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The Contributions of
Data Visualization &
Learning Analytics
for Online Courses
Ruth Cobos Pérez
Universidad Autónoma de M...
Outline
Context of the presented Case Studies: MOOCs
What can we extract and do?
Initial Analysis
Data Visualization A...
3
Context
4
What can we extract from the
data?
Demographics data
User interaction with the system
Patterns of usage
Social inter...
5
What can we do with the data?
Initial Analysis Data Visualisation
Predictive models (Intervention)
MOOCs @ UAM
https://www.edx.org/school/uamx
https://www.edx.org/
- Length video < 5 min
- Length video < 10 min and > 5 min
- Length video < 15 min and > 10 min
- Length video > 15 min
Qu...
Initial Analysis:
Social Interaction - Quijote501x Discussion
Outline
Context of the presented Case Studies: MOOCs
What can we extract and do?
Initial Analysis
Data Visualization A...
10
Two Case Studies for Dashboards
Open-DLAs: An Open Dashboard for Learning Analytics
 University Autónoma of Madrid
 ...
Open-DLAs
12
Information is organized in sections about:
 Participation in the discussion forums
 Navigation among the educationa...
Next generation of Open-DLAs
14
UoS MOOCs:
 Started October’13
 15+ courses
 25+ runs
 400K+ joiners
 200K+ learners
 30K+ course completers
UoS...
UoS Dashboard
●Identifying most active members
●Identifying relationships between learners
(Claros, I., Cobos, R, & Collazos, C. An
Appr...
Outline
Context of the presented Case Studies: MOOCs
What can we extract and do?
Initial Analysis
Data Visualization A...
Initial Studies for Predictive
Models
UC3M and UAM
 eMadrid collaboration
UoS and UAM
 cMOOC vs xMOOC
(Cobos, R., Wild...
Case Study for Predictive models
Early prediction of students who will earn a certificate (or
not), with the purpose of e...
The course at edX
Dataset Description
The dataset is composed by several files in different
formats:
Methodology
11 variables Acquisition of
a certificate?
2. Implementation of these
classification models:
• random forests ...
Methodology
11 variables Acquisition of
a certificate?
2. Implementation of these
classification models:
• random forests ...
Methodology
11 variables Acquisition of
a certificate?
2. Implementation of these
classification models:
• random forests ...
Summary of this case study
Objective: early prediction of students who will fail to accomplish
sufficient points to earn ...
edX-MAS Tool:
edX Model Analizer System
edX-MAS Tool
Visualization of the Models
Classification models:
•Boosted Logistic Regression
•Stochastic Gradient Boosting...
edX-MAS Tool
Visualization of the Models
edX-MAS Tool
More Visualizations
 Context: Learners and Learning at MOOCs
 Great amount of data in different formats
 Learning Analytics levels:
 Initi...
31
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VII Jornadas eMadrid "Education in exponential times". Mesa redonda eMadrid Learning Analytics. "The contributions of Data Visualization & Learning Analytics for Online Courses". Ruth Cobos Pérez. 04/07/2017.

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VII Jornadas eMadrid "Education in exponential times". Mesa redonda eMadrid Learning Analytics. "The contributions of Data Visualization & Learning Analytics for Online Courses". Ruth Cobos Pérez. 04/07/2017.

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VII Jornadas eMadrid "Education in exponential times". Mesa redonda eMadrid Learning Analytics. "The contributions of Data Visualization & Learning Analytics for Online Courses". Ruth Cobos Pérez. 04/07/2017.

  1. 1. The Contributions of Data Visualization & Learning Analytics for Online Courses Ruth Cobos Pérez Universidad Autónoma de Madrid Ruth.Cobos@uam.es Red eMadrid, www.emadridnet.org
  2. 2. Outline Context of the presented Case Studies: MOOCs What can we extract and do? Initial Analysis Data Visualization Approaches – Dashboards Learning Analytics Approaches – Predictive models Conclusions 2
  3. 3. 3 Context
  4. 4. 4 What can we extract from the data? Demographics data User interaction with the system Patterns of usage Social interactions Questionnaires data Etc.
  5. 5. 5 What can we do with the data? Initial Analysis Data Visualisation Predictive models (Intervention)
  6. 6. MOOCs @ UAM https://www.edx.org/school/uamx https://www.edx.org/
  7. 7. - Length video < 5 min - Length video < 10 min and > 5 min - Length video < 15 min and > 10 min - Length video > 15 min Quijote501x Initial Analysis: Length of videos vs retention of attention
  8. 8. Initial Analysis: Social Interaction - Quijote501x Discussion
  9. 9. Outline Context of the presented Case Studies: MOOCs What can we extract and do? Initial Analysis Data Visualization Approaches – Dashboards Learning Analytics Approaches – Predictive models Conclusions 9
  10. 10. 10 Two Case Studies for Dashboards Open-DLAs: An Open Dashboard for Learning Analytics  University Autónoma of Madrid  edX and open edX  xMOOC UoS Dashboard  University of Southampton (UK)  FutureLearn  cMOOC (Cobos, R, Gil, Silvia, Lareo, A., Vargas, F. Open-DLAs: An Open Dashboard for Learning Analytics. L@S: Third Annual ACM Conference on Learning at Scale April 25-26, 2016, The University of Edinburgh ) (León, M., Cobos, R., Dickens, K., White, S., Davis, H. Visualising the MOOC experience: a dynamic MOOC dashboard built through institutional collaboration. eMOOCs 2016, Graz, Austria. 22-24 Feb, 2016. pp. 461-470)
  11. 11. Open-DLAs
  12. 12. 12 Information is organized in sections about:  Participation in the discussion forums  Navigation among the educational resources  Interactions with the videos  Performance results Open-DLAs Features:  Different charts can assess learning analytics and data visualization are available  Interactive charts can be parameterized and resized  Charts can be exported in formats such as .xls, .csv, .jpg, etc.  Charts can show information of several courses, sum values and average values
  13. 13. Next generation of Open-DLAs
  14. 14. 14 UoS MOOCs:  Started October’13  15+ courses  25+ runs  400K+ joiners  200K+ learners  30K+ course completers UoS Dashboard
  15. 15. UoS Dashboard
  16. 16. ●Identifying most active members ●Identifying relationships between learners (Claros, I., Cobos, R, & Collazos, C. An Approach Based on Social Network Analysis Applied to a Collaborative Learning Experience. IEEE Transactions on Learning Technologies, (1), 1-1) UoS Dashboard
  17. 17. Outline Context of the presented Case Studies: MOOCs What can we extract and do? Initial Analysis Data Visualization Approaches – Dashboards Learning Analytics Approaches – Predictive models Conclusions 17
  18. 18. Initial Studies for Predictive Models UC3M and UAM  eMadrid collaboration UoS and UAM  cMOOC vs xMOOC (Cobos, R., Wilde, A.,and Zaluska, E. Predicting attrition from Massive Open Online Courses in FutureLearn and ed. Comparing attrition prediction in FutureLearn and edX MOOCs. Proceedings of the LAK FutureLearn Worshop in the Learning Analytics and Knowledge 2017 Conference (LAK17), Canada., 13-17 Mar) (J.L. Ruipérez Valiente, Cobos, R., Muñoz-Merino, P.J., Andujar, A., Delgado-Kloos, Early Prediction and Variable Importance of Certificate Accomplishment, European MOOC Stakeholder Summit 2017 (eMOOCs 2017))
  19. 19. Case Study for Predictive models Early prediction of students who will earn a certificate (or not), with the purpose of enabling an intervention, such as alerting those students in risk of losing their certificate “The Spain of Don Quixote” (Quijote501x, https://www.edx.org/course/la-espana-de-el-quijote-uamx-quijo ). 3530 learners enrolled 7 weeks
  20. 20. The course at edX
  21. 21. Dataset Description The dataset is composed by several files in different formats:
  22. 22. Methodology 11 variables Acquisition of a certificate? 2. Implementation of these classification models: • random forests (RF) • generalized boosted regression modeling (GBM) • Support Vector Machine (SVM) • k-nearest neighbours (kNN) • a logistic regression 3. Study of importance of variables 1. Calculus of variables regarding to: •Learners’ progress •Volume and amount of learners´ activity •Distribution of learners’ activity across the different educational resources and days of the course
  23. 23. Methodology 11 variables Acquisition of a certificate? 2. Implementation of these classification models: • random forests (RF) • generalized boosted regression modeling (GBM) • Support Vector Machine (SVM) • k-nearest neighbours (kNN) • a logistic regression 3. Study of importance of variables 1. Calculus of variables regarding to: •Learners’ progress •Volume and amount of learners´ activity •Distribution of learners’ activity across the different educational resources and days of the course Type Variable Learner Progress Progress in problems (problem_progress) Progress in videos (video_progress) Volume and amount of learner activity Time invested in problems (total_problem_time) Time invested in videos (total_video_time) Total time (total_time) Amount of sessions (number_sessions) Amount of events (number_events) Learner activity distribution Homogeneity solving problems (problem_homogeneity) Homogeneity watching videos (video_homogeneity) Number of days (number_days) Constancy (constancy)
  24. 24. Methodology 11 variables Acquisition of a certificate? 2. Implementation of these classification models: • random forests (RF) • generalized boosted regression modeling (GBM) • Support Vector Machine (SVM) • k-nearest neighbours (kNN) • a logistic regression 3. Study of importance of variables 1. Calculus of variables regarding to: •Learners’ progress •Volume and amount of learners´ activity •Distribution of learners’ activity across the different educational resources and days of the course
  25. 25. Summary of this case study Objective: early prediction of students who will fail to accomplish sufficient points to earn a certificate  so that interventions (either automatic or through instructors) can be performed before it is too late 5 machine learning algorithms, division of the data in the seven course deadlines (one per week) Results suggest that GBM model was the best from those selected in terms of both performance and stability over the first weeks After week three the most important variable regarding to learners’ progress (progress in problems) Next objective: implementation of a warning system
  26. 26. edX-MAS Tool: edX Model Analizer System
  27. 27. edX-MAS Tool Visualization of the Models Classification models: •Boosted Logistic Regression •Stochastic Gradient Boosting •Extreme Gradient Boosting •Support Vector Machine •K-Nearest Neighbors
  28. 28. edX-MAS Tool Visualization of the Models
  29. 29. edX-MAS Tool More Visualizations
  30. 30.  Context: Learners and Learning at MOOCs  Great amount of data in different formats  Learning Analytics levels:  Initial Analysis  Data Visualisation  Predictive Models  Learning Analytics are useful for:  Detecting learners’ behaviours  Improving learning environments  Improving on-line learning approaches  Identifying learners in risk  Generating warning systems Conclusions
  31. 31. 31

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