Publication Type:

Conference Paper

Source:

Proceedings of the 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education, MITE 2015, Institute of Electrical and Electronics Engineers Inc., p.182-187 (2015)

ISBN:

9781467367462

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963861103&partnerID=40&md5=a877ca3f56356bcf3f1876ff47081dd7

Keywords:

Computer aided instruction, E-learning, Education, Educational technology, Employment, Engineering education, Engineering research, Internet, learning on MOOC, Learning Style, Logistic regressions, MOOC users, Online learning, personality, Population statistics, Regression analysis

Abstract:

Massive Open Online Courses have been looked at both as disruptive innovation as well as the biggest experiment in education in recent times. 15 Million learners have turned to MOOCs as in 2014 and Indians constitute the second largest share of the MOOC user base. This paper attempts to understand some of the essential characteristics that distinguish a MOOC user from a non-user in India. A proprietary survey for understanding MOOC consumers has been used to draw insights on demographics of potential MOOC users including age, occupation, gender, educational backgrounds as well as some salient aspects of their personality, learning styles and life goals. Three logistic regression models have been tested. The first model investigates the impact of background demographic variables and internet skills of respondents on the choice to enroll in at least one MOOC course. The second model includes key personality traits that are hypothesized to influence the user adoption of MOOCs and the full modeladdsvariables pertaining to learning styles, learning environment and life goals. Those with better internet skills and an existing preference for learning through videos were seen to be significantly more likely to adopt MOOCs. Personality traits aligned with an openness to try new things were seen to influence the adoption decision but the learning styles and learning environment did not differentiate users from non-users. The results also indicate a significant influence of gender and age. © 2015 IEEE.

Notes:

cited By 0; Conference of 3rd IEEE International Conference on MOOCs, Innovation and Technology in Education, MITE 2015 ; Conference Date: 1 October 2015 Through 2 October 2015; Conference Code:118997

Cite this Research Publication

A. Kaveri, Dr. Sangeetha G, Dr. Deepak Gupta, and Pratap, M., “Decoding the Indian MOOC learner”, in Proceedings of the 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education, MITE 2015, 2015, pp. 182-187.

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