SIRD – SI-eL Learning Analytics International Conference 2020
Data search, research data in education. How and why.
Turin, the dates will be defined…
In the last few years, digital innovation has brought some substantial differences in the collection and treatment of empirical data. Educational and training institutions now find themselves managing millions of data that could be used to help improve learning and teaching processes, as highlighted by the most recent studies on Learning Analytics. Under the definition of Learning Analytics, are included a plurality of approaches to data collection, analysis to monitor, evaluate, improve educational and training interventions.
The survey can make use of multiple techniques, technologies and supports. The analysis can refer to multiple procedures, approaches, statistical, logical and computational. The results can be used for different purposes, according to various methods and related to a more variegated set of evidences that take into account what the research of the past has already produced and the meta-analyzes have aggregated and consolidated.
After the success of the conference on Learning Analytics promoted by Sird and Sie-l at the University of Rome La Sapienza in May 2019, this new appointment has the objective of bringing out and comparing data collection and analysis practices aimed to generate a real added value for the ongoing monitoring and redesigning of learning environments and educational and training paths.
The work will stimulate the debate and interconnection between scholars dealing with the use of data in an operational research perspective, in a perspective aimed at broadening research horizons and interdisciplinary synergies.
- Data collection techniques and tools in educational and training contexts
- Data collection and presentation in digital learning environments
- Frameworks for the evaluation of blended learning learning courses and MOOCs
- Data literacy
- Ethics in Learning Analytics
- Learning Analytics and Lifelong Learning
- Data collection, analysis, presentation and self-regulatory processes in digital learning environments
- Collection and analysis of data in the assessment of learning and system
- Data collection and analysis in educational and training planning
- Data collection and analysis to contrast the drop-out in MOOCs
- Data analysis techniques and tools (statistical procedures, logical models, computational models, data mining) in educational and training contexts
- Collection and analysis of data and predictive models on learning
- Models and methods for the use of research results and general evidence for the improvement of learning environments and educational and training interventions
- Models and methods of operational research
- Meta-analysis and systematic reviews and their use in improving educational and training practices
- Epistemological and methodological reflections on the use of data as a vehicle for improvement in learning environments and educational and training interventions.
Piero Lucisano, Tommaso Minerva (co-chair)
Barbara Bruschi, Luciano Cecconi, Cristina Coggi, Anna Dipace, Loretta Fabbri, Ettore Felisatti, Paolo Ferri, Patrizia Ghislandi, Maria Luisa Iavarone, Alessandra La Marca, Marina Marchisio, Antonio Marzano, Giovanni Moretti, Loredana Perla, Veronica Rossano, Marina Rui, Susanna Sancassani, Roberto Trinchero, Ira Vannini.
Luciano Cecconi, Alberto Parola, Daniela Robasto, Marina Rui, Roberto Trinchero.