LEDA: A Learning Analytics Based Framework to Analyze Remote Labs Interaction.

In order to support the application of digital technologies in the educational context, it is important to understand the relationships between learners and the technologies they use during a practice. Among educational technologies are remote laboratories, tools that provide the manipulation of real experiments through an online platform, available 24/7, overcoming constraints of time and space. To extract information of the large amount of data generated during interactions, it is necessary to use technology-supported representations, in order to apply techniques capable of analyzing and extracting information from the data generated from interactions with technologies and, finally, enabling learning interventions. Learning Analytics (LA) consist of on measuring, collecting, analyzing and reporting student data during practices. LA combines data mining techniques to extract information and pedagogical intervention. This project proposes to develop an educational data mining framework based on Learning Analytics interventions, called LEDA (Laboratory Experimentation Data Analysis). The LEDA framework aims to extract information of interaction data with remote laboratories to relate students' interaction behavior with their learning progress. Our approach will apply association rules and clustering techniques using learning data, including clicks, number of controlled components, and time spent during the activity.