Unlocking Knowledge in the Digital Age

This volume, intended for educational researchers and practitioners, discusses the pivotal role of prerequisite relations between educational contents in shaping learning paths and offers tools for exploring and analyzing teaching materials. It demonstrates how uncovering the structured representation of educational text content fosters its dynamic and adaptive navigation, all while tackling the complexities of identifying prerequisite relations within such texts. Through a multidisciplinary methodology integrating corpus annotation, knowledge modelling, and deep textual analysis, the volume illustrates the interplay between form and content in textual materials, underscoring the importance of employing level-appropriate language for fostering effective learning. The efficacy of this approach is demonstrated through case studies on content modelling and textbook exploration that illustrate its potential to enhance teaching and learning across diverse domains.

Chiara Alzetta is a researcher at the Institute of Computational Linguistics “Antonio Zampolli” at the National Research Council (CNR-ILC) in Pisa and she is a member of the ItaliaNLP lab. She obtained her PhD in Digital Humanities from the Department of Modern Languages and Cultures of the University of Genoa in 2021 with a thesis on manual annotation and automatic recognition of prerequisite relations between educational concepts. Her research interests concern the definition of strategies for the personalisation of learning paths and the exploration of annotated linguistic resources.