Roberto Trinchero, Tommaso Minerva
The analysis of the relationships that characterize the learning and the contextual factors that influence these relationships represent one of the contemporary global challenges faced by researchers in a number of areas, particularly in education, psychology, sociology and computer science. Significant progresses have been achieved in a range of disciplines, from educational psychology to artificial intelligence in education, interested in the proactive role of students in the learning processes and the effectiveness of the teaching actions. Thanks to the widespread diffusion of digital learning environments (Learning Management System, LMS) and specific protocols (for example, eye-tracking, activity monitoring, video analysis, content analysis, sentiment analysis, immersive worlds, social network analysis, interaction analysis) currently it is possible to have databases of such dimensions to require entirely new approaches and analysis tools. In the last decade, these new information conditions and the consequent mass production of data, the so-called big data, have prompted the development of a new research field, also in the educational field, known as Learning Analytics (LA) precisely because it focuses on data concerning learning processes. This research field, initially developed in the Anglo-Saxon academic world, is gradually defining its boundaries, aims, methods and techniques. Currently, relevant aspects of the teaching-learning process, including of course those concerning evaluation processes, can be explored and analysed in a new way thanks to the methods and the techniques developed within the LA research community. However, the pedagogical dimensions that should accompany and define, in this specific field of research, the conceptualization of the analysis models, the criteria to accompany the decisional processes related to the “in process” monitoring and evaluation and those for the access, viewing and evaluation of the final results. In other words, the pedagogical contribution to research and development activities in the field of LA is still weak. In fact, while methods and techniques for detecting and analysing the data generated by digital learning environments are starting to be sufficiently clear, thanks to the contribution of mathematicians, statisticians and technologists above all, there is not as much clarity on the purposes for which to analyse such data and on the finalization of the analyses within the educational contexts. Since even in research the ends orient the means, the scientific community of the LA considers as fundamental the contribution of the pedagogy to identify and explain the reasons that must guide the analysis of the data and, therefore, to give new impetus and new objectives to the research.
The topics of study and research of the observatory are:
- Learning Analytics (in all their forms including that of initial and continuing teacher education, possibly discriminating between areas and recipients, i.e. teachers and learners)
- Educational Data Mining
- Academic Analytics
- Evaluation systems and tools in LMS
- Evaluative systems and tools in MOOCs
- Data visualization models in online systems
The initial nucleus of the observatory consists of: Luciano Cecconi, Antonio Marzano, Pierpaolo Limone, Antonella Poce, Valentina Grion, Giuliano Vivanet, Michele Biasutti, Giovanni Bonaiuti, Emilia Restiglian, Anna Dipace, Paolo Ferri.