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Learning Analytics for Massive
Open Online Courses
Pedro J. Muñoz-Merino
Twiter: @pedmumeTwiter: @pedmume
email: pedmume@i...
2
Learning Analytics for MOOCs
● The need of Learning Analytics is increased in
MOOCs
 MOOCs enable rich user interaction...
3
Challenges of Learning Analytics for
MOOCs
● Technological issues
 Effective processing of the data
 Compatibility bet...
4
Inference of indicators
● Pedagogical theories
● Best practices
● Adapted models from other systems
● Indicators that ca...
5
Examples of indicators I
Learning
profiles
Effectiveness,
efficiency,
interest
Behaviours
Skills
Emotions
Pedro J. Muñoz...
Example II: Inference of optional activities
José A. Ruipérez Valiente, Pedro J. Muñoz Merino, Carlos Delgado‐ ‐
Kloos, Ka...
7
Example III: Inference of more precise
indicators: Effectiveness
Pedro J. Muñoz-Merino, José A. Ruipérez-Valiente, Carlo...
8
Example IV: Inference of emotions
Derick Leony, Pedro J.
Muñoz-Merino, José A.
Ruipérez-Valiente, Abelardo
Pardo, David ...
9
Visual analytics for Khan Academy
José A. Ruipérez-Valiente, Pedro J. Muñoz-Merino, Derick Leony,
Carlos Delgado Kloos, ...
Level of relationship
•30.3 % hint avoider
•25.8 % video avoider
•40.9 % unreflective user
•12.1% of hint abuser
Analysis of optional activities
•76.81% of students who accessed,
did not use any optional activities
•Difference of use p...
Level of relationship: optional
activities vs learning activities
Optional
activities
sig.
(2-tailed)
N = 291
Exercises
ac...
Clustering depending on
effectiveness
Grouping types of students
14
Prediction models
Learning gains= 25.489 - 0.604 * pre_test +
6.112 * avg_attempts + 0.017 * total_time +
0.084 * profi...
15
Adaptation rules
Pedro J. Muñoz-Merino, Carlos Delgado Kloos, Mario Muñoz-
Organero, Abelardo Pardo, "A Software Engine...
16
ANALYSE: LA support for open edX
● General information
 http://www.it.uc3m.es/pedmume/ANALYSE/
● Github
 https://gith...
17
MOOC of maths
● MOOC of maths:
 Available at: http://ela.gast.it.uc3m.es
 Topics: Units of measurement, algebra, geom...
18
ANALYSE in the MOOC of maths
● Uses of ANALYSE in the experience
 Self-reflection for students
 Support for the flipp...
19
Example I: Self-reflection
20
Example II: Evaluation of students
21
Example III: Flipped classroom,
evaluation of materials
22
Example IV: Evaluation of the course
23
Present and future work in ANALYSE
● Design and implementation of higher level
learning indicators and their correspond...
24
ANALYSE in the Spanish TV
● Part of the mapaTIC project:
 “La Aventura del Saber”, La 2, TVE,
http://www.rtve.es/alaca...
25
WAPLA@EC-TEL
● Workshop on Applied and Practical Learning
Analytics
● Topics:
 Hands on Tutorial on exploratory data
a...
Learning Analytics for Massive
Open Online Courses
Pedro J. Muñoz-Merino
Twiter: @pedmumeTwiter: @pedmume
email: pedmume@i...
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V Jornadas eMadrid sobre “Educación Digital”. Pedro Muñoz Merino, Universidad Carlos III de Madrid: Learning analytics for Massive Open Online Courses

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V Jornadas eMadrid sobre “Educación Digital”. Pedro Muñoz Merino, Universidad Carlos III de Madrid: Learning analytics for Massive Open Online Courses. 2015-06-30

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V Jornadas eMadrid sobre “Educación Digital”. Pedro Muñoz Merino, Universidad Carlos III de Madrid: Learning analytics for Massive Open Online Courses

  1. 1. Learning Analytics for Massive Open Online Courses Pedro J. Muñoz-Merino Twiter: @pedmumeTwiter: @pedmume email: pedmume@it.uc3m.esemail: pedmume@it.uc3m.es Universidad Carlos III de Madrid
  2. 2. 2 Learning Analytics for MOOCs ● The need of Learning Analytics is increased in MOOCs  MOOCs enable rich user interactions with educational activities  Big amount of data to extract useful conclusions  Big amount of users increases the need of self- reflection and augmented vision for teachers ● Learning analytics support is in an early stage in MOOCs  Many learning analytics functionality to be developed
  3. 3. 3 Challenges of Learning Analytics for MOOCs ● Technological issues  Effective processing of the data  Compatibility between different sources ● Useful indicators ● Useful visualizations ● Know the relationships among indicators. Know the causes ● Actuators: Recommenders, adaptive learning, etc.
  4. 4. 4 Inference of indicators ● Pedagogical theories ● Best practices ● Adapted models from other systems ● Indicators that can predict other interesting indicators
  5. 5. 5 Examples of indicators I Learning profiles Effectiveness, efficiency, interest Behaviours Skills Emotions Pedro J. Muñoz-Merino, J.A. Ruipérez Valiente, C. Delgado Kloos, “Inferring higher level learning information from low level data for the Khan Academy platform.” Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 112–116. ACM, New York, (2013)
  6. 6. Example II: Inference of optional activities José A. Ruipérez Valiente, Pedro J. Muñoz Merino, Carlos Delgado‐ ‐ Kloos, Katja Niemann, Maren Scheffel, “Do Optional Activities Matter in Virtual Learning Environments?”,European Conference on Technology Enhanced Learning, pp 331-344, (2014)
  7. 7. 7 Example III: Inference of more precise indicators: Effectiveness Pedro J. Muñoz-Merino, José A. Ruipérez-Valiente, Carlos Alario- Hoyos, Mar Pérez-Sanagustín, Carlos Delgado Kloos, "Precise effectiveness strategy for analyzing the effectiveness of students with educational resources and activities in MOOCs", Computers in Human Behavior, vol. 47, pp. 108–118 (2015)
  8. 8. 8 Example IV: Inference of emotions Derick Leony, Pedro J. Muñoz-Merino, José A. Ruipérez-Valiente, Abelardo Pardo, David Arellano Martín-Caro, Carlos Delgado Kloos, "Detection and evaluation of emotions in Massive Open Online Courses", Journal of Universal Computer Science (2015) (In press).
  9. 9. 9 Visual analytics for Khan Academy José A. Ruipérez-Valiente, Pedro J. Muñoz-Merino, Derick Leony, Carlos Delgado Kloos, “ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform”, Computers in Human Behavior, vol. 47, pp. 139-148 (2015)
  10. 10. Level of relationship •30.3 % hint avoider •25.8 % video avoider •40.9 % unreflective user •12.1% of hint abuser
  11. 11. Analysis of optional activities •76.81% of students who accessed, did not use any optional activities •Difference of use per course and depending on type of optional activities •55 goals (50.9% completed) •40 votes (26 positive, 13 indifferent, 1 negative) Type of activity Percentage of activities accessed 0% 1-33 % 34-66% 67-99% 100% Regular learning activities 2.48% 51.55% 23.19% 18.84% 3.93% Optional activities 76.81% 18.43% 4.14% 0.41% 0.21%
  12. 12. Level of relationship: optional activities vs learning activities Optional activities sig. (2-tailed) N = 291 Exercises accessed: 0.429* (p=0.000) Videos accessed: 0.419* (p=0.000) Exercise abandonment: -0.259* (p=0.000) Video abandonment: -0.155* (p=0.008) Total time: 0.491* (p=0.000) Hint abuse: 0.089 (p=0.131) Hint avoider: 0.053 (p=0.370) Follow recommendations: -0.002 (p=0.972) Unreflective user: 0.039 (p=0.507) Video avoider: -0.051 (p=0.384) Proficient exercises sig. (2-tailed) N = 291 Optional activities: 0.553* (p=0.000) Goal: 0.384* (p=0.000) Feedback: 0.205* (p=0.000) Vote: 0.243* (p=0.000) Avatar: 0.415* (p=0.000) Display badges: 0.418* (p=0.000)
  13. 13. Clustering depending on effectiveness Grouping types of students
  14. 14. 14 Prediction models Learning gains= 25.489 - 0.604 * pre_test + 6.112 * avg_attempts + 0.017 * total_time + 0.084 * proficient_exercises José A. Ruipérez-Valiente, Pedro J. Muñoz-Merino, Carlos Delgado Kloos, “A predictive Model of Learning Gains for a Video and Exercise Intensive Learning Environment”, Artificial Intelligence in Education conference, pp. 760-763 (2015)
  15. 15. 15 Adaptation rules Pedro J. Muñoz-Merino, Carlos Delgado Kloos, Mario Muñoz- Organero, Abelardo Pardo, "A Software Engineering Model for the Development of Adaptation Rules and its Application in a Hinting Adaptive E-learning System", Computer Science and Information Systems, vol. 12, no. 1, pp. 203-231(2015)
  16. 16. 16 ANALYSE: LA support for open edX ● General information  http://www.it.uc3m.es/pedmume/ANALYSE/ ● Github  https://github.com/jruiperezv/ANALYSE ● Demos  https://www.youtube.com/watch?v=3L5R7BvwlDM&featur ● Authors  José Antonio Ruipérez Valiente, Pedro Jose Muñoz Merino, Héctor Javier Pijeira Díaz, Javier Santofimia Ruiz, Carlos Delgado Kloos
  17. 17. 17 MOOC of maths ● MOOC of maths:  Available at: http://ela.gast.it.uc3m.es  Topics: Units of measurement, algebra, geometry  High school education for adults  Generation of educational materials: CEPA Sierra Norte de Torrelaguna: Diego Redondo Martínez  28 videos, 32 exercises  Configuration, support and personalization of the MOOC platform at Univ. Carlos III de Madrid  Open for everyone  Flipped the classroom methodology
  18. 18. 18 ANALYSE in the MOOC of maths ● Uses of ANALYSE in the experience  Self-reflection for students  Support for the flipped classroom  Evaluation of educational materials  Evaluation of students  Evaluation of the course
  19. 19. 19 Example I: Self-reflection
  20. 20. 20 Example II: Evaluation of students
  21. 21. 21 Example III: Flipped classroom, evaluation of materials
  22. 22. 22 Example IV: Evaluation of the course
  23. 23. 23 Present and future work in ANALYSE ● Design and implementation of higher level learning indicators and their correspondent visualizations ● Scalability. Work with a big amount of users ● Recommender based on previous data analysis we have performed on other experiences -> clustering, prediction, relationship mining ● Integration with Insights
  24. 24. 24 ANALYSE in the Spanish TV ● Part of the mapaTIC project:  “La Aventura del Saber”, La 2, TVE, http://www.rtve.es/alacarta/videos/la-aventura-del- saber/aventuramapatic/3163463/  ANALYSE and the MOOC of maths: 5:30-7:11
  25. 25. 25 WAPLA@EC-TEL ● Workshop on Applied and Practical Learning Analytics ● Topics:  Hands on Tutorial on exploratory data analysis using Python and Spark, discussions about LA tools, oral presentations ● Dates: Friday 18 September ● Place: Toledo (EC-TEL conference) ● More information  http://educate.gast.it.uc3m.es/wapla/
  26. 26. Learning Analytics for Massive Open Online Courses Pedro J. Muñoz-Merino Twiter: @pedmumeTwiter: @pedmume email: pedmume@it.uc3m.esemail: pedmume@it.uc3m.es Universidad Carlos III de Madrid

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