Factors Influencing Diffusion and Continuance of Massive Open Online Course (MOOC)

 

Ms. Vaidehi R, Dr. Girija T

SSN School of Management, Chennai

 

ABSTRACT:

Distance education is not new and has been there as one of the means of education since ages. We are witnessing an age of digitalization and thereby people get access to products and services at a mouse click, Massive Open Online Courses (MOOCs) are no exceptions to it. Usage of MOOCs is determined by a complex of factors such as intrinsic motivation of participants, instructors’ willingness to adapt to MOOCs and to name a few. There are hundreds and thousands of courses available on MOOC platform that are being offered for free of cost and are thousands of participants enrolling for such courses. But given the fact that completion rate of such courses is far less; the most daunting problem is, how can we get participants to adopt and continue using this growing technology especially in Country like India where more than half of the population is from rural area.

 

 

 

INTRODUCTION:

Massive Open Online Courses (MOOCs) are courses offered online with several features that enable people to take courses anywhere, anytime from across the globe, which would not be possible through conventional learning, and they are often free of charge. This has been considered as one of the disruptive technologies in education. MOOCs represent a sign of the times; they instantiate an example of how technologies can disrupt the status quo of education and are a forewarning of further changes to come (Conole. G,2013). MOOCs can be used to improve teaching learning and impart knowledge and quality education to all sectors irrespective of socio economic background and geographic location.

 

The key components in MOOC are Platform, Courses, Participants and Professors. MOOC platforms provide a place for course creators to host their content and manage their learning environment (forums, quizzes, exams, peer to peer assessment, etc.). Courses offered on MOOC range from arts, science, engineering, and management and to name a few. Participants cut across geographic boundary and there is no age barrier for those who want to study though MOOC. The survey, conducted by The Chronicle, attempted to reach every professor who has taught a MOOC.A key way professors are learning new teaching tricks is by taking cues from their MOOC students. Coursera, edX, and Udacity all track the interactions each student has with the course materials, and with one another, within a given course.

 

BACKGROUND AND SIGNIFICANCE:

In developing countries like India where there are a significant number of underserved populations, people can get quality education through MOOC.As per Financial Express dated April 11, 2106 author Ashwin Damera has written “the country faces a huge demand-and-supply mismatch when it comes to higher education. India’s gross enrolment ratio in higher education is 22%, one of the lowest in the world”. Recently, Government of India has launched ‘SWAYAM (Study Webs of Active Learning for Young Aspiring Minds)’; a MOOC for any section of people starting from school dropouts to Post Graduates provides a unique opportunity to expand the horizons of knowledge Although MOOCs are gaining popularity, there has been a great concern about the alarming increase in the dropout rate in MOOCs. As noted by Kolowich: “massive open online courses have gained renown among academics for their impressive enrolment figures and, conversely, their unimpressive completion rates”. With many institutions adopting MOOC, leveraging an existing MOOC or developing a new MOOC, institutions should go slowly with a few pioneering professors, establish objectives and measure results (NadzeyaKalbaska, 2013).

 

PROBLEM DIRECTION:

The following problem areas can be focused on:

·         Factors that influence participants to complete courses enrolled on MOOC

·         Factors that influence participants to continue using MOOC

·         To compare the adoption of MOOC by rural and urban users in India

·         Suggest strategies for MOOC adoption and implementation in India

·         Challenges faced by instructors’ for adoption of MOOC

·         Diffusion and adoption of MOOC in rural India

 

REVIEW OF LITERATURE:

Adoption of E-Learning:

The research conducted by Sung Youl Park  (2009)using TAM and structural equation modeling, showed that both e-learning self-efficacy and subjective norm play an important role in affecting attitude and behavioral intention to use e-learning.

 

MOOCs Adoption:

Jamie Murphy, Alan Williams and Amy Lennox (2013) studied the effects in the organizational diffusion of educational technologies — bandwagon and leapfrog effects using Diffusion of Innovations theory showed that Bandwagon effects stem from social pressure driving the adoption of innovations. This ‘me-too’ behaviour often results in poor innovation use. In contrast to bandwagon effects, Leapfrogging organizations take a wait-and-see attitude towards adopting new technologies, and then use the technology more efficiently than many early- adopter organizations.

The research conducted by Suzannah Evans and Jessica Gall Myrick(2015) using Diffusion of Innovations model (Rogers, 2010) showed that most MOOC professors are experienced faculty members with relatively little prior experience teaching online, and that they are divided about the purpose of MOOCs in the institutional landscape of higher education.

 

MOOCs Continuance:

A research by Khaled M. Alraimi (2014) to identify factors that enhance an individuals' intention to continue using MOOCs, a research model based on the information systems continuance expectation-confirmation model is proposed and the model explained a substantial percentage of the variance for the intention to continue using MOOCs, which is significantly influenced by perceived reputation, perceived openness, perceived usefulness, perceived, and user satisfaction. Perceived reputation and perceived openness were the strongest predictors. The research conducted by D. F. O. Onah, J. Sinclair, R. Boyatt (2014) to understand the reason behind the drop-out rates in MOOCs showed that the factors such as level of difficulty, timing and lack of digital and learning skills hindered the completion of a course and lack of support is the most influencing factor for drop outs.

 

The research conducted by Suhang Jiang, Adrianne E. Williams, Katerina Schenke (2014) to predict MOOC performance with week 1 behavior showed that assignment performance in Week 1 is a strong predictor of students’ performance at the end of the course. The degree of social integration in the learning community in Week 1 is positively correlated with the achievement of Distinction certificates. Students with external incentive are more likely to complete the course compared to students in general, even in comparison with students who have similar backgrounds. The research conducted by Hsiu-Li Liao (2012) to predict intent to continue in a lifelong e-learning course showed that perceived usefulness and compatibility have significant effects on students’ intent to continue in lifelong learning courses.

 

MOOCs in India:

The research conducted by Parag Chatterjee, Asoke Nath (2014) to study the key issues of MOOCs especially in Higher Education system showed that a major portion of students are inclined upon the formal and traditional way of education as they did not accredit MOOCs at par with the traditional way. Courses with shorter duration have more completion rate.

 

TAM and DOI model:

The research by Jyoti Devi Mahadeo (2009) explained users’ intention to adopt and continues to make use of the e-Government services by integrating two models Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) model. Users’ attitude was seen as the most important predictor for user intention followed by Social Influence and Compatibility.

RESEARCH METHODOLOGY:

A descriptive approach will be adopted for the proposed research, which will begin with preparing a questionnaire for a survey based on the combination of TAM and DOI model by surveying professors and users of MOOC.

 

Table 1: Factors that influence the adoption of MOOC

Research Variables	Definition	Models  that include the variable
Use (USE)	One specific behavior of interest performed by individuals with regard to some Information Technology(IT) system	TAM, TAM2
Behavioral Intention(BI)	An individual’s motivation or willingness to exert effort to perform the target behavior	TAM, TAM2
Attitude (ATT)	An individual’s evaluative judgment of the target behavior on some dimension (eg. harmful/beneficial, good/bad)	TAM
Perceived Ease of Use(PEOU)	An individual’s perception that using an IT system will be free of effort	TAM, TAM2
Perceived usefulness (PU)	An individual’s perception that using an IT system will enhance job performance	TAM, TAM2
Subjective Norm (SU)	An individual’s perception of the degree to which other people approve or disapprove of the target behavior	TAM2
Relative advantage(RA)	The degree to which an innovation is perceived as better than the idea it supersedes.	DOI
Compatibility (CB)	The degree to which an innovation is perceived as being consistent with the existing values, past experiences, and needs of potential adopters.	DOI
Complexity (CP)	The degree to which an innovation is perceived as difficult to understand and use.	DOI
Observability (OB)	The degree to which the results of an innovation are visible to others	DOI
Communication (COMM)	Means by which messages get from one individual to another	DOI
TIME (TI)	The mental process through which an individual (or other decision making unit) passes from first knowledge of an innovation to forming an attitude toward the innovation, to a decision to adopt or reject, to implementation of the new idea, and to confirmation of this decision.	DOI
Social System (SS)	Set of interrelated units that are engaged in joint problem-solving to accomplish a common goal	DOI

 

PRELIMINARY SUPPOSITION:

With growing use of technology across various domains, the proposed study in the field of continuous learning with the help of technology will give a valuable insight into the factors that influence MOOCs acceptance. This will guide the institutions towards providing a better and more effective planning and adoption of MOOCs.

 

REFERENCES:

1.        Conole, G. MOOCs as Disruptive Technologies: Strategies For Enhancing The Learner Experience and Quality of MOOCs: Available from URL: http://www.um.es/ead/red/39/conole.pdf

2.        Nadzeya Kalbaska. Massive Open Online Course(MOOC) Adoption and Implementation, Available from URL: : https://www.researchgate.net/profile/Nadzeya_Kalbaska/publication/279925436_Massive_Open_Online_Course_MOOC_Adoption_and_Implementation/links/559e71f108aeab53f8fd2b95.pdf?origin=publication_detail

3.        Sung Your Park. An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning, Available from URL: http://www.ifets.info/journals/12_3/14.pdf

4.        Jamie Murphy, Alan Williams and Amy Lennox. MOOCs in vocational education and training and higher education. In: 22nd National Vocational Education and Training Research conference.2013. 76-81.

5.        Suzannah Evansand Jessica Gall Myrick.
How MOOC instructors view the pedagogy and purposes of massive open online courses. Journal of Distance Education.2015; 36 (3): 295-311.

6.        Khaled M. Alraimi, HangjungZo, Andrew P. Ciganek (2014). Understanding the MOOCs continuance: The role of openness and reputation. Computers and Education.2015; 80:28-38.

7.        Onah, Daniel F. O., Sinclair, Jane and Boyatt, Russell. Dropout rates of massive open online courses: behavioural patterns. In: 6th International Conference on Education and New Learning Technologies, Barcelona, Spain. Published in: EDULEARN14 Proceedings.2014. 5825-5834.

8.        Suhang Jiang, Adrianne E. Williams, Katerina Schenke. Predicting MOOC Performance with Week 1 Behavior. Proceedings of the 7th International Conference on Educational Data Mining. 2014; 273-275.

9.        Hsiu-Li Liao and Su-Houn Liu. A comparison analysis on the intention to continued use of a lifelong learning website. International Journal of Computers and Education.2015; 80: 28-38.

10.     Parag Chatterjee, Asoke Nath. Massive Open Online Courses (MOOCs) in Education – A Case Study in Indian Context and Vision to Ubiquitous Learning. In 2nd IEEE International Conference on MOOCs, Innovation and Technology in Education(MITE).2014; 36-41

11.     Jyoti Devi Mahadeo. Towards an Understanding of the Factors Influencing the Acceptance and Diffusion of e-Government Services. Electronic Journal of e-Government. 2009; 7 (4): 391 – 402.

 

 

 

 

Accepted on 21.04.2017          © A&V Publications all right reserved