Learn with surprize from a robot professor
Introduction
The use of social robots is spreading in many aspects of our lives from healthcare to the educational field and beyond. Education needs to prepare for this future by helping students use cutting edge technologies and learn how to interact with robots. Many researchers and teachers around the world have started using social robots in the classrooms in a variety of applications for different age groups. The four most common uses for robots in classrooms are as a) teachers, b) teacher's assistants, c) student's assistants, d) student's peers, or companions. Recent studies showed that social robots, especially from the position of a tutor or peer learner, can be effective at increasing cognitive and effective outcomes in students and have similar outcomes with those of human tutors on restricted tasks (Belpaeme et al., 2018). Social robots, such as the Kaspar humanoid robot, can also be used with the role of a student or pupil to which typical children and children with autism can improve their own understanding of the task and their social coordination with other children by teaching the robot how to replicate such behaviors (Zaraki et al., 2020).
A widely used social robot in education is the humanoid robot ‘NAO’ which has been programmed to provide games and activities in order to support fun learning in kindergarten (Alkhalifah et al., 2015) and teach a second language to school students (Meghdari et al., 2013). NAO has delivered positive effects in student's learning outcomes and engagement compared to a human teacher (Preau et al., 2019). Robots have also proved to be a beneficial tool for students to understand a broad range of engineering disciplines and have a significant advantage for University level students to develop Computational Thinking and learn about robotics and practice in control principles and programming (Keane et al., 2016).
In this study, we investigate the use of social robots in the university classroom comparing a) a human vs. a social robot tutor and b) the first-time exposure vs the repeated exposure of university students in a robot-tutor in a real classroom environment. Measuring students' learning outcomes and enjoyment levels. The subject of the course focused on basic engineering principles, and the students participating did not have engineering backgrounds. During the different classroom conditions and the repeated exposure of students in a robot-tutor, we examined the role of students’ previous experience and surprise in the learning process provided by a robot.
The key findings of the study are: a) when the participating students had a course with the robot-tutor for the first time, enjoyment levels increased but the level of knowledge gained was less compared to the students with a human-tutor. B) On the other hand, as familiarisation increased in the robot-tutor group experienced two robot-tutor lectures had a higher level of knowledge compared to the students with one lesson with a human-tutor and one lesson with a robot-tutor. And c) those who had a course with the robot-tutor multiple times had greater scores in the final exam in comparison with all the other students. Finally, based on the findings, the study supports the use of robots for teaching basic engineering principles to students without an engineering background at a university level.
Section snippets
Related work
Typically, research into robots in teaching positions focus on the student's learning outcome and attitudes towards robots (Belpaeme et al., 2018). However, there is a gap in research on how to apply educational theories such as the effect of surprise and the effect of familiarity in the learning process successfully in the robots as educators.
Several studies (Verner et al., 2016), (Polishuk & Verner, 2018) propose the use of the humanoid RoboThespian as a robot teacher for school students aged
Present study
Based on the evidence defined in the Introduction section, the present study investigates a university teaching procedure with a robot professor, measuring how this can affect student's learning outcomes and enjoyment of the course. Our goal is to teach basic engineering principles to first-year university students without engineering background in an educational department with the help of a humanoid robot professor. Teaching engineering principles has proven successful with the use of robots (
Participants
In the first experiment, 138 people participated, 7 Males and 131 Females, aged 18–28 years old. They were all freshmen, undergraduate students, in the School of Educational and Social Policies, on their first day in the mandatory course “Basic Principles of Information and Communication Technologies”. To avoid spoiling and/or developing hype from the robot lectures, we decided to experiment with the human tutor before the one with the robot. The experiments took place on two consecutive days,
Comparison of experiment I vs experiment II
The level of students’ enjoyment was higher in the Rob2 condition, where the students had a course with the robot-tutor for the second time, as indicated by the Bonferroni analysis, in comparison with Rob1 and both the human-tutor (p = .<001, d = 18.46, Std. Error = 3.67, Lower Bound = 8.68, Upper Bound = 28.25) and robot-tutor condition (p = .571, d = 4.71, Std. Error = 3.66, Lower Bound = −4.7, Upper Bound = 14.18) in Experiment I. Between Experiment I (where half of the students had a lesson
General discussion and Conclusion
The present study focused on the interaction between first-year university students without engineering backgrounds and a robot-tutor. We were interested mainly in how the robot-tutor can improve the students' knowledge about basic engineering subjects such as the internal and external computer systems as well as the level of enjoyment from the course's experience. The students had one or multiple courses held by a robot-tutor and one course with the robot-tutor and one with the human-tutor,
Credit author statement
Anna-Maria Velentza: Conceptualization, Methodology, Software, Validation, Formal analysis, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Nikolaos Fachantidis: Conceptualization, Methodology, Validation, Formal analysis, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Ioannis Lefkos: Software, Validation, Formal analysis, Formal analysis, Resources, Data curation, Writing –
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