Daniel Davison has a Bachelor degree in Computer Science and a Master degree in Human-Media Interaction from the University of Twente. He is currently a PhD Candidate at the Human-Media interaction group at the University of Twente. His research focuses on educational social robots for primary school children. He is particularly interested in designing instrumented learning materials, expressive robot behaviours and rich interactive dialogues to optimally support a child's inquiry learning process.
As part of the EU FP7 projects EASEL and SQUIRREL, we developed an instrument for measuring young children's subjective perceptions of robots' social competence. Using an iterative approach, in two consecutive studies, we designed and validated the instrument on three different robotic platforms in three different interaction contexts. The instrument consists of two main components: 1) a semi-structured interview, and 2) a pictorial selection task. This measurement instrument was first presented during ICSR2017, Tsukuba, Japan.
In the semi-structured interview we ask questions that have the purpose of eliciting explanations from the children about their subjective experience with, and their views about, the interaction with a robot. An overview of the questions is included in the original publication, and in the poster+handout document. Transcriptions of the interviews are analysed according to the following themes:
Theme A - Perceptions of the interaction with the robot
Theme B - Perceptions of the social capabilities of the robot
Within Theme A, we distinguish between explanations that fall in the following five categories: 1) task-related descriptions; 2) technology/design of the robot; 3) role of the robot; 4) aspects of the interaction with the robot; and 5) theory of mind of the robot. In Theme B, we distinguish the following three categories of descriptions of the robot's social capabilities: 1) machine-like; 2) the robot as social artefact; and 3) the robot as social agent.
In the pictorial selection task children select pictures that, in their opinion, "match" or "don’t match" the robot. They are then asked to elaborate on their choice, and explain in more detail. This enables them to use properties of familiar agents and objects to describe features of the robot. On the one hand, the pictures represent various social agents from a child's life, such as friends, teachers, pets and stuffed animals. On the other hand, they represent various less-social (technological) tools, such as a laptop, car and notebook. The pictures used for this task are shown on the back of this handout. The pictures are presented in random order, and examples can be found in the poster+handout document.
A child is first asked to select the picture that best matches with the robot, and elaborate/explain their choice. Their first selection will often show their most obvious associations they have with the robot. They are then asked to choose a second picture that best matches the robot. This second choice often reveals more varied underlying associations. This same process is repeated for pictures that, in their opinion, don't match with the robot.
Their choices and explanations are analysed with respect to the characteristics they use in their descriptions, according to the following categories: 1) references to social properties; 2) technology/design; 3) tool/function; or 4) educational properties.