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,

Logo of the Project DigiReady+

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 co-funded with the support of the Erasmus+ programme of the European Union.