dr. G. Sedrakyan (Gayane)

I obtained my PhD in Business Economics focusing on information systems, model-driven engineering, behavior / process analytics. My research interests include code generation (soft/web, backend/frontend/UI) to support simulation/testability, semantic/syntactic validation of business requirements represented as models (UML/XML/text) and process- / behavior- analytics based recommendation/feedback automation. My current teaching areas include Business Intelligence topics (e.g. BI methodologies, Business analytics, visual analytics dashboards, business performance management, process analytics) as well as low code application development. I am also interested in expanding my domain towards a broader context of data analytics including but not limited to NLP; machine learning, explainable AI.

Mathematics

# Concepts
# Framework
# Learning
# Learning Process
# Quantification

Engineering & Materials Science

# Feedback

Social Sciences

# Learning Behavior
# Regulation

Faculty of Behavioural, Management and Social Sciences (BMS), Industrial Engineering & Business Information Systems (IEBIS)

Current research topics / projects

How educational feedback needs changed during the times of Covid pandemics and what are the long-term effects?

TeToMoCo: The goal of the project is three-fold: 1. TeToMo framework that combines the state-of-the-art natural language processing approaches and techniques for identifying potential architecture elements candidates out of textual descriptions (business requirements). 2. A subsequent prototype implementation that can assist a knowledge construction process through (semi-) automatic generation and validation of UML models. 3. Automatic web application generation (backend/frontend/UI) out of generated UML/XML models following principles of Model Driven Engineering

Sedrakyan, G., Abdi, A. , van den Berg, S. M. , Veldkamp, B. P. , & van Hillegersberg, J. (2022). Text-To-Model (TeToMo) Transformation Framework to Support Requirements Analysis and Modeling. In Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - MODELSWARD (pp. 129-136). SCITEPRESS. https://doi.org/10.5220/0010771600003119

Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in human behavior, 107, [105512]. https://doi.org/10.1016/j.chb.2018.05.004

Sedrakyan, G., Dannerlein, S., Pammer-Schindler, V., & Lindstaedt, S. (2020). Measuring learning progress for serving immediate feedback needs: Learning process quantification framework (lpqf). In C. Alario-Hoyos, M. J. Rodríguez-Triana, M. Scheffel, I. Arnedillo-Sánchez, & S. M. Dennerlein (Eds.), Addressing Global Challenges and Quality Education: 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Proceedings (pp. 443-448). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12315 LNCS). Springer. https://doi.org/10.1007/978-3-030-57717-9_42

Naar mijn profiel

Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.