Abstract
Learning in higher education scenarios requires self-directed learning and the challenging task of self-motivation while individual support is rare. The integration of social robots to support learners has already shown promise to benefit the learning process in this area. In this paper, we focus on the applicability of an adaptive robotic tutor in a university setting. To this end, we conducted a long-term field study implementing an adaptive robotic tutor to support students with exam preparation over three sessions during one semester. In a mixed design, we compared the effect of an adaptive tutor to a control condition across all learning sessions. With the aim to benefit not only motivation but also academic success and the learning experience in general, we draw from research in adaptive tutoring, social robots in education, as well as our own prior work in this field. Our results show that opting in for the robotic tutoring is beneficial for students. We found significant subjective knowledge gain and increases in intrinsic motivation regarding the content of the course in general. Finally, participation resulted in a significantly better exam grade compared to students not participating. However, the extended adaptivity of the robotic tutor in the experimental condition did not seem to enhance learning, as we found no significant differences compared to a non-adaptive version of the robot.
Highlights
Qualitative interviews showed that participants praised the offer and the students showed high interest for the robotic tutoring. This benefit was supported by quantitative data, as participants who took part in the robotic tutoring performed significantly better in the exam compared to students who did not participate
We found no significant effect of condition, but a significant effect of session
We presented a field study with three learning sessions with our robotic tutor in order to further investigate the general applicability of social robots as tutors in higher education, and in particular, to assess if adaptivity of the tutor can benefit learning compared to a non-adaptive version of the robot
Summary
Self-directed and lifelong learning is the basis of today’s knowledge society. This is challenging for many learners, as it requires a high degree of motivation and attention. The requirements for a proactive and self-directed learning style are high, especially at universities. For students in their first semesters, self-study is usually a big challenge. Effective learning strategies and self-motivation are required, but often have to be learned and refined over time. This challenge was even more prominent during the last year due to the COVID-19 pandemic, and an accompanying shift to online teaching, which further reduced individual support and feedback from teachers
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