Collaborative MOOCs for Teacher Professional Development

Hsiaolin Hsieh, Director of Technology, Data & SystemsUnderstanding Language Online

Stanford University

GMU, Nov 9, 2014

http://ell.stanford.edu/

Our Inaugural MOOC

Vectors for data ingress

• User profile

• Uploading student language samples

• Team collaboration/social networking

User/educator profile

• NovoEd profile – Age– Gender– Highest level of

education– Location– Primary language– Education (schools)– Occupation– Bio

• Course profile – Primary role as an educator– Grade level focus– Content area focus– Years of experience– School district– Expected level of

engagement with teamwork

– Commitment to the course

Student language samples

• Samples– Conversation (transcript and/or audio recording)– Writing– Reading (notes or audio recording)

• Contextual information– Grade level – English proficiency– Primary language(s)*All data is anonymous

Team dynamics

MOOCs and beyond

Video library

Student language database

Student language samples

teachersresearchers

Privacy issues

• Implicit data collection– What: Tracking participants (educators) progress

through the course, generating data on their interactions with the platform and with one another

– How: terms of use of the platform, IRB approval– Why: research and course improvement– Issues: MOOC-related big data collection

Privacy issues

• Explicit data collection– What: Student language samples and

contextualizing information– How: student oral consent and parent letter, and

teacher agreement– Why: database – Issues: Ownership/licensing of data, obtain

permission (strategies for minimizing opt-out rate)

Appendix IResearch information

Appendix IIDear parent letter

Appendix IIIstudent oral consent

Appendix IVparticipation agreement (for database use)