In our previous research, we showed that students using the educational robot Thymio and its visual programming environment were able to learn the important computer-science concept of event-handling. This paper extends that work by integrating augmented reality ( ar) into the activities. Students used a tablet that displays in real time the event executed on the robot. The event is overlaid on the tablet over the image from a camera, which shows the location of the robot when the event was executed. In addition, visual feedback (fb) was implemented in the software. We developed a novel video questionnaire to investigate the performance of the students on robotics tasks. Data were collected comparing four groups: ar+fb, ar+non-fb, non-ar+fb, non-ar+non-fb. The results showed that students receiving feedback made significantly fewer errors on the tasks. Those using ar made fewer errors, but this improvement was not significant, although their performance improved. Technical problems with the ar hardware and software showed where improvements are needed.

The strong interest children show for mobile robots makes these devices potentially powerful to teach programming. Moreover, the tangibility of physical objects and the sociability of interacting with them are added benefits. A key skill that novices in programming have to acquire is the ability to mentally trace program execution. However, because of their embodied and real-time nature, robots make the mental tracing of program execution difficult.To address this difficulty, in this paper we propose an automatic program evaluation framework based on a robot simulator. We describe a real-time implementation providing feedback and gamified hints to students.In a user study, we demonstrate that our hint system increases the percentage of students writing correct programs from 50 % to 96 %, and decreases the average time to write a correct program by 30 %. However, we could not show any correlation between the use of the system and the performance of students on a questionnaire testing concept acquisition. This suggests that programming skills and concept understanding are different abilities.Overall, the clear performance gain shows the value of our approach for programming education using robots.

Speech-to-speech translation combines machine translation with speech synthesis, introducing evaluation challenges not present in either task alone. How to automatically evaluate speech-to-speech translation is an open question which has not previously been explored. Translating to speech rather than to text is often motivated by unwritten languages or languages without standardized orthographies. However, we show that the previously used automatic metric for this task is best equipped for standardized high-resource languages only. In this work, we first evaluate current metrics for speechto-speech translation, and second assess how translation to dialectal variants rather than to standardized languages impacts various evaluation methods.

Speech-to-speech translation combines machine translation with speech synthesis, introducing evaluation challenges not present in either task alone. How to automatically evaluate speech-to-speech translation is an open question which has not previously been explored. Translating to speech rather than to text is often motivated by unwritten languages or languages without standardized orthographies. However, we show that the previously used automatic metric for this task is best equipped for standardized high-resource languages only. In this work, we first evaluate current metrics for speechto-speech translation, and second assess how translation to dialectal variants rather than to standardized languages impacts various evaluation methods.