Inria Chile Talks - “Developing a data analysis framework to understand the temporal behavior of students”

Data analysis plays a crucial role in improving and understanding educational processes in our digital era. This talk presents an analytical framework designed to understand the temporal behavior of students in digital environments beyond the traditional classroom.

We investigate the evolution of students' learning strategies by integrating data analysis techniques, such as Sequence Analysis and Hidden Markov Models. This framework provides a detailed understanding of their behavior and how specific interventions influence it. The talk explores the connection between students' digital interactions, individual characteristics, and academic performance, revealing key patterns that can inform the design of more effective educational interventions. This talk gathers 2.5 years of research that demonstrate and underscore the value of AI-based data analysis techniques for detecting effective learning strategies and their potential to inform the design of educational interventions and technologies.

Esteban Villalobos, a mathematical engineer with an M.Sc. in computer science, applies his artificial intelligence knowledge to improve education. He is finalizing his Ph.D. in Computer Science at the University of Toulouse, where his research focuses on using AI methods to analyze students' behavior over time. His work, based on sequence analysis to investigate the dynamics of learning strategies, was recognized with the best paper award at the European Conference of Technology Enhanced Learning and included in 5 journal and conference papers. These advancements contribute to understanding learning processes and methodologies to extract indicators to inform the design of effective educational interventions.