Massive Open Online Courses (MOOCs) have opened new educational possibilities for learners around the world. Numerous providers have emerged, which usually have different targets (geographical, topics or language), but most of the research and spotlight has been concentrated on the global providers and studies with limited generalizability. In this work we apply a multi-platform approach generating a joint and comparable analysis with data from millions of learners and more than ten MOOC providers that have partnered to conduct this study. This allows us to generate learning analytics trends at a macro level across various MOOC providers towards understanding which MOOC trends are globally universal and which of them are context-dependent. The analysis reports preliminary results on the differences and similarities of trends based on the country of origin, level of education, gender and age of their learners across global and regional MOOC providers. This study exemplifies the potential of macro learning analytics in MOOCs to understand the ecosystem and inform the whole community, while calling for more large scale studies in learning analytics through partnerships among researchers and institutions.

1. LOGO
Macro MOOC learning analytics:
Exploring trends across global and
regional providers
José A. Ruipérez-Valiente, Matt Jenner, Thomas
Staubitz, Xitong Li, Tobias Rohloff, Sherif Halawa,
Carlos Turro, Yuan Cheng, Jiayin Zhang,
Ignacio Despujol, and Justin Reich
@JoseARuiperez — http://joseruiperez.me/

2. Thanks to these institutions that have
contributed to this research!
@JoseARuiperez — http://joseruiperez.me/Macro MOOC learning analytics: Exploring trends across global and regional providers 2

3. MOOC Research
 Plenty of data to study the worldwide classroom
 This generated tons of research, but…
 Frequently focused on learner modelling with algorithmic approaches
 Most of it were observational studies, lack of interventions
 The anonymity of online environments decreases confidence
 So many course and human factors, it’s hard to generalize
 This work focuses on universal vs. context-dependent trends:
 Focus on single courses or numerous courses from the same provider
 Literature reviews cannot bypass methodological differences for comparison
 The spotlight has been on the main MOOC providers (e.g., edX, FutureLearn or
Coursera)
Macro MOOC learning analytics: Exploring trends across global and regional providers 3 @JoseARuiperez — http://joseruiperez.me/

4. Macro MOOC Learning Analytics
 Three levels where learning analytics can have an impact, the
macro, meso and micro (Shum, 2012)
 MOLAC (Drachsler and Kalz, 2016) mapped those levels to MOOCs
 Macro level helps with analytics that inform the whole community
 MOOCs were to educate the world at a macro level
 Most research suggest that learners in need struggle in these global providers
 Previous research compared Arab learners in MITx/HarvardX and Edraak
(Ruipérez-Valiente et al., 2020)
 Concluded that regional MOOC providers might play a different role
 What differences can we find between global and regional MOOC
providers?
Macro MOOC learning analytics: Exploring trends across global and regional providers 4 @JoseARuiperez — http://joseruiperez.me/

5. Methodology
Replication approach that allows ’apples to apples’
Common variables:
 Demographics: Nationality, level of education, gender, age
 Participation: Viewed, explored, completed
 Human development index (HDI) from the United Nations
Procedure:
1. We launched a Call for Partners
2. Researchers shape their data into the same format
3. Collaboratively generate common script for analysis and execute it
4. We share only high level aggregate data and generate analysis
Macro MOOC learning analytics: Exploring trends across global and regional providers 5 @JoseARuiperez — http://joseruiperez.me/

6. Iterative process
Macro MOOC learning analytics: Exploring trends across global and regional providers 6 @JoseARuiperez — http://joseruiperez.me/

7. Data Collection
Macro MOOC learning analytics: Exploring trends across global and regional providers 7 @JoseARuiperez — http://joseruiperez.me/
Provider Scope # MOOC instances # Learners
HarvardX and MITx
Global providers
(targeting a global
audience)
565 MOOCs 3.7 million
FutureLearn 1548 MOOCs 1.1 million
openHPI 43 MOOCs 113 thousand
openSAP 166 MOOCs 515 thousand
OpenWHO 52 MOOCs 35 thousand
HEC Paris (French) on Coursera Global providers
(targeting a more
regional audience)
33 MOOCs 22 thousand
UPValenciaX (Spanish) on edX 230 MOOCs 700 thousand
Edraak (Arabic platform)
Regional or local
platforms (targeting a
specific populations)
231 MOOCs 3.77 million
UPVx (Spanish and Valencian platform) 123 MOOC 40 thousand
XuetangX (Chinese platform) 2884 MOOCs 655 thousand
mooc.house (German platform) 18 MOOCs 24 thousand
Chinese MOOC (Chinese platform) 2 MOOCs 7 thousand

8. Country representation by provider
Macro MOOC learning analytics: Exploring trends across global and regional providers 8 @JoseARuiperez — http://joseruiperez.me/
Certain similarities across global
MOOC providers except OpenWHO
HEC Paris (Coursera) and
UPValenciaX (edX) are special
cases focusing on regional
populations
Important regional focus of the
rest of regional MOOC providers
More international Hispanic
audience in UPValenciaX than
UPVx

9. Level of education by provider
Macro MOOC learning analytics: Exploring trends across global and regional providers 9 @JoseARuiperez — http://joseruiperez.me/
Constant trend of more educated
learners N. America and Europe
Differences in which platforms attract
learners with low education
OpenSAP shows a high focus on
professionals
Regional providers Edraak and
XuetangX attracts the least educated
learners (86% and 78%)

10. Gender distribution by provider
Macro MOOC learning analytics: Exploring trends across global and regional providers 10 @JoseARuiperez — http://joseruiperez.me/
Consistent pattern of wider gender
gap in less developed regions
Special cases in openSAP and
openHPI (low female
representation) and OpenWHO
(more diverse)

11. Age distribution by provider
11Macro MOOC learning analytics: Exploring trends across global and regional providers @JoseARuiperez — http://joseruiperez.me/
Most common buckets are [26,35) except for openHPI [45, 55) and Edraak [18, 25]
Regional MOOC providers (Edraak and XuetangX) have the youngest populations
Some of these variations might be linked to digital literacy and English knowledge

12. Conclusions and Future Work
 We find clear differences between global and regional providers
 Might be more successful to fulfill the original goal
 Generate guidelines that can help at institutional and individual learner levels
 Challenges for partners: Human resources, interest or tech
challenge
 More research is needed in regional settings
 In the future, we are expanding with some additional providers
 We are open to welcoming new members!
 Next focus: behavioural characteristics and other dimensions
 Comparing specific populations in global and regional settings
 Launching a common survey on learners’ perceptions
12Macro MOOC learning analytics: Exploring trends across global and regional providers @JoseARuiperez — http://joseruiperez.me/

13. References
1. SB Buckingham Shum. 2012. UNESCO Policy Brief: Learning Analytics (No.
November).UNESCO Institute for Information Technologies in Education.
2. Hendrik Drachsler and Marco Kalz. 2016. The MOOC and learning analyticsinnovation cycle
(MOLAC): a reflective summary of ongoing research and itschallenges.Journal of Computer
Assisted Learning32, 3 (2016), 281–290.
3. Ruipérez-Valiente, J. A., Halawa, S., Slama, R., & Reich, J. (2020). Using multi-platform
learning analytics to compare regional and global MOOC learning in the Arab world. Computers
& Education, 146, 103776.
13Macro MOOC learning analytics: Exploring trends across global and regional providers @JoseARuiperez — http://joseruiperez.me/

14. LOGO
Macro MOOC learning analytics: Exploring trends across global and regional providers
José A. Ruipérez-Valiente, Matt Jenner, Thomas
Staubitz, Xitong Li, Tobias Rohloff, Sherif Halawa,
Carlos Turro, Yuan Cheng, Jiayin Zhang,
Ignacio Despujol, and Justin Reich
@JoseARuiperez — http://joseruiperez.me/