Deepwise

Deepwise

E- learning environment that adapts the abilities of any student.
Challenge

Challenge

The challenge of this project, co-financed by the Ministry of Energy, Tourism and Digital Agenda (Num. TSI-100105-2017-9), has been to create a student-machine interaction that had into account the variety of educational resoruces, created by the teacher and the student´s interests and his/her intellectual abilities.

This R & D project aims to develop an intelligent platform that, through BigData, Machine Learning and Natural Language Processing techniques, can adapt to the specific needs of each student.

Adaptative Learning

Adaptative Learning

Adaptive learning implies a conception of each student as a unique individual with a particular type of intelligence. Each one will have their rhythm of studies and will use different ways to achieve their goals.

Emphasis arranged on the importance of addressing the strengths and weaknesses of each student while adapting the educational system to the needs of each student.

Our solution

Creating the ML platform, for adaptive learning I OpenEDx
A technological and user experience challenge.
Data research
Data research
Data Mining: ability to offer customized courses based on the student´s profile, his interests and his interaction with the platform Machine Learning: personalized courses, adapting the path, the syllabus and the representation of the contents, by means of the student´s cataloguing, based on Gardner´s different intelligencies.
Language Processing
Language Processing
Natural Processing Language This technology infers information knowledge gathered in the Big Data module, generated by human beings in their natural language. Moreover, educational agents able to answer the students in the forum in their own languages are created. Virtual participants are also answered by an intelligent and automated reply in the forum, based on the learning of previous editions of the course.
Machine learning challenges
Machine learning challenges
Indexing of the intelligence, learning styles and students interests employing self-study algorithms that allow that the recommendations made by the adaptive learning module develop and improve over time. The algorithms suggest the recommended courses and contents to each student in the platform based on the current alternatives and based on their intelligence type.

State of the art enhancements

Machine Learning platform for adaptive learning and automate communication based on Big Data techniques, Natural Language Processing and intelligent algorithms.
Results

Results

The solution based on different components:

  • MOOC e-learning platform
  • Big Data Module
  • Natural Language Processing Engine
  • Adaptive Pedagogic Agent Module

Technologies

Talend
Hadoop
Mongodb
Hortonworks
Spark
Solr
Elastic
Apache Hbase
Hive
Kafka
Mahout
Mapreduce

The project has been co-financed by the Ministry of Energy, Tourism and Digital Agenda.

Project number: TSI-100105-2017-9
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