ABSTRACT
While MOOCs have taken the world by storm, questions remain about their pedagogical value and high rates of attrition. In this paper we argue that MOOCs which have open entry and open curriculum structures, place pressure on learners to not only have the requisite knowledge and skills to complete the course, but also the skills to traverse the course in adaptive ways that lead to success. The empirical study presented in the paper investigated the degree to which students' prior knowledge and skills, and their engagement with the MOOC as measured through learning analytics, predict end-of-MOOC performance. The findings indicate that prior knowledge is the most significant predictor of MOOC success followed by students' ability to revise and revisit their previous work.
- Siemens, G., and Long, P. 2011. Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 30--32.Google Scholar
- Coffrin, C., Corrin, L., de Barba, P., and Kennedy, G. 2014. Visualizing patterns of student engagement and performance in MOOCs. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge. ACM, New York, NY, 83--92. Google ScholarDigital Library
- Daniel, J. 2012. Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of Int. Media in Education, 3.Google Scholar
- Piaget, J. 1973. To understand is to invent: The future of education. Grossman, New York, NY.Google Scholar
- Bransford, J. D., Brown, A. L., Cocking, R. R. 1999. How people learn: Brain, mind, experience, and school. Nat. Academy Press.Google Scholar
- Hawkins, D. (1994). Constructivism: Some history. In P. J. Fensham, R. F. Gunstone & R. T. White (Eds), The content of science: A constructivist approach to its teaching and learning (pp. 9--13). Falmer, London, UK.Google Scholar
- Pintrich, P. R., Smith, D., García, T., and McKeachie, W. 1991. A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor, Michigan, USA.Google Scholar
- Weinstein, C. E. and Mayer, R. E. 1986. The teaching of learning strategies. In M. C. Wittrock (Ed.) Handbook of research on teaching (3rd Ed.) Macmillan, New York, NY, 315--327.Google Scholar
- Zimmerman, B. J., and Schunk, D. H. (Eds.). 2011. Handbook of self-regulation of learning and performance. Taylor & Francis.Google Scholar
- Siemens, G. 2005. Connectivism: A learning theory for the digital age. Int. journal of inst. technology & distance learning 2.1, 3--10.Google Scholar
- Van Hentenryck, P., Coffrin, C. 2014. Teaching Creative Problem Solving in a MOOC.In Proceedings of The 45th ACM Technical Symposium on Computer Science Education. ACM, New York, NY, 677--682. Google ScholarDigital Library
Index Terms
- Predicting success: how learners' prior knowledge, skills and activities predict MOOC performance
Recommendations
Visualizing patterns of student engagement and performance in MOOCs
LAK '14: Proceedings of the Fourth International Conference on Learning Analytics And KnowledgeIn the last five years, the world has seen a remarkable level of interest in Massive Open Online Courses, or MOOCs. A consistent message from universities participating in MOOC delivery is their eagerness to understand students' online learning ...
A Literature Review on MOOCs Integrated With Learning Analytics
Although there have been numerous studies committed to MOOCs integrated with learning analytics, fewer of them have systematically reviewed the related literature. Using clustering techniques, bibliographic network visualization, content analysis, and ...
Using learning analytics to explore help-seeking learner profiles in MOOCs
LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge ConferenceIn online learning environments, learners are often required to be more autonomous in their approach to learning. In scaled online learning environments, like Massive Open Online Courses (MOOCs), there are differences in the ability of learners to ...
Comments