teaching fraternity to complement their teaching efforts by leveraging MOOCs.
The outcome of this study helps higher education institutes by providing inputs in implementing MOOCs as the part of blended model of education. Further, it also helps to the teachers in overcoming the pitfalls and challenges faced in MOOCs based education.
7. Research ethics and consent
Present study involved human participants; therefore we performed research procedure in accordance with principles stated in Declaration of Helsinki. We obtained ethical approval prior to study from the Institutional Review Board of DoS in Commerce of Mysore University. A committee conducted meeting on 02-06-2022 and approved our questionnaire. Further, respondents were informed about the purpose of the study and free consent was obtained from the respondents before the collection of data.
Factors, subitems and loadings
| Dimensions | Statements | Factor loadings |
|---|---|---|
| Teachers' Positive Perception (TPP) | MOOCs provides opportunity to learn from world class teachers (TPP1) | 0.720 |
| It enhances learning interest through video lectures, audio files, etc. (TPP2) | 0.762 | |
| Standardized certification and badges attracted me to enroll to MOOCs (TPP3) | 0.616 | |
| MOOCs replaces traditional teaching model with live video broadcast which identifies evidence of teacher's presence (TPP4) | 0.681 | |
| Sufficient quizzes, MCQ tests, discussion forums and assignments encourages and improves learning involvement (TPP5) | 0.700 | |
| Discussion forums, boards, chat rooms backed by social networking sites builds learning contacts and association (TPP6) | 0.752 | |
| It ensures liveliness through continuous feedback (TPP7) | 0.702 | |
| Visuals, examples used in video lectures and live sessions ensure fast learning and retaining the information for longer time in the mind (TPP8) | 0.702 | |
| Teachers' Negative Perception (TNP) | In MOOCs, acquiring customized knowledge is difficult as course designed is standardized (TNP1) | 0.643 |
| MOOCs in standardized language discourage enrollment (TNP2) | 0.700 | |
| There is no strict accountability during assessment in MOOCs (TNP3) | 0.666 | |
| MOOCs with prerecorded lectures cannot clear doubts in a lively manner (TNP4) | 0.780 | |
| Many MOOC lectures are of long duration which loses the interest of the learners (TNP5) | 0.755 | |
| Many MOOCs are academic certificate oriented and not skill oriented (TNP6) | 0.796 | |
| Deadlines in quiz, tests and assignments along with videos lectures creates stress during learning (TNP7) | 0.722 | |
| There is more scope for cheating during computer graded quizzes and tests (TNP8) | 0.748 | |
| Students' Positive Perception (SPP) | MOOC provides opportunity to teach millions of learners at a time (SPP1) | 0.419 |
| It eases delivery of information through video lectures, audio files, etc. in an attractive manner (SPP2) | 0.727 | |
| MOOCs with live video broadcast creates traditional learning environment (SPP3) | 0.728 | |
| Sufficient quizzes, MCQ tests, discussion forums and assignments encourages and improves learner's involvement (SPP4) | 0.664 | |
| Discussion forums, boards, chat rooms backed by social networking sites builds good teacher-students relationship (SPP5) | 0.733 | |
| MOOCs enable the teachers to understand the diversified learner's expectation (SPP6) | 0.713 | |
| MOOCs make the teachers continuously updated as they face millions of students from diversified environment (SPP7) | 0.632 | |
| Visuals, examples used in video lectures and lives sessions enables teachers to easy convey of knowledge (SPP8) | 0.162 | |
| Students' Negative Perception (SNP) | In MOOCs, there is a low teacher-student ratio which creates burden on individual attention towards learners (SNP1) | 0.764 |
| Designing of MOOCs require more technical skills than traditional skills and many teachers' lack these skills (SNP2) | 0.753 | |
| Designing MOOCs individually is difficult and needs to be funded by authorities (SNP3) | 0.614 | |
| Interdisciplinary effort is essential to develop a MOOC but arrangement is difficult (SNP4) | 0.543 | |
| Developing computer graded questions to assess day-to-day improvement requires more time and effort (SNP5) | 0.627 | |
| There is more scope for cheating in watching videos, attending quizzes and tests (SNP6) | 0.676 | |
| In MOOCs, as there are more learners, peer assessment becomes burden (SNP7) | 0.699 | |
| Prerecorded videos less impact on learning efficiency than live sessions in MOOCs (SNP8) | −0.261 |
Source(s): Compiled by authors
Construct reliability and validity
| Cronbach alpha | Composite reliability | AVE | |
|---|---|---|---|
| SNP | 0.869 | 0.868 | 0.518 |
| SPP | 0.850 | 0.868 | 0.588 |
| TNP | 0.717 | 0.856 | 0.537 |
| TPP | 0.754 | 0.811 | 0.385 |
Source(s): Compiled by authors
Discriminant validity-Fornell-Larcker criterion
| SNP | SPP | TNP | TPP | |
|---|---|---|---|---|
| SNP | 0.720 | |||
| SPP | 0.411 | 0.699 | ||
| TNP | −0.093 | 0.143 | 0.587 | |
| TPP | −0.023 | 0.198 | 0.521 | 0.620 |
Source(s): Compiled by authors
Collinearity statistics
| TPP | TNP | SPP | SNP | ||||
|---|---|---|---|---|---|---|---|
| Items | VIF | Items | VIF | Items | VIF | Items | VIF |
| TPP1 | 1.598 | TNP1 | 2.815 | SPP2 | 2.124 | SNP1 | 1.513 |
| TPP2 | 2.428 | TNP2 | 2.565 | SPP3 | 1.465 | SNP2 | 1.724 |
| TPP3 | 1.843 | TNP3 | 1.742 | SPP4 | 1.688 | SNP3 | 1.558 |
| TPP4 | 1.600 | TNP4 | 2.454 | SPP5 | 1.807 | SNP4 | 2.182 |
| TPP5 | 1.687 | TNP5 | 1.792 | SPP6 | 1.864 | SNP5 | 2.010 |
| TPP6 | 1.937 | TNP6 | 1.961 | SPP7 | 1.571 | SNP6 | 2.015 |
| TPP7 | 1.559 | TNP7 | 1.586 | SNP7 | 1.728 | ||
| TPP8 | 1.213 | TNP8 | 1.261 | ||||
Source(s): Compiled by authors
Model fit
| Structured model | Estimated model | |
|---|---|---|
| SRMR | 0.016 | 0.016 |
| d_ULS | 7.108 | 7.108 |
| d_G | 2.283 | 2.283 |
| Chi-square | 958.839 | 958.839 |
| NFI | 0.947 | 0.947 |
Source(s): Compiled by authors
Demographic profile of respondents
| Demographic profile of teachers' | N (%) | Demographic profile of students' | N (%) | ||
|---|---|---|---|---|---|
| Gender | Male | 23 (53.5) | Gender | Male | 124 (58.5) |
| Female | 20 (46.5) | Female | 88 (41.5) | ||
| Age (Years) | Below 29 | 7 (16.3) | Stream | Science | 29 (13.7) |
| 30–49 | 31 (72.1) | Commerce | 157 (74.1) | ||
| 50 and Above | 5 (11.6) | Humanities | 13 (6.1) | ||
| Academic experience (Years) | 0–10 | 18 (41.9) | Engineering | 7 (3.3) | |
| 10–20 | 17 (39.5) | Other | 6 (2.8) | ||
| 20 and Above | 8 (18.6) | Computer Literacy | High | 17 (8.0) | |
| Experience in MOOC(Years) | Less than 3 | 21 (48.8) | Medium | 157 (74.1) | |
| 3–5 | 11 (25.6) | Low | 38 (17.9) | ||
| 5 and Above | 11 (25.6) |
Source(s): Primary data
Positive influence of MOOC on learning efficiency
| Statements on positive influence of MOOC on learning | N | Mean | Std. | t-value | Sig (two-tailed) |
|---|---|---|---|---|---|
| SPP1 | 212 | 4.09 | 0.654 | 24.382 | 0.000 |
| SPP2 | 212 | 4.00 | 0.725 | 19.980 | 0.000 |
| SPP3 | 212 | 3.90 | 0.743 | 17.497 | 0.000 |
| SPP4 | 212 | 3.88 | 0.767 | 16.750 | 0.000 |
| SPP5 | 212 | 4.01 | 0.743 | 19.731 | 0.000 |
| SPP6 | 212 | 3.92 | 0.718 | 18.747 | 0.000 |
| SPP7 | 212 | 3.93 | 0.728 | 18.576 | 0.000 |
| SPP8 | 212 | 4.02 | 0.737 | 20.096 | 0.000 |
| Overall Mean | 212 | 3.9734 | 0.47882 | 29.250 | 0.000 |
Source(s): Primary data
Negative influence of MOOC on learning efficiency
| Statements on negative influence of MOOC on learning | N | Mean | Std. | t-value | Sig (Two-tailed) |
|---|---|---|---|---|---|
| SNP1 | 212 | 3.78 | 0.776 | 14.580 | 0.000 |
| SNP2 | 212 | 3.58 | 0.893 | 9.487 | 0.000 |
| SNP3 | 212 | 3.68 | 0.871 | 11.351 | 0.000 |
| SNP4 | 212 | 3.71 | 0.924 | 11.174 | 0.000 |
| SNP5 | 212 | 3.74 | 0.901 | 11.913 | 0.000 |
| SNP6 | 212 | 3.61 | 0.914 | 9.691 | 0.000 |
| SNP7 | 212 | 3.60 | 0.976 | 8.939 | 0.000 |
| SNP8 | 212 | 3.78 | 0.970 | 11.681 | 0.000 |
| Overall Mean | 212 | 3.6918 | 0.66415 | 14.877 | 0.000 |
Source(s): Primary data
Teachers' perception of the positive influence of MOOC on learning efficiency
| Statements on positive influence of MOOC on learning | N | Mean | Std. | t-value | Sig (two-tailed) |
|---|---|---|---|---|---|
| TPP1 | 43 | 4.49 | 0.551 | 17.718 | 0.000 |
| TPP2 | 43 | 4.21 | 0.709 | 11.184 | 0.000 |
| TPP3 | 43 | 3.93 | 0.828 | 7.368 | 0.000 |
| TPP4 | 43 | 4.23 | 0.841 | 9.616 | 0.000 |
| TPP5 | 43 | 4.07 | 0.936 | 7.495 | 0.000 |
| TPP6 | 43 | 3.91 | 0.971 | 6.123 | 0.000 |
| TPP7 | 43 | 4.09 | 0.921 | 7.782 | 0.000 |
| TPP8 | 43 | 4.19 | 0.794 | 9.789 | 0.000 |
| Overall Mean | 43 | 4.1395 | 0.50534 | 14.787 | 0.000 |
Source(s): Primary data
Teachers' perception of negative influence of MOOC on learning efficiency
| Statements on positive influence of MOOC on learning | N | Mean | Std. | t-value | Sig (Two-tailed) |
|---|---|---|---|---|---|
| TNP1 | 43 | 3.88 | 0.823 | 7.045 | 0.000 |
| TNP2 | 43 | 3.63 | 1.047 | 3.932 | 0.000 |
| TNP3 | 43 | 3.84 | 0.949 | 5.782 | 0.000 |
| TNP4 | 43 | 3.93 | 0.828 | 7.368 | 0.000 |
| TNP5 | 43 | 3.95 | 0.899 | 6.959 | 0.000 |
| TNP6 | 43 | 3.86 | 0.966 | 5.843 | 0.000 |
| TNP7 | 43 | 3.72 | 1.076 | 4.392 | 0.000 |
| TNP8 | 43 | 2.74 | 1.415 | −1.19 | 0.000 |
| Overall Mean | 43 | 3.6905 | 0.55508 | 8.062 | 0.000 |
Source(s): Primary data
Comparative mean result
| Sample | Categories | Mean | T value | Sig. | Remark |
|---|---|---|---|---|---|
| Students' Perception | Negative | 3.69 | 15.67 | 0.000 | HS |
| Positive | 3.97 | ||||
| Teachers' Perception | Negative | 3.69 | 19.24 | 0.000 | HS |
| Positive | 4.14 |
Source(s): Primary data
List of MOOCs offered by University of Mysore
| Si. No. | Name of the MOOC | No. of students enrolled |
|---|---|---|
| 1. | Business statistics | 3,141 |
| 2. | Tourism and travel management | 569 |
| 3. | Business research methodology | 3,366 |
| 4. | Hospitality industry in tourism | 856 |
| 5. | Geography of tourism | 796 |
| 6. | Geography of India | 2,820 |
| 7. | Plant pathology and soil health | 1,705 |
| 8. | Tourism resources of India | 540 |
| 9. | Tourism, transport & travel services | 604 |
| 10. | International tourism destinations | 753 |
| 11. | Food microbiology | 1,640 |
| 12. | Food chemistry | 1,637 |
| 13. | Human genetics | 1,588 |
| 14. | Virology | 1,417 |
| 15. | Microbial physiology and metabolism | 660 |
| 16. | International business | 1,990 |
| 17. | Biography | 1,038 |
| 18. | Food preservation & technology | 1809 |
| 19. | Food & nutrition | 4,603 |
| Total no. of students enrolled | 32,192 |
Notes
https://www.coursera.org, as of 5 May 2022.
https://www.mooc.org/, as of 28 April 2022.
https://www.uni-mysore.ac.in/, as of 7 March 2022.
References
Adams, D., Tan, M.H.J. and Sumintono, B. (2021), “Students' readiness for blended learning in a leading Malaysian private higher education institution”, Interactive Technology and Smart Education, Vol. 18 No. 4, pp. 515-534, doi: 10.1108/ITSE-03-2020-0032.
Admiraal, W., Huisman, B. and Pilli, O., (2015), “Assessment in massive open online courses”, Electronic Journal of E-Learning, Vol. 13 No. 4, pp. 207-216.
Ahmed, M.M., Sultana, N., Astri Dwi Jayanti, S., Mardoni, Y. and Helmiatin, H. (2021), “Attitude towards entrepreneurship development courses of MOOCs”, Asian Association of Open Universities Journal, Vol. 16 No. 1, pp. 129-141, doi: 10.1108/AAOUJ-09-2020-0081.
Anand Shankar Raja, M. and Kallarakal, T.K. (2021), “COVID-19 and students perception about MOOCs” a case of Indian higher educational institutions”, Interactive Technology and Smart Education, Vol. 18 No. 3, pp. 450-474, doi: 10.1108/ITSE-07-2020-0106.
Ayob, H.H., Daleure, G., Solovieva, N., Minhas, W. and White, T. (2023), “The effectiveness of using blended learning teaching and learning strategy to develop students’ performance at higher education”, Journal of Applied Research in Higher Education, Vol. 15 No. 3, pp. 650-662, https://doi.org/10.1108/JARHE-09-2020-0288.
Ayoub, A., Amin, R. and Wani, Z.A. (2020), “Contribution of developed countries towards MOOCs: an exploration and assessment from a representative platform Coursera”, Asian Association of Open Universities Journal, Vol. 15 No. 2, pp. 251-262, doi: 10.1108/AAOUJ-03-2020-0016.
Bakogianni, E., Tsitouridou, M. and Kyridis, A. (2020), “MOOCs in teachers' professional development: examining teacher readiness”, Academia - Boletin de la Real Academia de BellasArtes de San Fernando, Vol. 18, pp. 9-40.
Balfour, S.P. (2013), “Assessing writing in MOOCs: automated essay scoring and calibrated peer review”, Research and Practice in Assessment, Vol. 8, pp. 40-48.
Bates, T. (2012), “What’s right and what’s wrong about coursera-style MOOCs”, Information and Management, Vol. 5 No. 1, doi: 10.1016/j.im.2011.09.007.
Bekele, T.A. (2010), “Motivation and satisfaction in internet-supported learning environments: a review”, Educational Technology and Society, Vol. 13 No. 2, pp. 116-127.
Bloomfield, J. and Fisher, M.J. (2019), “Quantitative research design”, Journal of the Australasian Rehabilitation Nurses Association, Vol. 22 No. 2, pp. 27-30.
Bordoloi, R., Das, P. and Das, K. (2021), “Perception towards online/blended learning at the time of Covid-19 pandemic: an academic analytics in the Indian context”, Asian Association of Open Universities Journal, Vol. 16 No. 1, pp. 41-60, doi: 10.1108/AAOUJ-09-2020-0079.
Brisk, M.E., Homza, A. and Smith, J. (2014), “Preparation to practice: what matters in supporting linguistically responsive mainstream teachers”, Research on Preparing Preservice Teachers to Work Effectively with Emergent Bilinguals (Advances in Research on Teaching), Emerald Group Publishing Limited, Bingley, Vol. 21, pp. 167-199.
Camilleri, A.F. and Tannhäuser, A.-C. (2013), “Assessment and recognition of open learning”, in Openness and Education (Advances in Digital Education and Lifelong Learning), Emerald Group Publishing Limited, Bingley, Vol. 1, pp. 85-118.
Cheng, Y.-M. (2022), “Which quality determinants cause MOOCs continuance intention? A hybrid extending the expectation-confirmation model with learning engagement and information systems success”, Library Hi Tech, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/LHT-11-2021-0391.
Chowdhury, F. (2020), “Blended learning: how to flip the classroom at HEIs in Bangladesh?”, Journal of Research in Innovative Teaching and Learning, Vol. 13 No. 2, pp. 228-242, doi: 10.1108/JRIT-12-2018-0030.
Costello, E., Holland, J. and Kirwan, C. (2018), “The future of online testing and assessment: question quality in MOOCs”, International Journal of Educational Technology in Higher Education, Vol. 15 No. 1, pp. 1-14.
Dreisiebner, S. (2018), “Content and instructional design of MOOCs on information literacy: a comprehensive analysis of 11 xMOOCs”, Information and Learning Sciences, Vol. 120 Nos 3/4, pp. 173-189, doi: 10.1108/ILS-08-2018-0079.
Fearon, C., Starr, S. and McLaughlin, H. (2012), “Blended learning in higher education (HE): conceptualizing key strategic issues within a business school”, Development and Learning in Organisations, Vol. 26 No. 2, pp. 19-22, doi: 10.1108/14777281211201196.
Garrison, D.R. and Kanuka, H. (2004), “Blended learning: uncovering its transformative potential in higher education”, The Internet and Higher Education, Vol. 7 No. 2, pp. 95-105.
Ghazali, N., Nordin, M.S., Abdullah, A. and Ayub, A.F.M. (2020), “The relationship between students' MOOC-efficacy and meaningful learning”, Asian Journal of University Education, Vol. 16 No. 3, pp. 89-101.
Glaser, R. (1990), “The reemergence of learning theory within instructional research”, American Psychologist, Vol. 45 No. 1, p. 29.
Gosper, M., McNeill, M., Phillips, R., Preston, G., Woo, K. and Green, D. (2010), “Web-based lecture technologies and learning and teaching: a study of change in four Australian universities”, Australasian Journal of Educational Technology, Vol. 26 No. 8.
Harding, A., Engelbrecht, J., Lazenby, K. and Roux, I. (2006), “Blended learning in undergraduate mathematics”, in Bonk, C.J. and Graham, C.R. (Eds), The Handbook of Blended Learning: Global Perspectives, Local Designs, Pfeiffer, San Francisco, CA, pp. 400-418.
Hossain, M.N., Hossain, M.Y., Bao, Y., Kumar, N. and Hoque, M.R. (2022), “A proposed model to design MOOCs through the lens of addressing graduate skill gap”, Higher Education, Skills and Work-Based Learning, Vol. 12 No. 5, pp. 963-982, doi: 10.1108/HESWBL-04-2021-0070.
Johnson, D.W. and Johnson, R.T. (1992), “Positive interdependence: key to effective cooperation”, Interaction in Cooperative Groups: The Theoretical Anatomy of Group Learning, Cambridge University Press, pp. 174-199.
Kim, K.-J. and Bonk, C.J. (2006), “The future of online teaching and learning in higher education: the survey says”, Educause Quarterly, No. 4, pp. 22-30.
Krejcie, R.V. and Morgan, D.W. (1970), “Determining sample size for research activities”, Educational and Psychological Measurement, Vol. 30 No. 3, pp. 607-610.
Kundu, A. and Bej, T. (2020), “Perceptions of MOOCs among Indian State University students and teachers”, Journal of Applied Research in Higher Education, Vol. 12 No. 5, pp. 1095-1115.
Liang, R. and Chen, D.-T.V. (2012), “Online learning: trends, potential and challenges”, Creative Education, Vol. 3 No. 8, pp. 1332-1335, doi: 10.4236/ce.2012.38195.
Liyanagunawardena, T., Adams, A. and Williams, S. (2013), “MOOCs: a systematic study of the published literature 2008-2012”, The International Review of Research in Open and Distance Learning, Vol. 14 No. 3, pp. 202-227.
Lizzio, A., Wilson, K. and Simons, R. (2002), “University students' perceptions of the learning environment and academic outcomes: implications for theory and practice”, Studies in Higher Education, Vol. 27 No. 1, pp. 27-52.
Looney, C.A., Akbulut, A.Y. and Poston, R.S. (2008), “Understanding the determinants of service channel preference in the early stages of adoption: a social cognitive perspective on online brokerage services”, Decision Sciences, Vol. 39 No. 4, pp. 821-857.
Mashaw, B. (2012), “A model for measuring effectiveness of an online course”, Decision Sciences Journal of Innovative Education, Vol. 10 No. 2, pp. 189-221.
McAlpine, I. (2000), “Collaborative learning online”, Distance Education, Vol. 21 No. 1, p. 6680, doi: 10.1080/0158791000210105.
McKiernen, P. and Wilson, D. (2014), “Strategic choice: taking business out of b-schools”, in Pettigrew, A., Cornuel, E. and Hommel, U. (Eds), The Institutionial Development of Business Schools, 1st ed., Oxford University Press, Oxford, pp. 248-269.
Mitchell, A., Petter, S. and Harris, A.L. (2017), “Learning by doing: twenty successful active learning exercises for information systems courses”, Journal of Information Technology Education. Innovations in Practice, Vol. 16, p. 21.
Morris, R. and Hayes, C. (1997), “Small group work: are group assignments a legitimate form of assessment” in Pospisil, R. and Willcoxson, L. (Eds), Learning through Teaching, Murdoch: Proceedings of the 6th Annual Teaching Learning Forum, p. 229233.
Mu, S., Cui, M., Wang, X.J., Qiao, J.X. and Tang, D.M. (2019), “Learners' attention preferences of information in online learning: an empirical study based on eye-tracking”, Interactive Technology and Smart Education, Vol. 16 No. 3, pp. 186-203.
Onah, D.F., Pang, E.L. and Sinclair, J.E. (2021), “Investigating self-regulation in the context of a blended learning computing course”, The International Journal of Information and Learning Technology, Vol. 39 No. 1, pp. 50-69, doi: 10.1108/IJILT-04-2021-0059 (In this issue).
Owston, R., Wideman, H., Murphy, J. and Lupsheyuk, D. (2008), “Blended teacher professional development: a synthesis of three program evaluations”, Internet and Higher Education, Vol. 11, pp. 201-210.
Pallant, J. (2011), SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS, Routledge, New York.
Petraglia, J. (1998), Reality by Design: The Rhetoric and Technology of Authenticity in Education, Routledge, Mahwah, NJ.
Poon, J. (2014), “A cross-country comparison on the use of blended learning in property education”, Property Management, Vol. 32 No. 2, pp. 154-175.
Rasch, T. and Schnotz, W. (2009), “Interactive and non-interactive pictures in multimedia learning environments: effects on learning outcomes and learning efficiency”, Learning and Instruction, Vol. 19 No. 5, pp. 411-422.
Shao, Z. and Chen, K. (2021), “Understanding individuals' engagement and continuance intention of MOOCs: the effect of interactivity and the role of gender”, Internet Research, Vol. 31 No. 4, pp. 1262-1289.
Sharma, S., Vaidya, A. and Deepika, K. (2022), “Effectiveness and satisfaction of technology‐mediated learning during global crisis: understanding the role of pre-developed videos”, On the Horizon, Vol. 30 No. 1, pp. 28-43, doi: 10.1108/OTH-04-2021-0057.
Sinclair, J. and Kalvala, S. (2015), “Engagement measures in massive open online courses”, in International Workshop on Learning Technology for Education in Cloud, Springer, Cham, pp. 3-15.
Singh, A. and Sharma, A. (2021), “Acceptance of MOOCs as an alternative for internship for management students during COVID-19 pandemic: an Indian perspective”, International Journal of Educational Management, Vol. 35 No. 6, pp. 1231-1244, doi: 10.1108/IJEM-03-2021-0085.
Stacey, E. and Gerbric, P. (2008), “Success factors for blended learning”, Proceedings Ascilite Melbourne, pp. 964-968.
Steffens, D. and Reiss, M. (2010), “Performance of blended learning in university teaching”, Determinants and Challenges. Eleed, Vol. 6 No. 1, pp. 1-8.
Stokes, C.W., Towers, A.C., Jinks, P.V. and Symington, A. (2015), “Discover dentistry: encouraging wider participation in dentistry using a massive open online course (MOOC)”, British Dental Journal, Vol. 219 No. 2, pp. 81-85.
Sun, X., Zhou, W., Xiang, Q., Cui, B. and Jin, Y. (2016), “Research on big data analytics technology of MOOC”, Computer Science & Education (ICCSE), August 2016 11th, International Conference on IEEE, pp. 64-68.
Sursock, A. (2015), Trends 2015: Learning and Teaching in European Universities. EUA Publications, EUA European University Association Asbl.
Tailor, R.K., Ashoka, M.L., Acharya, P. and Abhishek, N. (2020), “Suitability of accounting education to current market”, Indian Journal of Commerce and Management Studies, Vol. 11 No. 2, pp. 14-23.
Taneja, S. and Goel, A. (2014), “MOOC providers and their strategies”, International Journal of Computer Science and Mobile Computing, Vol. 3 No. 5, pp. 222-228.
Viswanathan, R. (2012), “Teaching and learning through MOOC”, Frontiers of Language and Teaching, Vol. 3 No. 1, pp. 32-40.
Wang, Y. and Baker, R. (2015), “Content or platform: why do students complete MOOCs”, MERLOT Journal of Online Learning and Teaching, Vol. 11 No. 1, pp. 17-30.
Wang, M., Shen, R., Novak, D. and Pan, X. (2009), “The impact of mobile learning on students' learning behaviours and performance: report from a large blended classroom”, British Journal of Educational Technology, Vol. 40 No. 4, pp. 673-695.
Wang, K., van Hemmen, S.F. and Criado, J.R. (2022a), “The behavioural intention to use MOOCs by undergraduate students: incorporating TAM with TPB”, International Journal of Educational Management, Vol. 36 No. 7, pp. 1321-1342, doi: 10.1108/IJEM-11-2021-0446.
Wang, W., Zhao, Y., Wu, Y.J. and Goh, M. (2022b), “Factors of dropout from MOOCs: a bibliometric review”, Library Hi Tech, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/LHT-06-2022-0306.
Wieser, D. and Seeler, J.M. (2018), “Online, not distance education: the merits of collaborative learning in online education”, in The Disruptive Power of Online Education, Emerald Publishing Limited, Bingley, pp. 125-146, doi: 10.1108/978-1-78754-325-620181008.
Williams, N.A., Bland, W. and Christie, G. (2008), “Improving student achievement and satisfaction by adopting a blended learning approach to inorganic chemistry”, Chemistry Education Research and Practice, Vol. 9 No. 1, pp. 43-50.
Wong, B.T.-M. (2016), “Factors leading to effective teaching of MOOCs”, Asian Association of Open Universities Journal, Vol. 11 No. 1, pp. 105-118, doi: 10.1108/AAOUJ-07-2016-0023.
Wu, J.-H., Tennyson, R.D. and Hsia, T.-L. (2010), “A study of student satisfaction in a blended e-learning system environment”, Computers and Education, Vol. 55 No. 1, pp. 155-164, doi: 10.1016/j.compedu.2009.12.012.
Yen, J.C. and Lee, C.Y. (2011), “Exploring problem-solving patterns and their impact on learning achievement in a blended learning environment”, Computers and Education, Vol. 56 No. 1, pp. 138-145.
Further reading
Garrido, M., Koepke, L., Anderson, S., Felipe Mena, A., Macapagal, M. and Dalvit, L. (2016), “The advancing MOOCs for development initiative: an examination of MOOC usage for professional workforce development outcomes in Colombia, the Philippines, & South Africa”, Technology and Social Change Group, Vol. 11, pp. 1-122.
Preston, G., Phillips, R., Gosper, M., McNeill, M., Woo, K. and Green, D. (2010), “Web-based lecture technologies: highlighting the changing nature of teaching and learning”, Australasian Journal of Educational Technology, Vol. 26 No. 6, pp. 717-728, doi: 10.14742/ajet.1038.
Wahid, R. and Sani, M.A.M. (2015), “MOOCs and youth employment strategy”, The Social Sciences, Vol. 10 No. 7, pp. 1726-1731.
Wong, B.T.-M. (2021), “A survey on the pedagogical features of language massive open online courses”, Asian Association of Open Universities Journal, Vol. 16 No. 1, pp. 116-128, doi: 10.1108/AAOUJ-03-2021-0028.
Acknowledgements
The authors are thankful to Prof. Ashoka M L and Dr Parameshwar Acharya for their committed support and motivation towards the completion of this research.