Learning occurs throughout our entire life. We all learn about different things, at our own pace and method. In classical education, a class of learners is meant to learn a standard course in a pace consistent for the entire class. However, when a group of students are taught the same lessons at the same pace some students may lag in comprehension while some others excel. The concept of adaptive learning originates from the fact that every learner has a unique learning curve and personalized content aligned to her learning curve can improve learning outcomes. Recent technologies have made it possible to capture learner’s data, on the basis of which the system delivers different topics to the learner.
The Robots for Learning workshop, in its 5th series, focuses on adaptive learning. Adaptive learning systems are envisioned to maximize learning efficiency, improve learning outcomes, create personalised learning experience, generate analytics for early interventions, increase engagement, and allow for customizability to fit individual needs and preferences. In this workshop, we aim to discuss the approaches and challenges of using robots for learner-centric methods, learning path adaptation, social personalisation, remediation and multimodal techniques. Robots can be able to perceive and monitor learning, and at the same time empathize and give personalized feedback to the learners.
With this workshop, we aim at discussing recent advances in empirical and theoretical state-of-the-art research contributions on HRI in educational contexts regarding the following challenges: how adaptivity can play a role in the educational context? How can we use machine learning to provide better learning? How could robots be used to foster adaptive learning for a group of users?
Topics include the following
- Adaptive mechanisms for robot tutors, personalization and adaptation algorithms for tutoring interactions
- Design of autonomous systems for tutoring interactions
- Theories and methods for tutoring (pedagogical and language acquisition)
- Shared knowledge and knowledge modelling in HRI
- Human-robot collaborative learning
- Attachment and learning with a social robot (social and cognitive development)
- Engagement in educational human-robot interaction
- Human-robot relationship assessment
- Designing student models and assessing student’s learning
- Playful learning with a robot
- Human-robot creativity
- Kinesthetic and non-verbal communication in human-robot interaction
- Impact of embodiment on learning
- Technical innovation in learning or teaching robots
- Long term learning interactions, design and methodologies for repeated human-robot encounters
- Robots for learners with special needs and special abilities
- Education and re-training for adults
- Rehabilitation and re-education
- Privacy and ethical issues in robot tutoring applications
We propose to organize a half-day workshop that will include:
- Lightning talks: authors of accepted papers will provide short introduction of their posters.
- Keynotes: invited senior researchers’ will share their perspectives and experiences on the field of technologies for education.
- Structured group discussions: workshop attendees will engage in discussions on principal research questions or debates in the robots for education.
Format and Submissions
We invite contributions spanning the areas of education and robotics. We explicitly encourage the submission of papers describing work in progress, or containing preliminary results to discuss with the community. Submission papers should not exceed 4 pages (references excluded) .
Template to be used: https://www.overleaf.com/gallery/tagged/arxiv
The maximum file size is 2 MB. Submissions should be in PDF format through Easy Chair: https://easychair.org/conferences/?conf=r4lhri2019
Wafa Johal, École Polytechnique Fédérale Lausanne, Switzerland, wafa.johal (at) epfl.ch.
Anara Sandygulova, Nazarbaeyv University, Astana, Kazakhstan, anara.sandygulova (at) nu.edu.kz.
Jan de Wit, Tilburg University, the Netherlands, j.m.s.dewit (at) uvt.nl.
Mirjam de Haas, Tilburg University, the Netherlands, mirjam.dehaas (at) uvt.nl.
Brian Scassellati, Yale University, CT, USA.