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“). 

the team

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.

Irene studied Electrical and Computer Engineering in the University of Patras, where she also completed her PhD on Educational Technologies and Human-Computer Interaction. She has previously worked as a postdoctoral researcher at the Human-Computer Interaction Institute in Carnegie Mellon University (Pittsburgh, USA) and at the Department of Computational and Cognitive Sciences, University of Duisburg-Essen (Germany). 
Personal website: http://www.iachounta.com
Irene-Angelica Chounta Principle Investigator
Eric Roldan (Educational Technology MSc) is a phD student at the Institute of Education in the University of Tartu. His research interests include the analysis, design, and development of adaptive educational technologies where learning analytics are used to study and measure, among other aspects, learning outcomes between heterogeneous and homogeneous groups in cooperative problem solving and collaborative learning scenarios. Eric is the co-founder of the educational project MusicMath, which was awarded the EAPRIL Best Research and Practice Project 2019.
Eric Roldan
Eric Roldan
PhD student
Paraskevi Topali (Educational Technology MSc) is a Ph.D. candidate in the GSIC-EMIC Research Group at the University of Valladolid, Spain and she is currently a visiting Ph.D. student at the Institute of Computer Science, University of Tartu. Paraskevi has a background in Pedagogy and her research interests include learning at scale and MOOCs, feedback provision strategies, and the alignment of Learning Design with Learning Analytics. Her research focuses on how MOOC instructors can be assisted in identifying and supporting struggling learners during the course run-time.
Paraskevi Topali, visiting phD student
Paraskevi Topali
visiting PhD student
Hasan Mohammed Tanvir is an MSc. student at the Institute of Computer Scince, University of Tartu. His research interest lies in big data and analytics. He is also interested in implementation of machine learning models for improved decision making. Hasan is currently working as a student intern on developing predictive models for early dropout prediction and performance assessment in the context of Estonian Higher Education.
Hasan Tanvir
MSc student intern
Moein Fathi (Computer Science BSc) is a developer at the Centre for Educational Technology. He is a multifaceted programmer with experience in various technologies and programming languages, such as Javascript and Python, among others. During the last three years, he participated in research projects at the University of Malaya, Malaysia. His research interests focus on the areas of machine learning and data analytics.
Moein Fathi
Developer