La profesora Ruth Cobos, del grupo GHIA de la Universidad Autónoma de Madrid y miembro de SNOLA, presenta el artículo titulado “A Generative AI-Based Personalized Guidance Tool for Enhancing the Feedback to MOOC Learners” en la conferencia internacional 2024 IEEE Global Engineering Education Conference (EDUCON 2004) que se celebra en la isla de Kos (Grecia) del 8 al 11 de mayo de 2024.
Actas del congreso disponibles en IEEE Xplore.
Abstract: The widespread adoption of Massive Open Online Courses (MOOCs) has profoundly influenced higher education by granting learners access to an extensive array of educational materials. However, the substantial volume of data generated by MOOCs presents a considerable challenge for instructors who aim to assess and facilitate effective learner support. In this study, we introduce an innovative GenAI-based (Generative Artificial Intelligence) tool designed to assist and guide MOOC learners in understanding their progress in the course to enhance their performance and prevent dropout. Our proposed approach takes advantage of GenAI’s capabilities to analyze and understand anonymized learner educational data, including aspects such as course progression, assignment results, time spent on different types of content, timestamps, and other pertinent information. By applying natural language processing techniques, GenAI identifies patterns and trends within the data, enabling it to provide personalized guidance to learners to help them develop better learning strategies and enhance their performance in the course. The proposed tool, named GePeTo (Generative AIbased Personalized Guidance Tool), not only streamlines the process of analyzing large volumes of educational data but also equips instructors with practical insights into their learners’ performance and difficulties. GePeTo offers a promising solution for higher education institutions aiming to leverage the potential of MOOC data for effective learner assessment and support. Automating the analysis of educational data and delivering personalized guidance to learners will also facilitate instructors in making data-driven decisions. Ultimately, this will improve learning outcomes and educational experiences for learners in the digital age of education.
Autores: Ruth Cobos, Álvaro Becerra y Javier Sanz de la Universidad Autónoma de Madrid y Zeynab (Artemis) Mohseni de la Linnaeus University han publicado un nuevo artículo que será presentado en mayo en el congreso IEEE Global Engineering Education Conference (EDUCON). MÁS INFORMACIÓN