1. The Application of Big Data and Learning Analytics in Universities Management: Practices from the Open University of China
魏顺平 博士
Shunping Wei
May. 22nd, 2016

2. Outline 1.Big Data 2.Learning Analytics & Educational Data Mining
3.Application Cases

3. 1.Big Data

4. The total amount of global data was 25ZB(2.5 trillion GB) in 2016.
Big Data Era is Coming
25PB data is generated every day
equal to the information amount of 1500 national libraries
The total amount of global data was 25ZB(2.5 trillion GB) in 2016.
As we know, the big era is coming. 25 PB data is generated everyday in the world, which equals the information amount of 1500 national libraries.
The total amount of global data was 12ZB (Zettabyte) in 2016.?（同样是2016年的全球数据，对应幻灯片标题中的数字是25ZB？）
Suppose the storage capacity of your hard disk is 1 TB(Trillionbyte), which can store about 1000 high definition movies. then you have to use 1.2 billion hard disks to storage the data generated in 2016.
In education field, mass data is generated as digital education is developed swiftly.
1ZB=1024EB=1024*1024PB=109TB，100万块1TB硬盘
2020年全球数据将达到40ZB

5. OUC: Internet-based University

6. Six-Network Integration
A learner development model centered on students and learning is established based on the "Six-Network Integration".

7. Big Data is Emerging Storing users’ activities data
An onion model of educational big data
Storing users’ activities data
Activities Data
Resource Data
Status Data
Basic Data
Storing educational resources
Storing operation status data of educational equipment and business
Educational big data is emerging.
In my opinion, educational big data is total amount, high volume, high velocity, and high variety data about educational factors and educational process.
Here is an onion model of educational big data, which includes four layers.
The inside layer is basic data, which stores national educational basic data such as students and teachers’ basic information.
The second layer is status data which stores operation status data of educational equipment and business.
The third layer is resource data which stores educational resources.
The outer layer is activities data which stores users’ activities data.
Storing national educational basic data

8. Big Data of OUC Basic data
13,000,000 records of learners’ basic information
8,900,000 records of graduates’ information
180,000,000 records of learners’ course selection
Resource data
300+ Web courses
29,000 Micro-courses
510,000 minutes’ Video courses
Digital library, more than 3,600,000 e-books, 11, 000 digital journals and magazines
Behavior data
More than 1 billion accumulative log data in recent 8 years

9. The Value of Big Data 1 2 3 4 5 6 For learning Personalized learning
For instruction
Targeted teaching
For software
Continuous optimization
For research
Learning rules discovering
For management
Fine management
For desicion
Policy making
从学习视角看，是如何利用大数据促进学生学业成功；
从教学视角看，是如何利用大数据提升教师教学效能；
从技术开发视角看，是如何利用大数据实现教育教学软件持续优化。
从研究视角看，是如何利用大数据发现教育和学习规律。
从管理视角看，是如何利用大数据优化配置教育教学资源。
从政策视角看， 是如何利用大数据聚焦于政策对象的微观层面，并获得迅速甚至达到实时与即时性的政策反馈，预测未来。

10. Database Administrators
Big Data Centre in OUC
BDC
Data Scientists
Data Analysts
Database Administrators
Software Engineers
Director

11. 2.Learning Analytics & Educational Data Mining

12. A Model of Learning Behavior
action
who
what
where
When
results
If we describe learning activity, we could say a student assign a homework of one chapter in some time and gets ? .
There exists five components in such a description as this figure shows.
The action in the center, and who, do what, where, when, and get what results.
This is a data model used to represent learning activity and learning activity is analyzed according to such a model.

13. Data Model for LA

14. Key Methods
Many methods from data mining such as statistical analysis and visualization, relational mining, association rules mining, clustering are employed in this reaearch.

15. Typical Tools

16. Integrated LAS

17. Typical Examples

18. 3.Application Cases

19. Data-driven Evaluation of Online Instruction and Learning Process
Requirement description
The headquarters of OUC evaluates online instruction and learning process of 44 branches each year.
In the past, the headquarters employed many experts to evaluate the process by exploring some selected web courses of each branch artificially. The evaluation job would last 4 to 5 months.
Application of big data and LA
A data-driven Evaluation Index system for online tutoring and learning process of educational institution was constructed by using synthesis method of deduction and induction.
Then the evaluation index system was used in evaluating the tutoring and learning process of 44 branches of the Open University of China based on about 110 million action logs. The practice showed that this evaluation index system could be used to present a high-level overview of online tutoring and learning of an educational institution, to present the differences among different institutions, and to diagnose the existing problems.

20. Width-depth-persistence Model for Learning Process Evaluation

21. Evaluation Dimension: Width

22. Evaluation dimension: depth

23. Evaluation Dimension: Persistence

24. Formula For Calculating Comprehensive Score
Ybranch=ωTB(widthTB∧DepthTB∧PersistenceTB )+ωLB(widthLB∧DepthLB∧PersistenceLB )+ωI TL(widthITL∧DepthITL∧TimelinessITL)
TB, Tutors’ Behavior
LB, Learners’ Behavior
ITL, Interaction between Tutors and Learners

25. Score in centesimal system
Results
a high-level overview of online tutoring and learning of an educational institution
branches
Original score
Score in centesimal system BA
0.54 91 CD
0.43 86 JG
0.42 GX
0.41 85 LG
0.38 83 GU
0.34 79 GO
0.33 77 HE
0.32 76 JI TI
0.31 74 HU
0.29 70 SA
0.28 69

26. Results
Present the differences among different institutions and diagnose the existing problems

27. Conclusions
It’s helpful to use big data and LA appropriately in optimizing educational management, promoting the quality of education and instruction , and improving educational software design and development.
It’s necessary and possible for open universities to use big data and LA in the teaching and learning process timely.
It’s really valuable for gathering and storing data generated during the process of instruction, management and research.

28. One World, Multiple Models Learn from each other, develop together

29. Thanks