colaps: Computational Learning Analytics for Personalized Scaffolding
The goal of colaps is to explore the use of computational data analytics, namely machine-learning and data mining, with the aim to support learning from the perspective of personalization and adaptation and in relation to tutoring feedback and scaffolding. Our research focuses on data analytics to facilitate learning in formal education and ultimately in monitoring and guiding complex human activities. Our interests extend to using modern technologies in order to facilitate and promote learning by bridging the gap between theory and modern, data driven, technology-oriented practice: combining top-down, established pedagogical theories with bottom-up, data-driven computational approaches.
colaps was a 2-year-long (2019 – 2021) research project funded by the Estonian Research Council (PUT grant PSG286 – “Combining Machine-learning and Learning Analytics to provide personalized scaffolding for computer-supported learning activities“).
Dr. Irene-Angelica Chounta. Her research focuses on computational learning analytics for technology-enhanced learning and educational technologies. Her main research interest is to model learners in order to provide adaptive and personalized feedback, either in formal or informal contexts.
Personal website: http://www.iachounta.com