Background and activities
Zacharoula Papamitsiou (female) is a senior researcher at the Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU). Dr. Papamitsiou holds a Ph.D. degree from the University of Macedonia, Thessaloniki, Greece, in adapting and personalizing learning services for supporting learners’ decision-making using Learning Analytics. Her research interests include user modeling, quantified-self technologies, multimodal learning, human-computer interaction, and autonomous learning. She is an experienced Research Assistant with a demonstrated history of working on complex learning aspects. She has published articles in ranked international journals including Computers in Human Behavior, British Journal of Educational Technology, Journal of Computer Assisted Learning, IEEE Transactions on Learning Technologies, Educational Technology Research and Development, and Educational Technology and Society, as well as in international conferences such as the ACM UMAP, LAK, EC-TEL. She is a professional member of ACM, IEEE Technical Committee on Learning Technology, founding member of the Trondheim-ACM-W Chapter, and has served/serves in various organization committees (e.g., associate chair, workshop chair) and program committees. Papamitsiou is also recipient of the ERCIM fellowship.
Scientific, academic and artistic work
Displaying a selection of activities. See all publications in the database
- (2021) Supporting Learners in a Crisis Context with Smart Self-Assessment. Lecture Notes in Educational Technology.
- (2020) Mapping child–computer interaction research through co-word analysis. International Journal of Child-Computer Interaction. vol. 23-24.
- (2020) Utilizing multimodal data through fsQCA to explain engagement in adaptive learning. IEEE Transactions on Learning Technologies. vol. 13 (4).
- (2020) Games for Artificial Intelligence and Machine Learning Education: Review and Perspectives. Lecture Notes in Educational Technology.
- (2020) The impact of on‐demand metacognitive help on effortful behaviour: A longitudinal study using task‐related visual analytics. Journal of Computer Assisted Learning.
- (2019) Fostering Learners’ Performance with On-demand Metacognitive Feedback. Lecture Notes in Computer Science (LNCS). vol. 11722.
- (2019) Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach. British Journal of Educational Technology (BJET). vol. 50 (6).
- (2019) Modelling Learners’ Behaviour: A Novel Approach Using GARCH with Multimodal Data. Lecture Notes in Computer Science (LNCS). vol. 11722.
Part of book/report
- (2020) From childhood to maturity: Are we there yet? Mapping the intellectual progress in learning analytics during the past decade. LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge.
- (2020) Computing Education Research Landscape through an Analysis of Keywords. ICER '20: Proceedings of the 2020 ACM Conference on International Computing Education Research.
- (2020) On the Dependence Structure Between Learners' Response-time and Knowledge Mastery: If Not Linear, Then What?. UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization.
- (2020) Predicting learners' effortful behaviour in adaptive assessment using multimodal data. LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge.
- (2018) Explaining learning performance using response-time, self-regulation and satisfaction from content: an fsQCA approach. LAK '18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge.