1. Development of a software stack to support language tutoring using a social robot and a touchscreen; the software consists of eight components which coordinate perception, planning, output generation, data logging and intra-process communication. The main focus of the software system is to support a large-scale scientific evaluation of the contribution social robots can make to second language tutoring.
2. Rigid evaluation of social signal processing techniques for Human-Robot Interaction, including speech recognition, voice activity detection, face detection and recognition. Various technologies have been evaluated and tested with an eye on their performance in the context of Human-Robot Interaction with young children.
3. Observations of second language learning and tutoring with the aim to inform the design of the robot tutor. Tutoring sessions between teachers and children have been recorded and annotated to get a view on how human tutors approach lecturing and tutoring of second language. Specific attention as paid to feedback, the use of L1 and L2 during tutoring, and scaffolding of the material.
4. A range of experiments and pilot studies which explore various parameters impacting on L2 learning created valuable insights into how to design a tutoring robot. For example, we studied how the robot’s behaviour impacts on the children’s learning performance (too friendly a robot has a negative impact, probably caused by children experiencing cognitive overload; but a more “verbally available” robot does not make a difference). We studied if children benefit from learning L2 spatial relationships with real objects, rather than with objects shown on a screen (no difference in performance was observed, probably because the spatial concepts are established already). Or we studied if personalised tutoring methods, using Bayesian Knowledge Tracing, made a difference.
5. A first pilot study has been completed, testing all technical components and the tutoring scripts, which provided valuable feedback in preparation for the large-scale study planned for 2018.
