The development of Massive Open Online Courses (MOOCs) has stimulated educational research and challenged the existing models of distance and online learning. New questions have emerged that could be answered and administered across thousands of students. Although data on the engagement of this large number of online learners is quite accessible for researchers, they are usually limited to few sources. Very few researchers have data from different courses, institutions and/or platforms. Student privacy and data protection regulations tend to limit data sharing and suppress collaborative research that is necessary for addressing some of the main challenges MOOC research is currently facing. The MOOC Data Science Commons is a collaborative platform that tends to bring the next generation of MOOC research, allowing for generalized MOOC data organization and shared analytics. The aim of the project is to bring together educational researchers, computer science researchers, machine learning researchers, technologists, database and big data experts to advance MOOC data science.

1. MOOCdb:
Developing Data Standards for MOOCs
Srećko Joksimović
s.joksimovic@ed.ac.uk
@s_joksimovic
Kalyan Veeramachaneni
kalyan@csail.mit.edu
Dragan Gašević
dragan.gasevic@ed.ac.uk
FutureLearn Academic Network Conference
15 June 2015

2. Data sources
SQL_anonymized_forum.sql
SQL_anonymized_general.sql
SQL_hash_mapping.sql
standard extract.csvSQL_unanonymizable.sql.gz
clickstream_export.gz
Personal data
Demographic data
…

3. Data sources
Weekly data packages
auth_user-{site}-analytics.sql
auth_userprofile-{site}-analytics.sql
certificates_generatedcertificate-{site}-analytics.sql
Daily data packages
course_structure-{site}-analytics.json
courseware_studentmodule-{site}-analytics.sql
email_opt_in-{site}-analytics.csv
student_courseenrollment-{site}-analytics.sql
user_api_usercoursetag-{site}-analytics.sql
user_id_map-{site}-analytics.sql
{org}-{course}-{date}-{site}.mongo
wiki_article-{site}-analytics.sql
wiki_articlerevision-{site}-analytics.sql
{org}-{site}-events-{date}.log.gz.gpg

4. Challenges
• Analytics across several courses
• Analytics across different platforms
• Analytics across different institutions
• Sharing data

5. Solution?
• Collaborative data science platform
– Standardize data storage
– Generalizable across courses and data providers
(currently OpenEdX, edX and Coursera)
– “Data being shared without data being
exchanged”
– Sharing and reproducing the results

6. MOOC data science commons

7. MOOCdb
Observing mode
- Observed Events table
- Resources table
- Resources Types table
- URLs table
- Resource URLs table
Submitting mode
- Problem Types table
- Problems table
- Submissions table
- Assessments table
Collaborating mode
- Collaborations table
- Collaboration Types table
Feedback mode
- Feedbacks table
- Questions table
- Answers table
- Surveys table
User information
- User PII table
- Global User table
- Course User table
http://moocdb.csail.mit.edu/wiki/index.php?title=MOOCdb

8. Collaborative platform and
applications
edX Coursera MOOCdb doc Github repo
Feature factory LabelMe Digital learner quantified Problem analytics
My MOOCViz Social network analysis Forum analysis Dropout prediction

9. Current state
• Established network of institutions
– MIT, Stanford, University of Michigan, University
of Edinburgh, University of Queensland, University
of Texas (Austin)
• Release of open source software
• Development and release of the first data
analytics framework

10. Next steps
Digital Learner Quantified
Discussion forum analysis
LabelMe
Problem analytics
Dropout prediction
Social network analysis

11. Collaboration
• If you are interested in…
– Development
– Feature modeling
– Translating your data
– Testing
kalyan@csail.mit.edu
s.Joksimovic@ed.ac.uk

12. Q&A
MOOCdb:
Developing Data Standards for MOOCs
Srećko Joksimović
s.joksimovic@ed.ac.uk
@s_joksimovic
Kalyan Veeramachaneni
kalyan@csail.mit.edu
Dragan Gašević
dragan.gasevic@ed.ac.uk
FutureLearn Academic Network Conference
15 June 2015