Investigating Commonalities in Higher Education Massive Open Online Courses (MOOCs) using Social Net Members : #OER17

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Public Group Active 7 years, 7 months ago

Wed, Apr 5 2017, 11:20am – 12:40pm

Authors: Saman Zehra Rizvi, and Quratulain Rajput

Room: Seminar 4

Theme: Inst/Org Politics

Type: Presentations

Description

Massive Open Online Course (MOOC) being comparatively new occurrence in the realm of computer mediated or collaborative learning. There are few papers available to appreciate MOOCs prospects among other digital learning methods using network analysis techniques (Oshima et al., 2012; Takaffoli et al., 2012; Stuetzer et al., 2011; Aviv et al., 2003). While scarcity of comprehensive research in this area makes it difficult to guesstimate the future prospects of MOOCs, a significant attempt was made in this regard when HarvardX Research Committee and The Office of Digital Learning at MIT published a report and made a data set public, that contained results of first two years of HarvardX and MITx courses (Ho et al., 2014). In MOOCs, free online registration and open access to data made it easy to enroll and pursue multiple courses offered by different providers at any time. Many students got not only enrolled but also explored most of the courseware. Some of the mature learners thrived for and hence received certificates of completion. A certain number of learners, after completion of one specific course, opted for another relevant course. In this study we investigated one-mode projection of student-course bipartite network of interest for courses that share some students. This sharing made courses a common interest among learners community. A total of 16 MOOCs were used in this study. We first explored the interest network of all courses as a whole from both HarvardX and MITx. Afterwards we analyzed courses from HarvardX and MITx individually. This study provides information to above mentioned, or other MOOCs provider that in which courses lies learners’ common interest? This easy to interpret process of finding commonalities in offered MOOCs will help MOOCs providers to better know the learners’ interest level and will also help to understand overall behavior of learners’ subpopulation interested in more than one courses at a time.

References:

Aviv, R., Erlich, Z., Ravid, G., Geva, A., 2003. Network analysis of knowledge construction in asynchronous learning networks. Journal of Asynchronous Learning Networks 7, 1–23.

Ho, A.D., Reich, J., Nesterko, S.O., Seaton, D.T., Mullaney, T., Waldo, J., Chuang, I., 2014. HarvardX and MITx: The first year of open online courses, fall 2012-summer 2013. Ho, AD, Reich, J., Nesterko, S., Seaton, DT, Mullaney, T., Waldo, J., & Chuang, I.(2014). HarvardX and MITx: The first year of open online courses (HarvardX and MITx Working Paper No. 1).

Oshima, J., Oshima, R., Matsuzawa, Y., 2012. Knowledge Building Discourse Explorer: a social network analysis application for knowledge building discourse. Educational technology research and development 60, 903–921.

Stuetzer, C.M., Carley, K.M., Koehler, T., Thiem, G., 2011. The communication infrastructure during the learning process in web based collaborative learning systems, in: Proceedings of the 3rd International Web Science Conference. ACM, p. 17.

Takaffoli, M., Zaïane, O.R., others, 2012. Social network analysis and mining to support the assessment of on-line student participation. ACM SIGKDD Explorations Newsletter 13, 20–29.

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