IGNITE

Project Description

Significant individual differences in students’ knowledge and skills pose a challenge when it comes to meeting course requirements and achieving program-wide learning objectives. Offering courses that fully cover all gaps in knowledge is hardly feasible due to limited teaching capacity and credit restrictions, while excessive repetition of content students already know reduces the curriculum’s appeal. This problem is particularly relevant during the transition from various national and international bachelor’s programs to a master’s program.

The project addresses this challenge through personalized instruction within modularized courses, in which individual knowledge gaps are closed by tailoring a combination of module elements toeach student’s needs. Knowledge graphs model both students’ competencies and the dependencies between module elements, thereby enabling tools for generating optimized, individualized learning and course paths. The project will focus on various modular courses and conduct an experimental evaluation with students in computer science and digital health sciences.

Key Areas of the Project

• Personalized Learning: Generating individualized learning and course paths based on students’ prior knowledge and learning objectives.
• Adaptive Curriculum: Supporting instructors in adapting course content and reducing redundancies.
• Knowledge graphs: Use of semantic representations to model competencies and content for coherent learning paths.