Below the list of projects cofunded by the María de Maeztu program (selected via internal calls, in this link the first one launched at the beginning of the program, and in this link the second one, launched in September 2016).

In addition, the program supports:

The detail of the internal procedures for the distribution of funds associated to the program can be found here

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Educational Data Science

Educational Data Science

Educational Data Science

During recent years, analytics or data mining techniques have been used to extract actionable information from large data quantities in an increasing variety of scientific fields. In the context of education, the use of technology to mediate activities in learning environments allows the collection of large data sets about student interactions. The area of learning analytics and educational data science have emerged to explore how this data can be used to increase the understanding and quality of learning experiences. The field has undergone a fast expansion phase and the use of data is now being considered in aspects such as for example student retention. Recent detailed analysis of the use of data in learning environments show an increasingly complex landscape influenced by multiple disciplines to deploy effective initiatives. Existing models however do not pay the deserved attention to the connection between analytics and learning design. The integration of learning analytics with learning design has been identified as important.

 

However, a tight integration of data analytics in learning designs has not yet been exploited to its full potential. The vision is the overall research program of the PI aims at articulating the variety and connections at various levels (community, design and implementation layers, see figure) between data obtained from students’ and teachers’ actions and the creation and implementation stages of “learning designs”.

 

Learning Design is the field studying how teachers can model potentially effective learning activities using computational representations interpretable by software systems. Computational representations of learning designs also enables the automatic analysis, reuse and sharing of the modelled learning activities. Previous research by the PI has been focused on Learning Design, with the Integrated Learning Design Environment (ILDE, http://ilde.upf.edu/about) as a remarkable achievement in terms of developed infrastructure to enable the modelling and sharing of learning designs. ILDE is a Community Environment that integrates a number of learning design conceptualization, authoring and implementation tools.

 

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