THRIVE - Techniques for Holistic, Responsible, and Interpretable Virtual Education (THRIVE)
Main funder
Funder's project number: 356314
Funds granted by main funder (€)
- 373 194,00
Funding program
Project timetable
Project start date: 01/09/2023
Project end date: 31/08/2027
Summary
Artificial intelligence and machine learning models in education have shown exceptional performance and promise more accessible and personalized education. However, actual applications of these models remain rare as the best-performing models are usually the least explainable. Opaque models not only contain the risk of algorithms or automated decision-making systems making decisions that unfairly disadvantage certain groups of students, but they also prevent educational stakeholders from understanding the decisions. Thus, explainability plays a pivotal role in ensuring the right features are used and detecting algorithmic discrimination. The THRIVE project aims to address the explainability issue by jointly considering (i) the representation and abstraction of data, (ii) the identification of “right” features with causality, (iii) the architecture of educational models, and (iv) model-usefulness established by the educational domain experts.
Principal Investigator
Primary responsible unit
Follow-up groups
- Human and Machine based Intelligence in Learning (Faculty of Information Technology IT) HUMBLE
- Learning and Cognitive Sciences (Faculty of Information Technology IT) LEACS
- School of Resource Wisdom (University of Jyväskylä JYU) JYU.Wisdom
- School of Wellbeing (University of Jyväskylä JYU) JYU.Well
- Teacher education research (teaching, learning, teacher, learning paths, education) (University of Jyväskylä JYU) JYU.Edu; Formerly JYU.Ope
Related publications and other outputs
- Adapting Teaching and Learning in Higher Education Using Explainable Student Agency Analytics (2024) Heilala, Ville; et al.; A3; OA; 979-8-3693-0232-3
- Understanding teachers’ perspectives on ethical concerns and skills to use AI tools (2024) Karimov, Ayaz; et al.; D3; OA
- Clustering to define interview participants for analyzing student feedback : a case of Legends of Learning (2023) Karimov, Ayaz; et al.; A4; OA; 978-1-7336736-4-8
- The impact of online educational platform on students’ motivation and grades : the case of Khan Academy in the under-resourced communities (2023) Karimov, Ayaz; et al.; A4; OA; 978-1-7336736-4-8