How Do English Language Learners Interact with Different Content Types in MOOC Videos?

English Language Learners (ELLs) are a substantial portion of the students who enroll in MOOCs. In order to fulfill the promise of MOOCs – i.e., making higher education accessible to everyone with an internet connection – appropriate interventions should be offered to students who struggle with the...

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Bibliographic Details
Published in:International journal of artificial intelligence in education 2018-09, Vol.28 (4), p.508-527
Main Authors: Uchidiuno, Judith, Koedinger, Ken, Hammer, Jessica, Yarzebinski, Evelyn, Ogan, Amy
Format: Article
Language:English
Subjects:
Artificial Intelligence
Behavior
Behavior Patterns
College Students
Computer assisted language learning
Computer Science
Computers and Education
Course Content
Dialects
Educational Quality
Educational Strategies
Educational Technology
English as a second language
English as a second language learning
English language
English Language Learners
Higher Education
Interaction
Internet
Language Skills
Language Usage
Large Group Instruction
Listening
Native Speakers
Online Courses
Online instruction
Participation
Psychology
Second language learning
Self evaluation
Speaking
Statistical thermodynamics
Student Behavior
Students
Technology Uses in Education
User Interfaces and Human Computer Interaction
Video
Video Technology
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Summary:English Language Learners (ELLs) are a substantial portion of the students who enroll in MOOCs. In order to fulfill the promise of MOOCs – i.e., making higher education accessible to everyone with an internet connection – appropriate interventions should be offered to students who struggle with the language of course content. Through the analysis of clickstream log data gathered from two MOOC courses deployed on Coursera, Introduction to Psychology and Statistical Thermodynamics, we show that compared to native English speakers, ELL students have distinct behavioral patterns in how they engage with MOOC content including increased interaction with content that contains text, increased seeking away from content without visual support, and decreased video play rates. These patterns are expressed differently in response to different types of course content and domains. Our findings not only suggest more fine-grained methods for automatically identifying students who need language interventions, but also have further implications for the design of language support interventions and MOOC videos.
ISSN:1560-4292
1560-4306
DOI:10.1007/s40593-017-0156-x