IV2019- France https://ieeexplore.ieee.org/document/8811924 https://sites.google.com/site/cilabuniba/people/gabriella-casalino

1. Incremental and adaptive
fuzzy clustering for
Virtual Learning Environments
data analysis
Gabriella Casalino
University of Bari, Italy
IV2019-France
23rd International Conference on Information Visualization,
July 2-5 2019, Paris
Giovanna Castellano
University of Bari, Italy
Corrado Mencar
University of Bari, Italy

2. Virtual Learning
Environments
IV2019-France, July 2-5 2019, Paris

3. Educational Data Mining
IV2019-France, July 2-5 2019, Paris

4. Educational Data Mining
IV2019-France, July 2-5 2019, Paris

5. Educational Data Mining
IV2019-France, July 2-5 2019, Paris

6. Educational Data Mining
IV2019-France, July 2-5 2019, Paris

7. • Continuous flow of data
• sensors, online transactions, health monitoring, network traffic,…
• Impractical to store and use all data
• Need of new techniques that:
• Process a finite number of data at a time
• Use a limited amount of memory
• Predict/classify at any time and in a limited amount of time
• Take into account the evolution of data
Data streams
IV2019-France, July 2-5 2019, Paris

8. • DISSFCM: Dynamic Incremental Semi-Supervised
Fuzzy C-Means
• a method for data stream classification that
• works in an incremental way
• dynamically adapt the number of clusters:
• a fixed number of clusters may not capture
adequately the evolving structure of streaming
data
Data analysis method
IV2019-France, July 2-5 2019, Paris

9. DISSFCM
IV2019-France, July 2-5 2019, Paris

10. DISSFCM
IV2019-France, July 2-5 2019, Paris

11. DISSFCM
IV2019-France, July 2-5 2019, Paris

12. OULAD Dataset
https://analyse.kmi.open.ac.uk/open_dataset
IV2019-France, July 2-5 2019, Paris

13. OULAD Dataset
https://analyse.kmi.open.ac.uk/open_dataset
IV2019-France, July 2-5 2019, Paris
80% training set
20% test set
75% labeling
3 chunks

14. Data analysis results
IV2019-France, July 2-5 2019, Paris

15. Data interpretation
IV2019-France, July 2-5 2019, Paris

16. Data interpretation
IV2019-France, July 2-5 2019, Paris
education level = no formal quals
age band= 35-55 disability = N

17. Data interpretation
IV2019-France, July 2-5 2019, Paris
home page
out collaboration subpages url
forum

18. Data interpretation
IV2019-France, July 2-5 2019, Paris
class = fail
class = pass

19. Data interpretation
IV2019-France, July 2-5 2019, Paris
class = fail
class = pass

20. Conclusions and Future
Work
• Open University Learning Analytics Dataset (OULAD)
• educational data as a stream
• DISSFCM to predict students’ outcomes
• the classification model is able to adapt and evolve
according to the new data
• interpretable results
• further work on complex and heterogeneous educational
data
IV2019-France, July 2-5 2019, Paris

21. Thanks
Gabriella Casalino
gabriella.casalino@uniba.it
IV2019-France, July 2-5 2019, Paris