Design Model for MOOCs in China

  • Qinhua ZhengEmail author
  • Li Chen
  • Daniel Burgos
Part of the Lecture Notes in Educational Technology book series (LNET)


Fanatical behind MOOCs, we must ponder what essence is it? Ultimately, MOOCs origin is online learning, which is a form of education. Since it is education, course design is a systematic approach to its success. The study began this question: Whether course design of Chinese MOOCs effectively promote learning? To solve this problem, data analysis is running by the domestic representative 622 courses which contain all information from the following five aspects: learning path, learning objectives, learning assessment, learning activities, and learning. The study found that the use of Chinese MOOCs whole “watch micro-courses + online discussions + test” learning paths, less emphasis on the design of learning objectives, learning evaluation mostly in the form of a single “test” to “online discussion” as the key learning activities and “micro-course” as the main learning resources. To more effectively promote learning, increase completion rates, research suggests multiple strategies designed learning paths, SMART learning objectives, diversified evaluation methods, scaffold-like learning activities, and effective micro-courses.


  1. Aguaded-Gomez, J. Ignacio. (2013). The MOOC revolution: A new form of education from the technological paradigm. Comunicar, 41, 7–8.Google Scholar
  2. Cross, S. (2013). Evaluation of the olds MOOC curriculum design course: Participant perspectives, expectations and experiences.Google Scholar
  3. Daradoumis,T., Bassi, R., Xhafa, F., & Caballé, S. (2013) A review on massive e-learning (MOOC) design, delivery and assessment. Google Scholar
  4. Deng, K., & Deng, C. (2013). Development and design of distance open courses: Experience of open university from Great Britain—Plan and design of distance open courses [J]. Journal of Beijing Open University, 4, 15–19.Google Scholar
  5. Dirksen, J. (2012). Cognitive design: Art to improve learning experience (Jianjia, Trans.). (pp. 7–135) Beijing: China Machine Press.Google Scholar
  6. Huang, J., Dasgupta, A., Ghosh, A., Manning, J., & Sanders, M. (2014). Superposter behavior in MOOC forums.Google Scholar
  7. Jiang, L., & Zhang, H. (2014). MOOC knowledge mapping of MOOC research hotspots and trends [J]. Distance Education in China, 23, 35–40.Google Scholar
  8. Liu, Q., Ye, Y., & Zhu, K. (2014). Research on learning activity design for MOOC from the perspective of activity theory [J]. Journal of Distance Education, 4, 99–105.Google Scholar
  9. Mazoue, J. G. (2013). The MOOC model: Challenging traditional education.Google Scholar
  10. McAndrew, P. (2013). Learning from open design: Running a learning design MOOC. eLearning Papers(33).Google Scholar
  11. Miles, & Huberman. (2008). Qualitative data analysis [M]. (F. Zhang, Trans.). Chongqing: Chongqing University Press.Google Scholar
  12. Reich, J. (2015). Rebooting MOOC research. Science, 347(6217), 34–35.CrossRefGoogle Scholar
  13. Stacey, P. (2014). Pedagogy of MOOCs for innovation and quality in learning, 111.Google Scholar
  14. Sun, L., & Zhong, S. (2014). Probabilistic models of peer assessment in MOOC system [J]. Open Education Research, 5, 83–90.Google Scholar
  15. Wang, Y. (2010). Methods of compiling textbooks in distance education—Based on practical experiences from open university in Great Britain [M]. Beijing: Higher Education Press.Google Scholar
  16. Wilkowski, J., Deutsch, A., & Russell, D. M. (2014). Student skill and goal achievement in the mapping with Google MOOC. In Proceedings of the first ACM conference on Learning @ scale conference (pp. 3–10). ACM.Google Scholar
  17. Yang, Y., & Jiao, J. (2014). Eco-design framework for personalized learning of MOOC learners [J]. E-education Research, 8, 7.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Beijing Normal UniversityBeijingChina
  2. 2.Universidad Internacional de La Rioja (UNIR)LogroñoSpain

Personalised recommendations