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Examples of Tools
that supports
Learning Analytics in
MOOCs
Ruth Cobos Pérez
Universidad Autónoma de Madrid
Ruth.Cobos@uam...
Outline
 Context: MOOCs
 Types of Data Analysis
 Descriptive Analytics: Open-DLAs
 Predictive Analytics: edX-MAS+
 Pr...
3
Context
MOOCs @ UAM
https://www.edx.org/
https://www.edx.org/school/uamx
Types of Data Analytics
Descriptive Analytics Predictive Analytics
(Intervention)
Prescriptive Analytics
edX-MAS+Open-DLAs...
Types of Data Analytics
Content Analytics
Opinion Analysis
edX-CAS
7
Open-DLAs: An Open Dashboard
for Learning Analytics
8
Open-DLAs: An Open Dashboard
for Learning Analytics
 Information is organized
in sections about:
 Participation in the...
edX-MAS+:
Model Analizer System
 edX-MAS+: it is an interactive tool for supporting the
generation of Predictive Models f...
edX-MAS+:
Data Science Proccess
edX-MAS+:
Input Variables
 edX stores learners’ interactions
as events, each one has a
category
 The edX categories: nav...
edX-MAS+:
Generation of the Models
 Boosted Logistic Regression
 Stochastic Gradient Boosting
 Extreme Gradient Boostin...
edX-MAS+:
Visualization of the Models
edX-WS:
Warning System for edX MOOC
 This tool was created for the preventive detection of users
at risk of not successfu...
edX-WS:
Warning System for edX MOOC
edX-CAS: Content Analyzer
System for edX MOOC
Madrid, 2013-06-13
16
 This tool was created for textual content analysis i...
edX-CAS
Madrid, 2013-06-13
17
Conclusions
 Context: Learners and Learning Environments
 Great amount of data in different formats
 Types of Data Anal...
19
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«Ejemplos de herramientas que nos facilitan las analíticas de aprendizaje en MOOCs», por Ruth Cobos Pérez, profesora en la UAM

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23_11_2018 Seminario eMadrid sobre «Analíticas para el aprendizaje» organizado por la UAM

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«Ejemplos de herramientas que nos facilitan las analíticas de aprendizaje en MOOCs», por Ruth Cobos Pérez, profesora en la UAM

  1. 1. Examples of Tools that supports Learning Analytics in MOOCs Ruth Cobos Pérez Universidad Autónoma de Madrid Ruth.Cobos@uam.es Red eMadrid, www.emadridnet.org
  2. 2. Outline  Context: MOOCs  Types of Data Analysis  Descriptive Analytics: Open-DLAs  Predictive Analytics: edX-MAS+  Prescriptive Analytics: edX-WS  Content Analytics: edX-CAS  Conclusions 2
  3. 3. 3 Context
  4. 4. MOOCs @ UAM https://www.edx.org/ https://www.edx.org/school/uamx
  5. 5. Types of Data Analytics Descriptive Analytics Predictive Analytics (Intervention) Prescriptive Analytics edX-MAS+Open-DLAs edX-WS
  6. 6. Types of Data Analytics Content Analytics Opinion Analysis edX-CAS
  7. 7. 7 Open-DLAs: An Open Dashboard for Learning Analytics
  8. 8. 8 Open-DLAs: An Open Dashboard for Learning Analytics  Information is organized in sections about:  Participation in the discussion forums  Navigation among the educational resources  Interactions with the videos  Performance results (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 )  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
  9. 9. edX-MAS+: Model Analizer System  edX-MAS+: it is an interactive tool for supporting the generation of Predictive Models for edX MOOCs  It is based on the data collected from a MOOC, allowing the simple application of a complete Data Science process, where Machine Learning algorithms will be applied to predict:  which learners are likely to leave the course (dropout)  which learners are expected to pass the course (certificate acquisition) (R. Cobos, V. Macias, edX-MAS: Model Analyzer System. International Conference Technological Ecosystems for Enhancing Multiculturality, TEEM 2017 R. Cobos, L. Olmos, A Learning Analytic Tool for Predictive Modeling of Dropout and Certificate acquisition on MOOCs for Professional Learning, IEEE IEEM 2018)
  10. 10. edX-MAS+: Data Science Proccess
  11. 11. edX-MAS+: Input Variables  edX stores learners’ interactions as events, each one has a category  The edX categories: navigation events, videos events, forum events and problem events  Spent time and number of interactions are calculated per each event category  And others which summarize events, time and sessions • num_events, num_sessions, total_time • nav_events, nav_time • video_events, video_time • forum_events, forum_time • problem_events, problem_time • connected_days • consecutive_inactivity_days • num_diff_problems • num_diff_videos
  12. 12. edX-MAS+: Generation of the Models  Boosted Logistic Regression  Stochastic Gradient Boosting  Extreme Gradient Boosting  Support Vector Machines (SVM)  k-Nearest Neighbours (kNN)  Random Forest  Naïve Bayes  Neuronal Network (one hidden layer)  Bayesian Generalized Linear Model  Classification and Regression Tree (CART)
  13. 13. edX-MAS+: Visualization of the Models
  14. 14. edX-WS: Warning System for edX MOOC  This tool was created for the preventive detection of users at risk of not successfully completing a course or not obtaining the certificate based on their indicators (input variables). These indicators collect the activity and the tasks carried out by the students throughout their learning  The tool detects these users, it allows generating suggestions and exports comparative charts in order to help and follow the evolution of each student
  15. 15. edX-WS: Warning System for edX MOOC
  16. 16. edX-CAS: Content Analyzer System for edX MOOC Madrid, 2013-06-13 16  This tool was created for textual content analysis in edX MOOCs, it provides general information during the course as well as information related with the field of Natural Language Processing (NLP), focusing the analysis on emotions recognition  Sentiment Analysis:  Polarity: positive, negative or neutral  Subjectivity: objective or subjective  Semantic, syntax and morphological analysis  The information provides by the tool can be used for improving the course materials
  17. 17. edX-CAS Madrid, 2013-06-13 17
  18. 18. Conclusions  Context: Learners and Learning Environments  Great amount of data in different formats  Types of Data Analysis - Tools  Descriptive Analytics: Open-DLAs  Predictive Analytics: edX-MAS+  Prescriptive Analytics: edX-WS  Content Analytics: edX-CAS  Learning Analytics are useful for:  Detecting learners’ behaviors  Improving learning environments  Improving on-line learning approaches  Improving learning materials  Identifying learners in risk  Providing recommendations and advices to learners
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