V Jornadas eMadrid sobre “Educación Digital”. Pedro Muñoz Merino, Universidad Carlos III de...

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Learning Analytics for Massive Open Online Courses Pedro J. Muñoz-Merino Twiter: @pedmume Twiter: @pedmume email: [email protected] email: [email protected] Universidad Carlos III de Madrid

Transcript of V Jornadas eMadrid sobre “Educación Digital”. Pedro Muñoz Merino, Universidad Carlos III de...

Learning Analytics for Massive Open Online Courses

Pedro J. Muñoz-Merino

Twiter: @pedmumeTwiter: @pedmumeemail: [email protected]: [email protected]

Universidad Carlos III de Madrid

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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

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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.

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Inference of indicators

● Pedagogical theories

● Best practices

● Adapted models from other systems

● Indicators that can predict other interesting indicators

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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)

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)

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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)

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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).

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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)

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 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%

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)

Clustering depending on effectiveness

Grouping types of students

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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)

15Adaptation 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)

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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&f

eature=youtu.be

● Authors José Antonio Ruipérez Valiente, Pedro Jose Muñoz

Merino, Héctor Javier Pijeira Díaz, Javier Santofimia Ruiz, Carlos Delgado Kloos

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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

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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

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Example I: Self-reflection

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Example II: Evaluation of students

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Example III: Flipped classroom, evaluation of materials

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Example IV: Evaluation of the course

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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

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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

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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/

Learning Analytics for Massive Open Online Courses

Pedro J. Muñoz-Merino

Twiter: @pedmumeTwiter: @pedmumeemail: [email protected]: [email protected]

Universidad Carlos III de Madrid