Incremental and Adaptive fuzzy clustering for Virtual Learning Environments Data Analysis

Sep 3, 20190 likes81 views

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

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

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

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

• 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

• 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

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

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

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

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

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

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

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

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

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

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

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APM People Interest Network Conference 2025 - Autonomy, Teams and Tension - Oliver Randall & David Bovis - Own Your Autonomy Oliver Randall Consultant, Tribe365 Oliver is a career project professional since 2011 and started volunteering with APM in 2016 and has since chaired the People Interest Network and the North East Regional Network. Oliver has been consulting in culture, leadership and behaviours since 2019 and co-developed HPTM® an off the shelf high performance framework for teams and organisations and is currently working with SAS (Stellenbosch Academy for Sport) developing the culture, leadership and behaviours framework for future elite sportspeople whilst also holding down work as a project manager in the NHS at North Tees and Hartlepool Foundation Trust. David Bovis Consultant, Duxinaroe A Leadership and Culture Change expert, David is the originator of BTFA™ and The Dux Model. With a Masters in Applied Neuroscience from the Institute of Organisational Neuroscience, he is widely regarded as the ‘Go-To’ expert in the field, recognised as an inspiring keynote speaker and change strategist. He has an industrial engineering background, majoring in TPS / Lean. David worked his way up from his apprenticeship to earn his seat at the C-suite table. His career spans several industries, including Automotive, Aerospace, Defence, Space, Heavy Industries and Elec-Mech / polymer contract manufacture. Published in London’s Evening Standard quarterly business supplement, James Caan’s ‘Your business’ Magazine, ‘Quality World’, the Lean Management Journal and Cambridge Universities ‘PMA’, he works as comfortably with leaders from FTSE and Fortune 100 companies as he does owner-managers in SME’s. He is passionate about helping leaders understand the neurological root cause of a high-performance culture and sustainable change, in business. Session | Own Your Autonomy – The Importance of Autonomy in Project Management #OwnYourAutonomy is aiming to be a global APM initiative to position everyone to take a more conscious role in their decision making process leading to increased outcomes for everyone and contribute to “a world in which all projects succeed”. We want everyone to join the journey. #OwnYourAutonomy is the culmination of 3 years of collaborative exploration within the Leadership Focus Group which is part of the APM People Interest Network. The work has been pulled together using the 5 HPTM® Systems and the BTFA neuroscience leadership programme. https://www.linkedin.com/showcase/apm-people-network/about/

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Incremental and Adaptive fuzzy clustering for Virtual Learning Environments Data Analysis

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

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