Research background: I obtained my PhD at KU Leuven (2016, Belgium) focusing on information systems, which consisted of 2 parts: (1) Model-Driven Engineering (code generation for soft/web app including backend/frontend/UI/databases) to support requirements (data and behavior) modeling, simulation/testability, semantic/syntactic validation of business requirements represented as models (UML/XML/text), and (2) model-based cognitive feedback and behavioral feedforward generation based on behavior and process analytics.
Research expertise: My current research areas include process- / behavior- analytics, learning analytics, feedback/recommendation automation, visual analytics dashboards, model-driven engineering, low code, business intelligence and applications, explainable AI, educational technology among others. I am also interested in expanding my domain towards a broader context of digitalization and big data analytics applications in different sectors such as education, government, healthcare, logistics, etc.
Teaching: Currently my teaching areas include (1) Business Intelligence topics (e.g. Business analytics, Big Data, visual analytics & dashboards, process analytics, text analytics) and (2) Low Code application development.
- Tutorial presenter:
- Novel way of training conceptual modeling skills by means of feedback-enabled simulation (ER conference, 2015)
- Boosting requirements analysis and validation skills through feedback-enabled semantic prototyping (IEEE RE, 2015)
Industry experiences: Next to research in academia, I also combine professional experiences from government, banking and software industries.
Other professional expertise - Software Engineering (5 years in software engineering positions practicing agile methodology + 6 years using programming java, #C and related technologies in daily research activities).
Cross-disciplinary research synergies: Currently I work in the intersection of IEBIS and CODE sections researching shared areas of interest on information and education technologies. Next to UT, I combine research experiences from several other research groups:
- PhD/postdoc - Information systems / Augment (KU Leuven, Belgium)
- Research project management - IDLab (imec / UGent, Belgium)
- Postdoc - KNOW center (Technical University of Graz, Austria)
- Visiting researcher - Learning and Educational Technology (Oulu University, Finland)
- Visiting researcher - Welten Institute Research Centre for Learning, Teaching and Technology (OUNL)
Awards - The results of my PhD research were nominated in the context of university-wise educational prize for innovative feedback at KU Leuven (2016). As a computer science student I was nominated and awarded with “Best master student in Information Technologies Award (2007)” by the President of Armenia and Synopsys corporation.
- Editor: Interactive visualizations, special issue of the Information journal (MDPI)
- Reviewer: Expert Systems with Applications (Elsevier), Computers in Human Behavior (Elsevier), Computers & Education (Elsevier), Transactions on Learning Technologies (IEEE), Learning Analytics (Springer), Computer-Supported Collaborative Learning (Springer), Conferences - LAK, CAiSE.
- Co-chair: Workshop on controlled vocabularies and data platforms for Smart Food Systems (SmartFood) at ER'23
- Program committee: AMARETTO 2017 at Modelsward, Online Measures for Learning Processes at EARLI SIG 2016
- Cognitive feedback and behavioral feedforward perspectives for modeling and validation in a learning context, G Sedrakyan, M Snoeck, 2017, Model - Driven Engineering and Software Engineering, Springer
- Evaluating emotion visualizations using AffectVis, an affect-aware dashboard for students, L Derick, G Sedrakyan, PJ Munoz-Merino, C Delgado Kloos, K Verbert, 2017, Journal of Research in Innovative Teaching & Learning 10 (2), 107-125
- Process-mining enabled feedback: “tell me what I did wrong” vs.“tell me how to do it right”, G Sedrakyan, J De Weerdt, M Snoeck, 2016, Computers in human behavior 57, 352-376
- Assessing the influence of feedback-inclusive rapid prototyping on understanding the semantics of parallel UML statecharts by novice modellers, 2016, G Sedrakyan, S Poelmans, M Snoeck, Information and Software Technology 82, 159-172
- Assessing the effectiveness of feedback enabled simulation in teaching conceptual modeling, G Sedrakyan, M Snoeck, S Poelmans, 2014, Computers & Education 78, 367-382
- Do we need to teach testing skills in courses on requirements engineering and modelling? G Sedrakyan, M Snoeck, 2014, CEUR Workshop Proceedings 1217, 40-44
UT Research Information System
Google Scholar Link
Courses Academic Year 2023/2024
Courses Academic Year 2022/2023
- How educational feedback needs changed during the times of Covid pandemic and what are the long-term effects? (BMS funding) While it is evident that digitalization will be pivotal for accomplishing a transition to post-pandemics educational environments, where hybrid classroom/campus uniting the physical and digital learning experiences will most likely define the new norms, the field lacks insights to guide informed decisions in the domain of feedback digitalization. Despite the importance of this transition world-wide, still questions such as “what is the type of digital feedback that worked best during lockdown education?”, “which new formats used by teachers proved effective among students?”, “are there preferences in these new formats/elements of feedback to continue even when the lockdown education disappears?” remain unanswered.
- TeToMoCo: The goal of the project is three-fold: 1. TeToMoCo (Text-To-Model-To-Code) framework that combines the state-of-the-art natural language processing approaches and techniques for identifying potential architecture elements candidates out of business requirements articulated in natural language textual description. 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 code generation (backend/frontend/UI) out of generated UML/XML models following principles of Model Driven Engineering.
- High-tech & data-driven agri-food system of the future (4TU project)
- Next generation educational chatbots
Recently completed research
- PROFEELEARN: Process-oriented assessment and feedback based on learning behavior/process data analytics grounded on the links between information/data analytics and learning sciences (Postdoctoral research funding)
- CITADEL H2020: CITADEL is a European H2020 Project involving twelve partners. These are research institutes, universities, public sector entities and IT companies from five different European countries. The project’s objective is to create an ecosystem of best practices for a transparent, innovative and cooperative public sector and to provides more efficient and inclusive tools to respond to citizen requirements. The CITADEL ecosystem combines and promotes a set of technologies (e.g. semantics, mobile, analytics, sentiment analysis, open linked data) to both empower Public Administrations (PAs) to improve their offering and the engagement of citizens, as well as to foster cooperation among PAs and users of public services in local, regional and national environments. Main contributions as a partner included: 1. an ecosystem architectural guidelines, 2. design and development of a semantic dashboard to support improving public services for e-government at EU public administration organizations.
- HOBBIT H2020: Holistic Benchmarking of Big Linked Data aims at abolishing the barriers in the adoption and deployment of Big Linked Data by European companies, by means of open benchmarking reports that allow them to assess the fitness of existing solutions for their purposes. These benchmarks are based on data that reflects reality and measures industry-relevant Key Performance Indicators (KPIs) with comparable results using standardized hardware. Main contributions as a partner included: 1. benchmark on query answering features for live time series in the form of multidimensional interfaces 2. establishing a taskforce subgroup for Benchmarking under the umbrella of Big Data Value Association with the aim to provide a scalable and FAIR benchmarking platform for data-driven solutions with a focus on AI (especially ML) solutions, corresponding benchmarks, key performance indicators, benchmarking tools and services for the independent, repeatable and scalable benchmarking of data-driven (especially AI) technologies, detecting potential use cases and categories of users as well as potential synergies with existing benchmarking organizations.