"The advent of Massive Online Open Courses (MOOCs) has led to the availability of large educational datasets collected from diverse international audiences. However, there is a need to research the impact of cultural and geographic factors on student performance in MOOCs, and there is a need to connect this research to existing cultural theoretical frameworks. In this paper we analyze national and cultural differences in students’ performance in a large-scale MOOC in the context of existing theoretical frameworks for cultural analysis. We focus on three dimensions of learner behavior: course activity profiles; quiz activity profiles; and forum best friends. We conclude that countries or associated cultural clusters are associated with differences in all three dimensions. These findings stress the need for more research on the internationalization in online education and greater intercultural awareness among MOOC designers"
Liu, Zhongxiu, Rebecca Brown, Collin Lynch, Tiffany Barnes, Ryan Shaun Joazeiro de Baker, Yoav Bergner and Danielle S. McNamara. “MOOC Learner Behaviors by Country and Culture; an Exploratory Analysis.” EDM (2016).
1. SUMMARY
The paper analyzes the national and cultural differences in students’ performance while taking MOOC, with a focus on course activity profiles, quiz activity profiles and most connected peers. It was found that culture differences indeed have an impact on those three dimensions. For examples, “students from countries with higher individualism and lower power distance are twice as likely to be all-rounders”.
2. STRENGTHS
Good scope - 29,149 students from 172 countries in 5 continents
Good background with decent amount of references
Nice comparison between Hofstede dimensions and CDLF dimensions
Answers to the five research questions have decent weights
3. WEAKNESSES
- Only 638 students finished the course (750 posted on the forum) and the paper did not elaborate why there is a 98% difference between the active and the inactive users and how cultural differences may explain that phenomenon.
- The paper did not consider situations like course retakes which may explain patterns in figure 4. For example, by-standers in this cohort may become more active in the next cohort.
- More “down-to-earth” cultural metrics that have closer ties to education should be considered. For one example, there are some countries that are much exposed to English than others. For example, English has far deeper influence on India than on China and that may have some influence on users’ behaviors in this English-based courseware. It is also not clear if the paper considered the cultural dimensions as being independent or being inter-linked. Let’s go back to the English exposure example. Because of the limited exposure to English, only a special group of people in China was able to recognize the importance of English, had enough resource to pursue the language education which ultimately led them to online courses that teach knowledge other than English like this one. In the case of China, such group of people may already carry special unique cultural characters that not only differentiate themselves from other countries but also from the rest of China.
Image credit: Quino Marin
