Research Interests and Contributions
My research focuses on studying and modeling user interactions in various learning contexts. To that end, I follow a combined bottom-up and top-down approach: I engage in exploring data traces of user practice and in seeking patterns that may be backed up by theoretical reasoning. Following this approach, my main contributions are in student modeling and in learning analytics.
Providing personalized and adaptive feedback
To choose the appropriate level of scaffolding and to provide personalized feedback with respect to student's needs, I follow the Vygotskian concept of the Zone of Proximal Development (ZPD). To that end, I have proposed the "Grey Area" approach that acts as a proxy for modeling the ZPD of individual students using learning analytics. According to this approach, the Grey Area is the area where the student model cannot predict with acceptable accuracy the outcome of a student step with respect to correctness and this can provide indication with respect to whether the student is (or is not) in the ZPD.
Exploring the relationship between response time (step duration) and student performance
This work builds on the hypothesis that there is no linear relationship between step duration and correctness. The rationale is that, on the one hand, a student needs a minimum amount of time in order to process the problem, retrieve appropriate information, and to construct a correct response. On the other hand, taking too long to carry out a step could indicate lack of background knowledge, failure to retrieve critical information, and inability to address the step. Therefore, there is a time frame defined by a minimum and a maximum step duration (dtmin and dtmax respectively) in which a student will likely provide the correct answer. Every step that lies outside this time frame will most likely be solved incorrectly or not solved. We identify this time frame as the Zone of Interest (ZOI) and we envision that this concept can be used to provide timely feedback to students and to improve the performance of computational student models.
Research and Teaching Experience
University of Duisburg-Essen (Junior Professor of Computational Methods for Modeling and Analysis of Learning Processes, current position)
University of Tartu (Senior Researcher / Assistant Professor, 2018 - 2021)
Carnegie Mellon University (postdoctoral researcher, 2016-2018)
University of Duisburg-Essen (postdoctoral researcher, 2014-2016)
University of Patras (phD student, 2009 - 2014)
Personal Research Funding (Estonian Research Council): Combining Machine-learning and Learning Analytics to provide personalized scaffolding for computer-supported learning activities, PSG286, Duration: 4 Years (2019 - 2022)
As part of my research, I am a member of scientific societies, such as the ISLS (I serve in the Communications Committee since 2017), SOLAR, IAED, ACM and IEEE. I participate in research activities such as program committees, conference organizations, reviewing, etc. I am a reviewer for international journals such as Computers and Education and iJCSCL, and international conferences, such as LAK, CSCL, CSCW, CHI, etc. I serve as a Program Co-Chair for Collabtech 2019 and as a program committee member for conferences (LAK, ICALT, ECTEL). Additionally, I am giving talks, webinars and demonstrations to conferences and other events. You can find the resources for my recent talks here:
October, 2020: Invited talk, Bernoulli Institute, University of Groningen, the Netherlands. Talk title: “Towards computational systems of human cognition for guiding multifaceted, personalized learning.”
July, 2020: Invited talk, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Universität des Saarlandes, Germany. Talk title: “Using computational analytics to provide personalized, adaptive scaffolding in technology-enhanced learning contexts.”
February, 2020: Invited talk, Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Germany. Talk title: “From data traces to interventions: Computational Methods in Modeling and Analysis of Learning Processes”
September, 2019: Invited talk at the Learning and Educational Technologies Research Unit, University of Kyoto, Japan (Talk title: "Computational Learning Analytics for Personalized, Adaptive Feedback in Formal Education")
May, 2019: Invited talk at the Institute for Business Informatics, Johannes Kepler University (Talk title: "Computational Learning Analytics to Provide Personalized, Adaptive Scaffolding")
April, 2019: Invited talk at the Institute of Computer Science , University of Tartu (Talk title: "Using data analytics to provide personalized, adaptive feedback in educational contexts and beyond").
December 2018: Invited talk at the European Schoolnet, Learning Research Exchange subcommittee (Talk title: Use of Artificial Intelligence and Machine Learning in OERs and educational portals)
August 2018: Invited talk at the IEEE Estonia Section Meeting at Jäneda, Estonia (talk title: " LA Tartu: Designing a Learning Analytics platform for the University of Tartu… and beyond!")
October 2017: Invited talk at the ScienceEd meetings, University of Pittsburgh
September 2017: Invited talk at the HCII Seminar Talks (video)
April 2017: Two demos for the HCII Demo Day, Carnegie Mellon University
November 2016: Invited talk at the PAWS Lab, University of Pittsburgh (slides)