In colaps, we focus on studying and modeling user activities and interactions in various learning contexts. In our research, we employ artificial intelligence, machine-learning, and data-mining approaches to model students' knowledge state and to assess students' performance. For example, we are interested in exploring the relationship between response times and student performance with the aim to improve the accuracy of predictive student models. Additionally, we are interested in modeling established pedagogies (such as the Zone of Proximal Development) with the aim to facilitate personalization and adaptation of instruction and feedback.Among others, we explore the use of data to analyze the collaborative construction of artefacts and to model collaborative practice, creative processes,
The DigiReady+ project (February, 2022 - January, 2025) aims at defining a digital readiness framework and developing relevant tools (in the form of web-based applications) that allow digital readiness measurement. We plan to perform a feasibility study across three European universities, with different levels of digital readiness, during which we will implement the framework and use the tool. Through this project, we aim at validating this data-driven framework demonstrating its effectiveness in obtaining an objective measurement of digital readiness, as well as recommendations for appropriate actions for different stakeholders. The ultimate objective is to integrate the DigiReady+ framework into the quality assurance processes of European higher education institutions.
This project is funded by the Erasmus+ programme of the European Union (KA2 Cooperation Partnerships in Higher Education, Overall Budget: 325.931 EUR, 74.070 EUR for own institution)
augMentor (expected to start on January 2023) aims to develop a novel pedagogical framework that promotes both basic skills and 21st century competencies by integrating emerging technologies. This framework will be supported by an open access AI-boosted toolkit that builds on the strengths of big data and learning analytics to provide different types of stakeholders with explainable recommendations for smart search and identification of educational resources, as well as for designing personalized learning profiles that take into account individual actors’ characteristics, needs, and preferences. Our goal is to provide guidelines to stakeholders on how to address potential underlying educational difficulties and disabilities, shape individual learning paths, or identify cases of gifted and talented students, so as to enable them to reach their full potential.
This project is funded by HORIZON Research and Innovation Actions (HORIZON-CL2-2021-TRANSFORMATIONS-01-05, approx. overall budget: 2 000 000 EUR, 390.000 Euro for own institution)