The primary focus of this research project was to understand the needs of English Language Learners (ELLs) in Massive Open Online Courses (MOOCs). This project included a series of quantitative and mixed-methods research studies that uncover important phenomena that inform how we cater to ELLs that participate in English MOOCs.
ELL Student Motivation:
Through insights learned from interviews and analysis of a large scale online survey, we uncovered that ELL students participate in MOOCs not just to gain knowledge. In several cases, MOOC certificates serve as their only proof of knowledge to employers although they might already have the knowledge. For some ELLs, English language MOOCs, although harder than taking it in their local language, are more attractive to them as it exposes them to learning English, and people who live in other countries. This potentially gives them opportunity for upward economic, social, and geographic mobility both in their home countries and abroad.
Uchidiuno, J., Ogan, A., Yarzebinski, E., & Hammer, J. (2016, April). Understanding ESL Students’ Motivations to Increase MOOC Accessibility. In Proceedings of the third (2016) ACM conference on Learning@ Scale (pp. 169-172). ACM.
Uchidiuno, J. O., Ogan, A., Yarzebinski, E., & Hammer, J. (2017). Going Global: Understanding English Language Learners’ Student Motivation in English-Language MOOCs. International Journal of Artificial Intelligence in Education, 1-25.
ELL Student Identification:
IP address mapping (to countries official languages) are a popular way of identifying students who may need language interventions in MOOCs. However, this metric ignores the status of English as a popular second language in the world. It also potentially miscategorizes people who live outside of their home countries. Our analysis of clickstream data in MOOCs show that using students browser language for identification is more consistent with ELL expected behavior, compared to IP addresses.
Uchidiuno, J., Ogan, A., Koedinger, K. R., Yarzebinski, E., & Hammer, J. (2016, April). Browser language preferences as a metric for identifying ESL speakers in MOOCs. In Proceedings of the third (2016) ACM conference on Learning@ Scale (pp. 277-280). ACM.
Characterizing ELL Student Behavior in MOOCs:
In order to target language interventions for the students who need them most, we need a way to characterize ELL student behavior in MOOCs. We analyzed data from two different Coursera MOOCs – Introduction to Psychology and Statistical Thermodynamics.
We coded all the videos in both courses to extract the different content types. The table below shows the description of each content type, and how each course was broken down by the different content types.
We found that ELLs are statistically more likely to pause on most types of content, and slow down on text content. We also found that they are more likely to seek away from talking only sections towards other types of content. For courses, e.g the Thermodynamics course, where almost half of the class is talking only, ELLs may be missing a significant amount of the content due to the way it is presented.
Uchidiuno, J., Koedinger, K., Hammer, J., Yarzebinski, E., & Ogan, A. (2017). How Do English Language Learners Interact with Different Content Types in MOOC Videos?. International Journal of Artificial Intelligence in Education, 1-20.
Uchidiuno, J., Hammer, J., Yarzebinski, E., Koedinger, K. R., & Ogan, A. (2017, April). Characterizing ELL Students’ Behavior During MOOC Videos Using Content Type. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale (pp. 185-188). ACM.