Supporting Adaptive and Personalized Learning

Students are diverse and their learning depends on their backgrounds, prior knowledge and individual learning pace. It is therefore very difficult for teachers to address this diversity effectively in a class.

Digital learning tools offer the opportunity for students to learn at their own pace. The data that is logged as students work with digital learning tools, such as learning management systems, MOOCs, simulations, robotics systems, AR/VR/MR systems and other learning software, can be used in order to assess the current state of their learning, and then adapt and personalize the content to the learner. This requires applying machine learning algorithms to the collected data in order to assess learners’ competencies and then presenting them with the content and feedback.

The goal of this project is to build adaptation and personalization modules to improve the effectiveness of digital learning tools for different types of learners.