1. Collaborative MOOCs for Teacher Professional Development Hsiaolin Hsieh, Director of Technology, Data & Systems Understanding Language Online Stanford University GMU, Nov 9, 2014

2. http://ell.stanford.edu/

3. Our Inaugural MOOC

4. Vectors for data ingress User profile Uploading student language samples Team collaboration/social networking

5. 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

6. 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

7. Team dynamics

8. MOOCs and beyond Video library Student language database Student language samples teachers researchers

9. 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

10. 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)

11. Appendix I Research information

12. Appendix II Dear parent letter

13. Appendix III student oral consent

14. Appendix IV participation agreement (for database use)